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40%","2026-10-15T04:15:18.572Z",{"id":781,"type":304,"title":379,"body":782,"tool_name":381,"z2u_price":726,"z2u_url":382,"status":310,"read_at":48,"created_at":783},1261,"Platforma: Dealify | Kategoria: Creative | Cena: €66.95 (regular: €225.95) | Rabat: 70%","2026-10-15T04:15:18.431Z",{"id":785,"type":304,"title":386,"body":786,"tool_name":388,"z2u_price":726,"z2u_url":389,"status":310,"read_at":48,"created_at":787},1260,"Platforma: Dealify | Kategoria: Creative | Cena: €66.95 (regular: €257.95) | Rabat: 74%","2026-10-15T04:15:18.289Z",{"id":789,"type":304,"title":401,"body":790,"tool_name":403,"z2u_price":726,"z2u_url":404,"status":310,"read_at":48,"created_at":791},1259,"Platforma: Dealify | Kategoria: Productivity | Cena: €66.95 (regular: €669.95) | Rabat: 90%","2026-10-15T04:15:16.690Z",{"id":793,"type":304,"title":408,"body":790,"tool_name":409,"z2u_price":726,"z2u_url":410,"status":310,"read_at":48,"created_at":794},1258,"2026-10-15T04:15:16.547Z",{"id":796,"type":304,"title":797,"body":798,"tool_name":799,"z2u_price":765,"z2u_url":800,"status":310,"read_at":48,"created_at":801},1257,"💰 StickyPassword — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €32.95 (regular: €218.95) | Rabat: 85%","StickyPassword","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fstickypassword","2026-10-15T04:15:15.904Z",{"id":803,"type":304,"title":804,"body":805,"tool_name":806,"z2u_price":765,"z2u_url":807,"status":310,"read_at":48,"created_at":808},1256,"💰 Notepad — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €32.95 (regular: €329.95) | Rabat: 90%","Notepad","https:\u002F\u002Fdealify.com\u002Fproducts\u002Fnotepad","2026-10-15T04:15:15.108Z",{"id":810,"type":304,"title":811,"body":812,"tool_name":813,"z2u_price":814,"z2u_url":815,"status":310,"read_at":48,"created_at":816},1255,"💰 FlashBooks — spadek ceny","Platforma: Dealify | Kategoria: Productivity | Cena: €282.95 (regular: €1973.95) | Rabat: 86%","FlashBooks",282.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fflashbooks","2026-10-15T04:15:14.664Z",{"id":818,"type":304,"title":819,"body":820,"tool_name":821,"z2u_price":822,"z2u_url":823,"status":310,"read_at":48,"created_at":824},1254,"💰 Envoice — spadek ceny","Platforma: Dealify | Kategoria: Business | Cena: €157.95 (regular: €1576.95) | Rabat: 90%","Envoice",157.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fenvoice","2026-10-15T04:15:12.373Z",{"id":826,"type":304,"title":452,"body":827,"tool_name":454,"z2u_price":726,"z2u_url":455,"status":310,"read_at":48,"created_at":828},1253,"Platforma: Dealify | Kategoria: Business | Cena: €66.95 (regular: €669.95) | Rabat: 90%","2026-10-15T04:15:12.225Z",{"id":830,"type":304,"title":459,"body":831,"tool_name":461,"z2u_price":609,"z2u_url":463,"status":310,"read_at":48,"created_at":832},1252,"Platforma: Dealify | Kategoria: Business | Cena: €336.95 (regular: €3369.95) | Rabat: 90%","2026-10-15T04:15:11.677Z",{"id":834,"type":304,"title":835,"body":836,"tool_name":837,"z2u_price":838,"z2u_url":839,"status":310,"read_at":48,"created_at":840},1251,"💰 LeadLocator — spadek ceny","Platforma: Dealify | Kategoria: Business | Cena: €191.95 (regular: €1917.95) | Rabat: 90%","LeadLocator",191.95,"https:\u002F\u002Fdealify.com\u002Fproducts\u002Fleadlocator","2026-10-15T04:15:11.332Z",{"id":842,"type":304,"title":482,"body":843,"tool_name":484,"z2u_price":726,"z2u_url":485,"status":310,"read_at":48,"created_at":844},1250,"Platforma: Dealify | Kategoria: Social Media | Cena: €66.95 (regular: €669.95) | Rabat: 90%","2026-10-15T04:15:09.802Z",{"id":846,"type":304,"title":489,"body":843,"tool_name":490,"z2u_price":726,"z2u_url":491,"status":310,"read_at":48,"created_at":847},1249,"2026-10-15T04:15:09.556Z",{"data":849,"_meta":1365,"alternatives":1366,"deals":1367},{"source":850,"source_key":851,"source_page_url":852,"name":853,"slug":854,"description":855,"title":856,"category":857,"access_model":858,"pricing_evidence":859,"capability_evidence":863,"headings":875,"breadcrumbs":880,"pros":881,"cons":892,"external_links":896,"affiliate_links":957,"official_link_candidates":958,"text_excerpt":969,"fetched_at":970,"final_url":852,"http_status":971,"content_type":972,"content_hash":973,"cache_file":974,"published":975,"verification_status":976,"official_url":913,"official_evidence":977,"api_available":1233,"blog_titles":1234,"data_region":1235,"data_training":1235,"entity_type":1236,"external_rating":1237,"features":1238,"free_summary":1256,"gdpr":1235,"integrations":1257,"languages":1268,"license":1273,"limitations":1274,"model_names":1282,"polish_support":1285,"pricing_plans":1286,"pricing_summary":1318,"privacy_summary":1235,"release_date":1319,"research_links":1320,"research_summary":1321,"security_summary":1235,"self_hosted":1233,"short_description":1322,"starting_price":1289,"supported_platforms":1323,"target_users":1330,"trial_summary":1339,"use_cases":1340,"vendor":1352,"status":1353,"last_verified_at":1354,"alternatives":1355,"quality":1360,"category_v3":265,"category_v3_reason":1362,"deep_card":1363,"deep_card_generated":975,"category_v3_main":259,"category_v3_label":266,"category_v3_main_label":260,"_category_fixed":975,"evidence":1364},"AIxploria","aixploria","https:\u002F\u002Fwww.aixploria.com\u002Fglm-5-2\u002F","GLM-5.2","glm-5-2","Z.ai GLM-5.2 est un modèle LLM open source de grande envergure avec des capacités avancées en raisonnement, génération de texte et compréhension contextuelle.","GLM-5.2 : Avis, Prix, Info & 46 IA Alternatives | 2026 | AIxploria","agents","open_source",[860,861,862],"Accès Ce que vous y faites Facturation chat.z.ai Questions, essais, petits sites Sans frais GLM Coding Plan Agents de code au forfait mensuel Autour de 18 $\u002Fmois en entrée de gamme API Z.ai Applications et agents en production 1,40 $ en entrée \u002F 4,40 $ en sortie par million de tokens, 0,26 $ en cache Poids ouverts Hébergement sur vos propres GPU Licence MIT, matériel à votre charge Essayer GLM-5.2 gratuitement Questions fréquentes","Freemium Claude Sonnet 5","97 Visiter Freemium GPT‑5.5 Instant",[864,865,866,867,868,869,860,870,871,872,873,874],"« Un modèle open source conçu pour le code et les tâches longues, avec une fenêtre de contexte allant jusqu’à 1 million de tokens. Ce LLM à experts mixtes (MoE) gère des projets complexes, analyse des référentiels entiers et effectue des raisonnements en plusieurs étapes. Il est disponible en open source sous licence MIT","API Doc","GLM-5.2 est le grand modèle de langage de Z.ai conçu pour les tâches au long cours: il maintient un contexte d' un million de tokens qui reste exploitable du premier au dernier échange. Ses poids se téléchargent librement sous licence MIT, une rareté à ce niveau. Le chat de z.ai se teste sans payer, tandis que l'API se facture au token, loin des tarifs des grands modèles fermés. Les équipes de développement qui font tourner des agents de code pendant des heures sont le public visé.","Avantages Contexte d'un million de tokens réellement stable Poids ouverts publiés sous licence MIT Niveaux d'effort de réflexion réglables Servi par une vingtaine d'hébergeurs au choix Inconvénients Entrée et sortie limitées au texte Poids trop lourds pour un GPU personnel Quotas du Coding Plan plus serrés aux heures de pointe Un million de tokens qui ne s'effritent pas en route","Z.ai a entraîné GLM-5.2 pendant des mois sur des scénarios d'agent de codage. Vous lui confiez un dépôt complet: il retient les frontières entre modules, les contrats d'API et les décisions déjà prises, puis les applique des heures plus tard au lieu de les réinventer. Une seule tâche peut couvrir le cycle entier, jusqu'au produit déployable sur plusieurs plateformes.","Refonte de code à l'échelle d'un dépôt entier Recherche automatisée et bancs d'essai Débogage qui traverse des dizaines de fichiers Mini-jeux et prototypes complets en une consigne Chat sans frais, Coding Plan ou API: comment essayer GLM-5.2","GLM-5.2 est-il gratuit ? Oui, en partie. Le chat de z.ai permet de l'essayer sans payer, et ses poids se téléchargent librement sous licence MIT. L'usage intensif passe en revanche par l'API facturée au token ou par le Coding Plan mensuel, un abonnement réservé aux outils de codage compatibles.","Peut-on intégrer GLM-5.2 dans un produit commercial ? La licence MIT autorise l'usage commercial comme non commercial, sans redevance. Vous pouvez embarquer le modèle dans un produit payant, le modifier ou l'héberger vous-même. Peu de modèles de ce calibre laissent une liberté aussi complète, et c'est un vrai argument face aux API fermées.","Peut-on faire tourner GLM-5.2 en local ? Les poids sont disponibles sur Hugging Face et via Ollama, mais leurs 753 milliards de paramètres réclament une grosse infrastructure GPU. En pratique, la plupart des équipes passent par un hébergeur ou l'API officielle et réservent l'auto-hébergement aux volumes très réguliers.","« Créez rapidement des miniatures accrocheuses pour vos vidéos sur YouTube, Twitch et Facebook. Attirez plus de vues et améliorez facilement le taux de clics sur vos clip vidéos »","« Exploitez une IA puissante optimisée pour le code, la finance et les tâches autonomes. Ce nouveau modèle LLM de chez SpaceXAI est conçu pour être rapide, économique et capable de gérer des flux de travail compliqués de bout en bout »",[876,877,878,879],"Un million de tokens qui ne s'effritent pas en route","GLM-5.2 au travail, du cahier des charges au déploiement","Chat sans frais, Coding Plan ou API: comment essayer GLM-5.2","Questions fréquentes",[],[882,883,884,885,886,887,888,889,890,891],"Rzeczywiście stabilne 1M-token context","Otwarta licencja MIT","Regulowane poziomy effort w rozumowaniu","Wsparcie dla długich zadań agentowych i kodowania","Możliwość lokalnego wdrożenia","One million token context window that remains stable","Open weights under MIT license","Adjustable reasoning effort levels","Available through 20+ hosting providers","Designed for long-running agent tasks and code projects",[893,894,895],"Modele jest bardzo ciężki (753B parametrów), więc pełny self-hosting wymaga dużej infrastruktury GPU","Wydajność w benchmarkach agentowych nie zawsze dorównuje najmocniejszym modelom zamkniętym","W praktyce codzienne użycie w Coding Plan podlega limitom kwotowym i ograniczeniom godzinowym",[897,900,902,904,906,909,912,915,918,920,923,926,928,930,933,935,936,939,940,941,944,946,947,949,951,953,956],{"url":898,"text":899},"https:\u002F\u002Ftwitter.com\u002Fintent\u002Ftweet?url=https%3A%2F%2Fwww.aixploria.com%2Fglm-5-2%2F&text=GLM-5.2%3A+Un+mod%C3%A8le+open+source+con%C3%A7u+pour+le+code+et+les+t%C3%A2ches+longues%2C+avec+une+fen%C3%AAtre+de+contexte+allant+jusqu%E2%80%99%C3%A0+1+million+de+tokens.+Ce+LLM+%C3%A0+experts+mixtes+%28MoE%29+g%C3%A8re+des+projets+complexes%2C+analyse+des+r%C3%A9f%C3%A9rentiels+entiers+et+effectue+des+raisonnements+en+plusieurs+%C3%A9tapes.+Il+est+disponible+en+open+source+sous+licence+MIT","",{"url":901,"text":899},"https:\u002F\u002Fwww.facebook.com\u002Fsharer\u002Fsharer.php?u=https%3A%2F%2Fwww.aixploria.com%2Fglm-5-2%2F",{"url":903,"text":899},"https:\u002F\u002Fwww.linkedin.com\u002FshareArticle?url=https%3A%2F%2Fwww.aixploria.com%2Fglm-5-2%2F&title=GLM-5.2",{"url":905,"text":899},"https:\u002F\u002Ft.me\u002Fshare\u002Furl?url=https%3A%2F%2Fwww.aixploria.com%2Fglm-5-2%2F&text=Un+mod%C3%A8le+open+source+con%C3%A7u+pour+le+code+et+les+t%C3%A2ches+longues%2C+avec+une+fen%C3%AAtre+de+contexte+allant+jusqu%E2%80%99%C3%A0+1+million+de+tokens.+Ce+LLM+%C3%A0+experts+mixtes+%28MoE%29+g%C3%A8re+des+projets+complexes%2C+analyse+des+r%C3%A9f%C3%A9rentiels+entiers+et+effectue+des+raisonnements+en+plusieurs+%C3%A9tapes.+Il+est+disponible+en+open+source+sous+licence+MIT",{"url":907,"text":908},"https:\u002F\u002Fchatgpt.com\u002F?q=Donne-moi%20une%20pr%C3%A9sentation%20claire%20et%20concise%20de%20GLM-5.2.%20Explique%20%C3%A0%20quoi%20sert%20cet%20outil%20IA%2C%20%C3%A0%20qui%20il%20s%E2%80%99adresse%2C%20ses%20principales%20fonctionnalit%C3%A9s%2C%20son%20mod%C3%A8le%20tarifaire%2C%20ses%20points%20forts%2C%20ses%20limites%20et%20ses%20alternatives%20possibles.","ChatGPT",{"url":910,"text":911},"https:\u002F\u002Fwww.perplexity.ai\u002F?q=Recherche%20des%20informations%20%C3%A0%20jour%20sur%20GLM-5.2.%20R%C3%A9sume%20%C3%A0%20quoi%20sert%20cet%20outil%2C%20ses%20prix%20actuels%20si%20disponibles%2C%20ses%20principales%20fonctionnalit%C3%A9s%2C%20ses%20points%20forts%2C%20ses%20limites%20et%20ses%20alternatives%20notables.","Perplexity",{"url":913,"text":914},"https:\u002F\u002Fhuggingface.co\u002Fzai-org\u002FGLM-5.2","Hugging Face",{"url":916,"text":917},"https:\u002F\u002Fgithub.com\u002Fzai-org\u002FGLM-5","GitHub",{"url":919,"text":865},"http:\u002F\u002Fdocs.z.ai\u002Fguides\u002Fllm\u002Fglm-5.2",{"url":921,"text":922},"https:\u002F\u002Ffr.wikipedia.org\u002Fwiki\u002FLicence_MIT","licence MIT",{"url":924,"text":925},"https:\u002F\u002Fget.emergent.sh\u002Fe8utricukk6y","Emergent AI",{"url":924,"text":927},"« Transformez vos idées en applications complètes grâce à des agents IA qui codent, testent et déploient pour vous. Créez sites web (full-stack), apps IA, jeux et outils automatisé",{"url":924,"text":929},"Visiter",{"url":931,"text":932},"https:\u002F\u002Ftry.web.clickup.com\u002Fgslcwsp7r8e2","ClickUp",{"url":931,"text":934},"« Gérez vos projets plus vite avec une plateforme qui regroupe équipes, tâches et documents en un seul espace. Son IA intégrée résume vos notes, rédige pour vous et organise automa",{"url":931,"text":929},{"url":937,"text":938},"https:\u002F\u002Fwww.thumbnailcreator.com\u002F","ThumbnailCreator.com",{"url":937,"text":873},{"url":937,"text":929},{"url":942,"text":943},"https:\u002F\u002Fvmake.ai\u002Fvideo-enhancer","Vmake Video Enhancer",{"url":942,"text":945},"« Améliorez vos vidéos floues et de mauvaise qualité pour les transformer en HD et 4K grâce à l'IA. Vmake accentue les détails de vos vidéos, améliore leur netteté et leur donne un",{"url":942,"text":929},{"url":948,"text":899},"https:\u002F\u002Ftwitter.com\u002FAixploria",{"url":950,"text":899},"https:\u002F\u002Fwww.youtube.com\u002F@aixploria-off?sub_confirmation=1",{"url":952,"text":899},"https:\u002F\u002Fwww.tiktok.com\u002F@aixploria",{"url":954,"text":955},"https:\u002F\u002Fcookiedatabase.org\u002Ftcf\u002Fpurposes\u002F","En savoir plus sur ces finalités",{"url":954,"text":955},[],[959,960,961,962,963,964,965,966,967,968],{"url":905,"text":899},{"url":907,"text":908},{"url":910,"text":911},{"url":913,"text":914},{"url":916,"text":917},{"url":919,"text":865},{"url":921,"text":922},{"url":924,"text":925},{"url":924,"text":927},{"url":924,"text":929},"GLM-5.2 : Avis, Prix, Info & 46 IA Alternatives | 2026 | AIxploria \nTop 10 Catégories IA Tutoriels IA + Plus Nouveaux Outils IA Outils IA Gratuits Actualités IA 2026 Bonus & Extras Soumettre une IA \nEnglish Français Español Deutsch Italiano Português + More × 中文 日本語 العربية हिन्दी Русский 한국어 Türkçe Tiếng Việt فارسی বাংলা Indonesia ไทย Nederlands Polski اردو Kiswahili Українська Ελληνικά עברית Svenska Norsk Dansk Suomi Čeština Magyar Melayu Tagalog 中文 (香港) தமிழ் \nS'inscrire \nTop 10 Catégories IA Tutoriels IA + Plus Nouveaux Outils IA Outils IA Gratuits Actualités IA 2026 Bonus & Extras Soumettre une IA \nS'inscrire \nAccueil IA Récentes GLM-5.2 \nGLM-5.2 \n#18 dans Modèles LLM \n4.5\u002F5 \nVisiter ce site \n« Un modèle open source conçu pour le code et les tâches longues, avec une fenêtre de contexte allant jusqu’à 1 million de tokens. Ce LLM à experts mixtes (MoE) gère des projets complexes, analyse des référentiels entiers et effectue des raisonnements en plusieurs étapes. Il est disponible en open source sous licence MIT\n»\nEn savoir plus : \nChatGPT \nPerplexity \nOutil de la Semaine \n15 – 21 juin 2026 \nGratuit \n151466 \nLiens utiles \nSite Officiel \nHugging Face \nGitHub \nAPI Doc \n#IA Récentes \n#Modèles LLM \n60 GLM-5.2 fait tenir tout un projet logiciel dans son million de tokens de contexte\nGLM-5.2 est le grand modèle de langage de Z.ai conçu pour les tâches au long cours: il maintient un contexte d' un million de tokens qui reste exploitable du premier au dernier échange. Ses poids se téléchargent librement sous licence MIT, une rareté à ce niveau. Le chat de z.ai se teste sans payer, tandis que l'API se facture au token, loin des tarifs des grands modèles fermés. Les équipes de développement qui font tourner des agents de code pendant des heures sont le public visé.\nAvantages Contexte d'un million de tokens réellement stable Poids ouverts publiés sous licence MIT Niveaux d'effort de réflexion réglables Servi par une vingtaine d'hébergeurs au choix Inconvénients Entrée et sortie limitées au texte Poids trop lourds pour un GPU personnel Quotas du Coding Plan plus serrés aux heures de pointe Un million de tokens qui ne s'effritent pas en route\nLe contexte d'un million de tokens de GLM-5.2 reste utilisable sur toute sa longueur, là où beaucoup de modèles acceptent des tokens supplémentaires sans vraiment s'en servir. Son architecture IndexShare partage un même indexeur toutes les quatre couches d'attention creuse et divise le calcul par token par 2,9 à pleine longueur.\nSous cette mécanique vit un modèle de 753 milliards de paramètres, successeur direct de GLM-5.1, avec plusieurs niveaux d'effort de réflexion pour arbitrer entre qualité et latence. À son lancement, il terminait à un point de Claude Opus 4.8 sur le benchmark FrontierSWE tout en devançant GPT-5.5, le genre d'écart qui se vérifie dans les classements de modèles de langage .\nTest complet de GLM-5.2 en français, avis et accès gratuit GLM-5.2 confronté à Claude Code sur de vrais projets GLM-5.2 au travail, du cahier des charges au déploiement\nZ.ai a entraîné GLM-5.2 pendant des mois sur des scénarios d'agent de codage. Vous lui confiez un dépôt complet: il retient les frontières entre modules, les contrats d'API et les décisions déjà prises, puis les applique des heures plus tard au lieu de les réinventer. Une seule tâche peut couvrir le cycle entier, jusqu'au produit déployable sur plusieurs plateformes.\nDans la pratique, quatre terrains lui vont particulièrement bien:\nRefonte de code à l'échelle d'un dépôt entier Recherche automatisée et bancs d'essai Débogage qui traverse des dizaines de fichiers Mini-jeux et prototypes complets en une consigne Chat sans frais, Coding Plan ou API: comment essayer GLM-5.2\nGLM-5.2 se découvre gratuitement sur chat.z.ai (aucune carte bancaire demandée, ce qui simplifie le premier essai). Pour un usage soutenu, trois circuits de facturation cohabitent, résumés ci-dessous.\nLes poids complets sont publiés sur Hugging Face sous licence MIT , et une vingtaine d'hébergeurs tiers servent le modèle, souvent sous les tarifs officiels. Ces chiffres bougent vite; seule la page de Z.ai fait foi au moment où vous lisez.\nAccès Ce que vous y faites Facturation chat.z.ai Questions, essais, petits sites Sans frais GLM Coding Plan Agents de code au forfait mensuel Autour de 18 $\u002Fmois en entrée de gamme API Z.ai Applications et agents en production 1,40 $ en entrée \u002F 4,40 $ en sortie par million de tokens, 0,26 $ en cache Poids ouverts Hébergement sur vos propres GPU Licence MIT, matériel à votre charge Essayer GLM-5.2 gratuitement Questions fréquentes\nGLM-5.2 est-il gratuit ? Oui, en partie. Le chat de z.ai permet de l'essayer sans payer, et ses poids se téléchargent librement sous licence MIT. L'usage intensif passe en revanche par l'API facturée au token ou par le Coding Plan mensuel, un abonnement réservé aux outils de codage compatibles.\nPeut-on intégrer GLM-5.2 dans un produit commercial ? La licence MIT autorise l'usage commercial comme non commercial, sans redevance. Vous pouvez embarquer le modèle dans un produit payant, le modifier ou l'héberger vous-même. Peu de modèles de ce calibre laissent une liberté aussi complète, et c'est un vrai argument face aux API fermées.\nGLM-5.2 rivalise-t-il avec Claude Opus 4.8 ? Sur le benchmark FrontierSWE, GLM-5.2 termine à un point derrière Claude Opus 4.8 et un point devant GPT-5.5, pour un coût par token nettement plus bas. Pour du codage agentique à gros volume, le rapport qualité-prix penche donc clairement de son côté.\nPeut-on faire tourner GLM-5.2 en local ? Les poids sont disponibles sur Hugging Face et via Ollama, mais leurs 753 milliards de paramètres réclament une grosse infrastructure GPU. En pratique, la plupart des équipes passent par un hébergeur ou l'API officielle et réservent l'auto-hébergement aux volumes très réguliers.\nVerdict : Refondre un dépôt entier sans saucissonner la mission en dix morceaux: voilà le terrain où GLM-5.2 excelle, et les équipes qui vivent dans un agent de code y gagneront un moteur d'endurance pour une fraction du budget des modèles fermés.\n★ Outils IA de Prestige ★ \n★ Mis en avant \nEmergent AI \n« Transformez vos idées en applications complètes grâce à des agents IA qui codent, testent et déploient pour vous. Créez sites web (full-stack), apps IA, jeux et outils automatisés simplement en décrivant votre projet »\nVisiter \n★ Mis en avant \nClickUp \n« Gérez vos projets plus vite avec une plateforme qui regroupe équipes, tâches et documents en un seul espace. Son IA intégrée résume vos notes, rédige pour vous et organise automatiquement vos priorités »\nVisiter \n★ Mis en avant \nThumbnailCreator.com \n« Créez rapidement des miniatures accrocheuses pour vos vidéos sur YouTube, Twitch et Facebook. Attirez plus de vues et améliorez facilement le taux de clics sur vos clip vidéos »\nVisiter \n★ Mis en avant \nVmake Video Enhancer \n« Améliorez vos vidéos floues et de mauvaise qualité pour les transformer en HD et 4K grâce à l'IA. Vmake accentue les détails de vos vidéos, améliore leur netteté et leur donne un aspect plus professionnel en quelques secondes »\nVisiter \nIA Alternatives à \nGLM-5.2 \nFreemium Claude Sonnet 5 \n4.5\u002F5 \n« Un modèle LLM pensé pour agir comme un vrai collaborateur technique. Il planifie, utilise le navigateur et le terminal, gère du code sur de longs contextes et atteint des scores de niveau Opus. Il reste également moins cher et plus adapté aux agents autonomes»\n104 Visiter Payant Gemini 3.1 Pro \n4.7\u002F5 \n« Un modèle IA de Google capable de gérer des tâches complexes avec du texte, des images, de l'audio ou de la vidéo. Il peut résumer de longues sources, analyser des données et aider à coder des interfaces complètes»\n101 Visiter Payant GPT-5.4 \n4.4\u002F5 \n« Le modèle frontier le plus puissant d'OpenAI contrôle directement votre ordinateur, traite 1 million de tokens et raisonne sur des tâches très longues. Annoncé comme réduisant les erreurs de 33 % et améliorant considérablement le code et l'analyse des documents »\n189 Visiter Payant Claude Opus 4.7 \n4.5\u002F5 \n« Le dernier modèle haut de gamme de chez Anthropic optimisé pour le code agentique, les tâches longues et les scénarios critiques en entreprise. Avec auto‑vérification des réponses et fenêtre de contexte d’un million de tokens. Il est également capable d'ajuster automatiquement la profondeur de sa réflexion selon la difficulté du problème»\n162 Visiter Payant GPT‑5.5 \n4.6\u002F5 \n« Résolvez des tâches longues et complexes en laissant l’IA planifier, utiliser des outils, vérifier son travail et aller jusqu’au bout avec moins de supervision. Ce modèle améliore nettement le codage, l’usage d’ordinateurs virtuels, la recherche en ligne et les missions de knowledge work»\n213 Visiter Payant Grok 4.5 \n4.5\u002F5 \n« Exploitez une IA puissante optimisée pour le code, la finance et les tâches autonomes. Ce nouveau modèle LLM de chez SpaceXAI est conçu pour être rapide, économique et capable de gérer des flux de travail compliqués de bout en bout »\n97 Visiter Gratuit DeepSeek V4 \n4.4\u002F5 \n« Ce modèle Mixture‑of‑Experts d’environ 1 000 milliards de paramètres offre un contexte de 1M tokens, une mémoire Engram quasi infinie et des capacités multimodales texte, image et vidéo. Il vise des scores proches de Claude Opus en code tout en restant bien moins cher ( prévus en Apache 2.0)»\n97 Visiter Freemium GPT‑5.5 Instant \n4.4\u002F5 \n« Utilisez un ChatGPT plus fiable et plus direct. Ce nouveau modèle réduit nettement les hallucinations sur les sujets sensibles (médecine, droit, finance). Il raccourcit ses réponses et personnalise davantage selon vos fichiers, conversations passées et compte Gmail connecté.»\n89 Visiter \n+ Voir plus d'IA \nCopier le code \nL'intelligence artificielle pour tous\nRessources Tutoriels, astuces et blog Agenda des conférences sur l'IA Glossaire des IA Emplois dans l'IA Newsletter Services pratiques Chaînes YouTube sur l'IA Top 100 IA Liste GPTs Outils IA Hubspot Meilleurs Agents IA Entreprise Ajouter un outil IA Annoncer Mettre à jour votre site Sponsorship ★ Media Kit Qui sommes-nous ? Contactez-nous \n© 2026 AIxploria. 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En acceptant ces technologies, vous nous permettez de traiter des données telles que le comportement de navigation ou des identifiants uniques sur ce site. \nFonctionnel \nFonctionnel \nToujours activé \nLe stockage ou l’accès technique est strictement nécessaire dans la finalité d’intérêt légitime de permettre l’utilisation d’un service spécifique explicitement demandé par l’abonné ou l’internaute, ou dans le seul but d’effectuer la transmission d’une communication sur un réseau de communications électroniques. \nPréférences \nPréférences \nLe stockage ou l’accès technique est nécessaire dans la finalité d’intérêt légitime de stocker des préférences qui ne sont pas demandées par l’abonné ou la personne utilisant le service. \nStatistiques \nStatistiques \nLe stockage ou l’accès technique qui est utilisé exclusivement à des fins statistiques. \nLe stockage ou l’accès technique qui est utilisé exclusivement dans des finalités statistiques anonymes. En l’absence d’une assignation à comparaître, d’une conformité volontaire de la part de votre fournisseur d’accès à internet ou d’enregistrements supplémentaires provenant d’une tierce partie, les informations stockées ou extraites à cette seule fin ne peuvent généralement pas être utilisées pour vous identifier. \nMarketing \nMarketing \nLe stockage ou l’accès technique est nécessaire pour créer des profils d’internautes afin d’envoyer des publicités, ou pour suivre l’internaute sur un site web ou sur plusieurs sites web ayant des finalités marketing similaires. \nGérer les options \nGérer les services \nGérer {vendor_count} fournisseurs \nEn s","2026-08-09T00:17:58.998Z",200,"text\u002Fhtml; charset=UTF-8","ba3d72a86bf01913c1ad956461d7268eac25ed5626384af9ef1fa622271a0753","data\u002Fcatalog\u002Fcache\u002Faixploria\u002F2215323891e39a5b6a7aad10b6ab8cb3c35baf693c39e5506ce6f84091e31dd6.html",true,"human_verified",{"url":913,"final_url":913,"status":971,"fetched_at":978,"content_hash":979,"title":980,"description":981,"headings":982,"pricing_evidence":999,"privacy_evidence":1009,"capability_evidence":1014,"text_excerpt":1018,"linked_pages":1019,"research_links":1206,"blog_titles":1229,"use_case_evidence":1231,"feature_evidence":1232},"2026-08-09T01:06:00.135Z","f5ccac69c1f506e1496beb18c81d957fefb70d33e3ae1f379adc20bf9ab6e637","zai-org\u002FGLM-5.2 · Hugging Face","We’re on a journey to advance and democratize artificial intelligence through open source and open science.",[983,984,853,985,986,987,988,989,990,991,992,853,993,994,995,996,997,998],"zai-org \u002F GLM-5.2 like 4.9k Follow Z.ai 18.3k","Instructions to use zai-org\u002FGLM-5.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.","Introduction","Benchmark","Serve GLM-5.2 Locally","Footnote","Citation","Model tree for zai-org\u002FGLM-5.2","Spaces using zai-org\u002FGLM-5.2 100","Collection including zai-org\u002FGLM-5.2","Papers for zai-org\u002FGLM-5.2","IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse","GLM-5: from Vibe Coding to Agentic Engineering","Article mentioning zai-org\u002FGLM-5.2","GLM-5.2: Built for Long-Horizon Tasks","Evaluation results",[1000,1001,1002,1003,1004,1005,1006,1007,1008],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up \",\"pad_token\":\"\u003C|endoftext|>\"},\"chat_template_jinja\":\"[gMASK]\u003Csop>\\n{%- set effective_reasoning_effort = 'high' if reasoning_effort is defined and reasoning_effort == 'high' else 'max' -%}\\n{%- if (enable_thinking is not defined or enable_thinking) and effective_reasoning_effort is not none -%}\u003C|system|>Reasoning Effort: {{ effective_reasoning_effort | capitalize }}{%- endif -%}\\n{%- if tools -%}\\n{%- macro tool_to_json(tool) -%}\\n {%- set ns_tool = namespace(first=true) -%}\\n {{ '{' -}}\\n {%- for k, v in tool.items() -%}\\n {%- if k != 'defer_loading' and k != 'strict' -%}\\n {%- if not ns_tool.first -%}{{- ', ' -}}{%- endif -%}\\n {%- set ns_tool.first = false -%}\\n \\\"{{ k }}\\\": {{ v | tojson(ensure_ascii=False) }}\\n {%- endif -%}\\n {%- endfor -%}\\n {{- '}' -}}\\n{%- endmacro -%}\\n\u003C|system|>\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within \u003Ctools>\u003C\u002Ftools> XML tags:\\n\u003Ctools>\\n{% for tool in tools %}\\n{%- if 'function' in tool -%}\\n {%- set tool = tool['function'] -%}\\n{%- endif -%}\\n{% if tool.defer_loading is not defined or not tool.defer_loading %}\\n{{ tool_to_json(tool) }}\\n{% endif %}\\n{% endfor %}\\n\u003C\u002Ftools>\\n\\nFor each function call, output the function name and arguments within the following XML format:\\n\u003Ctool_call>{function-name}\u003Carg_key>{arg-key-1}\u003C\u002Farg_key>\u003Carg_value>{arg-value-1}\u003C\u002Farg_value>\u003Carg_key>{arg-key-2}\u003C\u002Farg_key>\u003Carg_value>{arg-value-2}\u003C\u002Farg_value>...\u003C\u002Ftool_call>{%- endif -%}\\n{%- macro visible_text(content) -%}\\n {%- if content is string -%}\\n {{- content }}\\n {%- elif content is iterable and content is not mapping -%}\\n {%- for item in content -%}\\n {%- if item is mapping and item.type == 'text' -%}\\n {{- item.text }}\\n {%- elif item is string -%}\\n {{- item }}\\n {%- elif item is mapping and item.type in ['image', 'image_url', 'video', 'video_url', 'audio', 'audio_url', 'input_audio'] -%}\\n {%- set media_type = item.type | replace('_url', '') | replace('input_', '') -%}\\n {{- \\\"\u003Creminder>You are unable to process this \\\" ~ media_type ~ \\\" because you don't have multi-modal input ability. Try different methods.\u003C\u002Freminder>\\\" }}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- else -%}\\n {{- content }}\\n {%- endif -%}\\n{%- endmacro -%}\\n{%- set ns = namespace(last_user_index=-1) -%}\\n{%- for m in messages %}\\n {%- if m.role == 'user' %}\\n {%- set ns.last_user_index = loop.index0 -%}\\n {%- endif %}\\n{%- endfor %}\\n{%- for m in messages -%}\\n{%- if m.role == 'user' -%}\u003C|user|>{{ visible_text(m.content) }}\\n{%- elif m.role == 'assistant' -%}\\n\u003C|assistant|>\\n{%- set content = visible_text(m.content) %}\\n{%- if m.reasoning_content is string %}\\n {%- set reasoning_content = m.reasoning_content %}\\n{%- elif '\u003C\u002Fthink>' in content %}\\n {%- set reasoning_content = content.split('\u003C\u002Fthink>')[0].split('\u003Cthink>')[-1] %}\\n {%- set content = content.split('\u003C\u002Fthink>')[-1] %}\\n{%- endif %}\\n{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content is defined -%}\\n{{ '\u003Cthink>' + reasoning_content + '\u003C\u002Fthink>'}}\\n{%- else -%}\\n{{ '\u003Cthink>\u003C\u002Fthink>' }}\\n{%- endif -%}\\n{%- if content.strip() -%}\\n{{ content.strip() }}\\n{%- endif -%}\\n{% if m.tool_calls %}\\n{% for tc in m.tool_calls %}\\n{%- if tc.function %}\\n {%- set tc = tc.function %}\\n{%- endif %}\\n{{- '\u003Ctool_call>' + tc.name -}}\\n{% set _args = tc.arguments %}{% for k, v in _args.items() %}\u003Carg_key>{{ k }}\u003C\u002Farg_key>\u003Carg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}\u003C\u002Farg_value>{% endfor %}\u003C\u002Ftool_call>{% endfor %}\\n{% endif %}\\n{%- elif m.role == 'tool' -%}\\n{%- if loop.first or (messages[loop.index0 - 1].role != \\\"tool\\\") %}\\n {{- '\u003C|observation|>' -}}\\n{%- endif %}\\n{%- if m.content is string -%}\\n {{- '\u003Ctool_response>' + m.content + '\u003C\u002Ftool_response>' -}}\\n{%- elif m.content is iterable and m.content is not mapping and m.content and m.content.0.type == \\\"tool_reference\\\" -%}\\n {{- '\u003Ctool_response>\u003Ctools>\\\\n' -}}\\n {% for tr in m.content %}\\n {%- for tool in tools -%}\\n {%- if 'function' in tool -%}\\n {%- set tool = tool['function'] -%}\\n {%- endif -%}\\n {%- if tool.name == tr.name -%}\\n {{- tool_to_json(tool) + '\\\\n' -}}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endfor -%}\\n {{- '\u003C\u002Ftools>\u003C\u002Ftool_response>' -}}\\n{%- elif m.content is iterable and m.content is not mapping and m.content and m.content.0 is mapping and m.content.0.output is defined -%}\\n {%- for tr in m.content -%}\\n {{- '\u003Ctool_response>' + tr.output + '\u003C\u002Ftool_response>' -}}\\n {%- endfor -%}\\n{%- else -%}\\n {{- '\u003Ctool_response>' + visible_text(m.content) + '\u003C\u002Ftool_response>' -}}\\n{% endif -%}\\n{%- elif m.role == 'system' -%}\\n\u003C|system|>{{ visible_text(m.content) }}\\n{%- endif -%}\\n{%- endfor -%}\\n{%- if add_generation_prompt -%}\\n \u003C|assistant|>{{- '\u003Cthink>\u003C\u002Fthink>' if (enable_thinking is defined and not enable_thinking) else '\u003Cthink>' -}}\\n{%- endif 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zai-org \u002F GLM-5.2 like 4.9k Follow Z.ai 18.3k","Pure Open : An MIT open-source license — no regional limits, technical access without borders","GLM-5.2 supports deployment with the following frameworks. 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what can you help me with?\"},{\"text\":\"What is 84 * 3 \u002F 2?\"},{\"text\":\"Tell me an interesting fact about the universe!\"},{\"text\":\"Explain quantum computing in simple terms.\"}],\"initialSelectedProvider\":\"novita\",\"widgetType\":\"conversational\",\"isMaximized\":false,\"isMac\":false}}\"> Inference Providers NEW Novita +5 Text Generation Examples Input a message to start chatting with zai-org\u002FGLM-5.2 . Send View Code Snippets Compare providers Model tree for zai-org\u002FGLM-5.2","zai-org • Jun 17 • 137 \",\"pad_token\":\"\u003C|endoftext|>\"},\"chat_template_jinja\":\"[gMASK]\u003Csop>\\n{%- set effective_reasoning_effort = 'high' if reasoning_effort is defined and reasoning_effort == 'high' else 'max' -%}\\n{%- if (enable_thinking is not defined or enable_thinking) and effective_reasoning_effort is not none -%}\u003C|system|>Reasoning Effort: {{ effective_reasoning_effort | capitalize }}{%- endif -%}\\n{%- if tools -%}\\n{%- macro tool_to_json(tool) -%}\\n {%- set ns_tool = namespace(first=true) -%}\\n {{ '{' -}}\\n {%- for k, v in tool.items() -%}\\n {%- if k != 'defer_loading' and k != 'strict' -%}\\n {%- if not ns_tool.first -%}{{- ', ' -}}{%- endif -%}\\n {%- set ns_tool.first = false -%}\\n \\\"{{ k }}\\\": {{ v | tojson(ensure_ascii=False) }}\\n {%- endif -%}\\n {%- endfor -%}\\n {{- '}' -}}\\n{%- endmacro -%}\\n\u003C|system|>\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within \u003Ctools>\u003C\u002Ftools> XML tags:\\n\u003Ctools>\\n{% for tool in tools %}\\n{%- if 'function' in tool -%}\\n {%- set tool = tool['function'] -%}\\n{%- endif -%}\\n{% if tool.defer_loading is not defined or not tool.defer_loading %}\\n{{ tool_to_json(tool) }}\\n{% endif %}\\n{% endfor %}\\n\u003C\u002Ftools>\\n\\nFor each function call, output the function name and arguments within the following XML format:\\n\u003Ctool_call>{function-name}\u003Carg_key>{arg-key-1}\u003C\u002Farg_key>\u003Carg_value>{arg-value-1}\u003C\u002Farg_value>\u003Carg_key>{arg-key-2}\u003C\u002Farg_key>\u003Carg_value>{arg-value-2}\u003C\u002Farg_value>...\u003C\u002Ftool_call>{%- endif -%}\\n{%- macro visible_text(content) -%}\\n {%- if content is string -%}\\n {{- content }}\\n {%- elif content is iterable and content is not mapping -%}\\n {%- for item in content -%}\\n {%- if item is mapping and item.type == 'text' -%}\\n {{- item.text }}\\n {%- elif item is string -%}\\n {{- item }}\\n {%- elif item is mapping and item.type in ['image', 'image_url', 'video', 'video_url', 'audio', 'audio_url', 'input_audio'] -%}\\n {%- set media_type = item.type | replace('_url', '') | replace('input_', '') -%}\\n {{- \\\"\u003Creminder>You are unable to process this \\\" ~ media_type ~ \\\" because you don't have multi-modal input ability. Try different methods.\u003C\u002Freminder>\\\" }}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- else -%}\\n {{- content }}\\n {%- endif -%}\\n{%- endmacro -%}\\n{%- set ns = namespace(last_user_index=-1) -%}\\n{%- for m in messages %}\\n {%- if m.role == 'user' %}\\n {%- set ns.last_user_index = loop.index0 -%}\\n {%- endif %}\\n{%- endfor %}\\n{%- for m in messages -%}\\n{%- if m.role == 'user' -%}\u003C|user|>{{ visible_text(m.content) }}\\n{%- elif m.role == 'assistant' -%}\\n\u003C|assistant|>\\n{%- set content = visible_text(m.content) %}\\n{%- if m.reasoning_content is string %}\\n {%- set reasoning_content = m.reasoning_content %}\\n{%- elif '\u003C\u002Fthink>' in content %}\\n {%- set reasoning_content = content.split('\u003C\u002Fthink>')[0].split('\u003Cthink>')[-1] %}\\n {%- set content = content.split('\u003C\u002Fthink>')[-1] %}\\n{%- endif %}\\n{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content is defined -%}\\n{{ '\u003Cthink>' + reasoning_content + '\u003C\u002Fthink>'}}\\n{%- else -%}\\n{{ '\u003Cthink>\u003C\u002Fthink>' }}\\n{%- endif -%}\\n{%- if content.strip() -%}\\n{{ content.strip() }}\\n{%- endif -%}\\n{% if m.tool_calls %}\\n{% for tc in m.tool_calls %}\\n{%- if tc.function %}\\n {%- set tc = tc.function %}\\n{%- endif %}\\n{{- '\u003Ctool_call>' + tc.name -}}\\n{% set _args = tc.arguments %}{% for k, v in _args.items() %}\u003Carg_key>{{ k }}\u003C\u002Farg_key>\u003Carg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}\u003C\u002Farg_value>{% endfor %}\u003C\u002Ftool_call>{% endfor %}\\n{% endif %}\\n{%- elif m.role == 'tool' -%}\\n{%- if loop.first or (messages[loop.index0 - 1].role != \\\"tool\\\") %}\\n {{- '\u003C|observation|>' -}}\\n{%- endif %}\\n{%- if m.content is string -%}\\n {{- '\u003Ctool_response>' + m.content + '\u003C\u002Ftool_response>' -}}\\n{%- elif m.content is iterable and m.content is not mapping and m.content and m.content.0.type == \\\"tool_reference\\\" -%}\\n {{- '\u003Ctool_response>\u003Ctools>\\\\n' -}}\\n {% for tr in m.content %}\\n {%- for tool in tools -%}\\n {%- if 'function' in tool -%}\\n {%- set tool = tool['function'] -%}\\n {%- endif -%}\\n {%- if tool.name == tr.name -%}\\n {{- tool_to_json(tool) + '\\\\n' -}}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endfor -%}\\n {{- '\u003C\u002Ftools>\u003C\u002Ftool_response>' -}}\\n{%- elif m.content is iterable and m.content is not mapping and m.content and m.content.0 is mapping and m.content.0.output is defined -%}\\n {%- for tr in m.content -%}\\n {{- '\u003Ctool_response>' + tr.output + '\u003C\u002Ftool_response>' -}}\\n {%- endfor -%}\\n{%- else -%}\\n {{- '\u003Ctool_response>' + visible_text(m.content) + '\u003C\u002Ftool_response>' -}}\\n{% endif -%}\\n{%- elif m.role == 'system' -%}\\n\u003C|system|>{{ visible_text(m.content) }}\\n{%- endif -%}\\n{%- endfor -%}\\n{%- if add_generation_prompt -%}\\n \u003C|assistant|>{{- '\u003Cthink>\u003C\u002Fthink>' if (enable_thinking is defined and not enable_thinking) else '\u003Cthink>' -}}\\n{%- endif 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Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fresearchclawbench.yaml\",\"verified\":false,\"pullRequest\":22,\"rank\":1,\"label\":\"Overall\",\"notes\":\"ResearchHarness evaluation with tools enabled, code execution, and a file-system workspace; completed 39\u002F40 ResearchClawBench tasks.\"},{\"dataset\":{\"id\":\"cais\u002Fhle\",\"isBenchmark\":false,\"task_id\":\"hle\"},\"value\":40.5,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fzai-org\u002FGLM-5.2\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fhle.yaml\",\"verified\":false,\"label\":\"Hle\"},{\"dataset\":{\"id\":\"cais\u002Fhle\",\"isBenchmark\":false,\"task_id\":\"hle\"},\"value\":54.7,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fzai-org\u002FGLM-5.2\",\"name\":\"Model Card\",\"isExternal\":false},\"filename\":\".eval_results\u002Fhle.yaml\",\"verified\":false,\"rank\":2,\"label\":\"Hle\",\"notes\":\"With tools\"},{\"dataset\":{\"id\":\"crosbylegal\u002FRedlineBench\",\"isBenchmark\":true,\"task_id\":\"redline_overall\"},\"value\":45.7,\"source\":{\"url\":\"https:\u002F\u002Fhuggingface.co\u002Fzai-org\u002FGLM-5.2\",\"name\":\"Model Card\",\"isExternal\":false,\"author\":{\"_id\":\"6a305ec8c17415c833135ab3\",\"avatarUrl\":\"https:\u002F\u002Fcdn-avatars.huggingface.co\u002Fv1\u002Fproduction\u002Fuploads\u002F6342f326aa45ed8ecf954c80\u002FbjQGFQhNkm75Za3XOByzu.png\",\"fullname\":\"Crosby\",\"name\":\"crosbylegal\",\"type\":\"org\",\"isHf\":false,\"isHfAdmin\":false,\"isMod\":false,\"followerCount\":7,\"isUserFollowing\":false}},\"filename\":\".eval_results\u002Fredlinebench.yaml\",\"verified\":false,\"pullRequest\":19,\"rank\":3,\"label\":\"Redline Overall\",\"notes\":\"agent=glm-5.2; 3-LLM judge panel (majority vote); turn-weighted weighted pass rate (0-100); post-publication run\"}],\"private\":false,\"repoType\":\"model\",\"gated\":false,\"tags\":[\"transformers\",\"safetensors\",\"glm_moe_dsa\",\"text-generation\",\"conversational\",\"en\",\"zh\",\"arxiv:2602.15763\",\"arxiv:2603.12201\",\"license:mit\",\"eval-results\",\"endpoints_compatible\",\"region:us\"],\"tag_objs\":[{\"id\":\"text-generation\",\"label\":\"Text Generation\",\"type\":\"pipeline_tag\",\"subType\":\"nlp\"},{\"id\":\"transformers\",\"label\":\"Transformers\",\"type\":\"library\"},{\"id\":\"safetensors\",\"label\":\"Safetensors\",\"type\":\"library\"},{\"id\":\"en\",\"label\":\"English\",\"type\":\"language\"},{\"id\":\"zh\",\"label\":\"Chinese\",\"type\":\"language\"},{\"id\":\"glm_moe_dsa\",\"label\":\"glm_moe_dsa\",\"type\":\"other\",\"clickable\":true},{\"id\":\"conversational\",\"label\":\"conversational\",\"type\":\"other\",\"clickable\":true},{\"id\":\"eval-results\",\"label\":\"Eval Results\",\"type\":\"other\",\"clickable\":true},{\"id\":\"endpoints_compatible\",\"label\":\"Inference Endpoints\",\"type\":\"other\",\"clickable\":true},{\"id\":\"arxiv:2602.15763\",\"label\":\"arxiv:2602.15763\",\"type\":\"arxiv\",\"extra\":{\"paperTitle\":\"GLM-5: from Vibe Coding to Agentic Engineering\"}},{\"id\":\"arxiv:2603.12201\",\"label\":\"arxiv:2603.12201\",\"type\":\"arxiv\",\"extra\":{\"paperTitle\":\"IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse\"}},{\"id\":\"license:mit\",\"label\":\"mit\",\"type\":\"license\"},{\"type\":\"region\",\"label\":\"🇺🇸 Region: US\",\"id\":\"region:us\"}],\"transformersInfo\":{\"auto_model\":\"AutoModelForCausalLM\",\"pipeline_tag\":\"text-generation\",\"processor\":\"AutoTokenizer\"},\"widgetData\":[{\"text\":\"Hi, what can you help me with?\"},{\"text\":\"What is 84 * 3 \u002F 2?\"},{\"text\":\"Tell me an interesting fact about the universe!\"},{\"text\":\"Explain quantum computing in simple terms.\"}],\"safetensors\":{\"parameters\":{\"BF16\":753329921024,\"F32\":19456},\"total\":753329940480,\"sharded\":true,\"totalFileSize\":1506672795440},\"hasBlockedOids\":false,\"region\":\"us\",\"isQuantized\":false,\"licenseFilePath\":\"LICENSE\"}}\"> Evaluation results","datacurve\u002Fdeep-swe · Deep Swe View evaluation results leaderboard 46.2 ScaleAI\u002FSWE-bench_Pro · SWE Bench Pro View evaluation results leaderboard 62.1 Idavidrein\u002Fgpqa · Diamond View evaluation results leaderboard 91.2 IntelligenceLab\u002FLong-Horizon-Terminal-Bench · Lhtb View evaluation results source 31.6 * InternScience\u002FResearchClawBench · Overall View evaluation results leaderboard 20.71 * cais\u002Fhle · Hle default View evaluation results 40.5 With tools View evaluation results 54.7 * Expand 1 benchmark System theme Company TOS Privacy About Careers Website Models Datasets Spaces Pricing Docs",[1000,1010,1011,1001,1012,1004,1013,1005,1006,1007,1008],"tokenizer = AutoTokenizer.from_pretrained(\"zai-org\u002FGLM-5.2\")","model = AutoModelForCausalLM.from_pretrained(\"zai-org\u002FGLM-5.2\", device_map=\"auto\")","PostTrainBench","PostTrainBench : The evaluation was conducted by PostTrainBench with 1M context length, max effort level, and 128K maximum output tokens.",[1000,1015,1016,1015,1016,1015,1016,1017,1005,1006,1007],"# Call the server using curl (OpenAI-compatible API):","\"content\": \"What is the capital of France?\"","📍 Use GLM-5.2 API services on Z.ai API Platform.","zai-org\u002FGLM-5.2 · Hugging Face \nHugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up \",\"pad_token\":\"\u003C|endoftext|>\"},\"chat_template_jinja\":\"[gMASK]\u003Csop>\\n{%- set effective_reasoning_effort = 'high' if reasoning_effort is defined and reasoning_effort == 'high' else 'max' -%}\\n{%- if (enable_thinking is not defined or enable_thinking) and effective_reasoning_effort is not none -%}\u003C|system|>Reasoning Effort: {{ effective_reasoning_effort | capitalize }}{%- endif -%}\\n{%- if tools -%}\\n{%- macro tool_to_json(tool) -%}\\n {%- set ns_tool = namespace(first=true) -%}\\n {{ '{' -}}\\n {%- for k, v in tool.items() -%}\\n {%- if k != 'defer_loading' and k != 'strict' -%}\\n {%- if not ns_tool.first -%}{{- ', ' -}}{%- endif -%}\\n {%- set ns_tool.first = false -%}\\n \\\"{{ k }}\\\": {{ v | tojson(ensure_ascii=False) }}\\n {%- endif -%}\\n {%- endfor -%}\\n {{- '}' -}}\\n{%- endmacro -%}\\n\u003C|system|>\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within \u003Ctools>\u003C\u002Ftools> XML tags:\\n\u003Ctools>\\n{% for tool in tools %}\\n{%- if 'function' in tool -%}\\n {%- set tool = tool['function'] -%}\\n{%- endif -%}\\n{% if tool.defer_loading is not defined or not tool.defer_loading %}\\n{{ tool_to_json(tool) }}\\n{% endif %}\\n{% endfor %}\\n\u003C\u002Ftools>\\n\\nFor each function call, output the function name and arguments within the following XML format:\\n\u003Ctool_call>{function-name}\u003Carg_key>{arg-key-1}\u003C\u002Farg_key>\u003Carg_value>{arg-value-1}\u003C\u002Farg_value>\u003Carg_key>{arg-key-2}\u003C\u002Farg_key>\u003Carg_value>{arg-value-2}\u003C\u002Farg_value>...\u003C\u002Ftool_call>{%- endif -%}\\n{%- macro visible_text(content) -%}\\n {%- if content is string -%}\\n {{- content }}\\n {%- elif content is iterable and content is not mapping -%}\\n {%- for item in content -%}\\n {%- if item is mapping and item.type == 'text' -%}\\n {{- item.text }}\\n {%- elif item is string -%}\\n {{- item }}\\n {%- elif item is mapping and item.type in ['image', 'image_url', 'video', 'video_url', 'audio', 'audio_url', 'input_audio'] -%}\\n {%- set media_type = item.type | replace('_url', '') | replace('input_', '') -%}\\n {{- \\\"\u003Creminder>You are unable to process this \\\" ~ media_type ~ \\\" because you don't have multi-modal input ability. Try different methods.\u003C\u002Freminder>\\\" }}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- else -%}\\n {{- content }}\\n {%- endif -%}\\n{%- endmacro -%}\\n{%- set ns = namespace(last_user_index=-1) -%}\\n{%- for m in messages %}\\n {%- if m.role == 'user' %}\\n {%- set ns.last_user_index = loop.index0 -%}\\n {%- endif %}\\n{%- endfor %}\\n{%- for m in messages -%}\\n{%- if m.role == 'user' -%}\u003C|user|>{{ visible_text(m.content) }}\\n{%- elif m.role == 'assistant' -%}\\n\u003C|assistant|>\\n{%- set content = visible_text(m.content) %}\\n{%- if m.reasoning_content is string %}\\n {%- set reasoning_content = m.reasoning_content %}\\n{%- elif '\u003C\u002Fthink>' in content %}\\n {%- set reasoning_content = content.split('\u003C\u002Fthink>')[0].split('\u003Cthink>')[-1] %}\\n {%- set content = content.split('\u003C\u002Fthink>')[-1] %}\\n{%- endif %}\\n{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content is defined -%}\\n{{ '\u003Cthink>' + reasoning_content + '\u003C\u002Fthink>'}}\\n{%- else -%}\\n{{ '\u003Cthink>\u003C\u002Fthink>' }}\\n{%- endif -%}\\n{%- if content.strip() -%}\\n{{ content.strip() }}\\n{%- endif -%}\\n{% if m.tool_calls %}\\n{% for tc in m.tool_calls %}\\n{%- if tc.function %}\\n {%- set tc = tc.function %}\\n{%- endif %}\\n{{- '\u003Ctool_call>' + tc.name -}}\\n{% set _args = tc.arguments %}{% for k, v in _args.items() %}\u003Carg_key>{{ k }}\u003C\u002Farg_key>\u003Carg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}\u003C\u002Farg_value>{% endfor %}\u003C\u002Ftool_call>{% endfor %}\\n{% endif %}\\n{%- elif m.role == 'tool' -%}\\n{%- if loop.first or (messages[loop.index0 - 1].role != \\\"tool\\\") %}\\n {{- '\u003C|observation|>' -}}\\n{%- endif %}\\n{%- if m.content is string -%}\\n {{- '\u003Ctool_response>' + m.content + '\u003C\u002Ftool_response>' -}}\\n{%- elif m.content is iterable and m.content is not mapping and m.content and m.content.0.type == \\\"tool_reference\\\" -%}\\n {{- '\u003Ctool_response>\u003Ctools>\\\\n' -}}\\n {% for tr in m.content %}\\n {%- for tool in tools -%}\\n {%- if 'function' in tool -%}\\n {%- set tool = tool['function'] -%}\\n {%- endif -%}\\n {%- if tool.name == tr.name -%}\\n {{- tool_to_json(tool) + '\\\\n' -}}\\n {%- endif -%}\\n {%- endfor -%}\\n {%- endfor -%}\\n {{- '\u003C\u002Ftools>\u003C\u002Ftool_response>' -}}\\n{%- elif m.content is iterable and m.content is not mapping and m.content and m.content.0 is mapping and m.content.0.output is defined -%}\\n {%- for tr in m.content -%}\\n {{- '\u003Ctool_response>' + tr.output + '\u003C\u002Ftool_response>' -}}\\n {%- endfor -%}\\n{%- else -%}\\n {{- '\u003Ctool_response>' + visible_text(m.content) + '\u003C\u002Ftool_response>' -}}\\n{% endif -%}\\n{%- elif m.role == 'system' -%}\\n\u003C|system|>{{ visible_text(m.content) }}\\n{%- endif -%}\\n{%- endfor -%}\\n{%- if add_generation_prompt -%}\\n \u003C|assistant|>{{- '\u003Cthink>\u003C\u002Fthink>' if (enable_thinking is defined and not enable_thinking) else '\u003Cthink>' -}}\\n{%- endif 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zai-org \u002F GLM-5.2 like 4.9k Follow Z.ai 18.3k \nText Generation Transformers Safetensors English Chinese glm_moe_dsa conversational Eval Results arxiv: 2602.15763 arxiv: 2603.12201 License: mit Model card Files Files and versions xet Community 60 Deploy Copy to bucket new Use this model Instructions to use zai-org\u002FGLM-5.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.\nLibraries Transformers How to use zai-org\u002FGLM-5.2 with Transformers:\n# Use a pipeline as a high-level helper\nfrom transformers import pipeline\npipe = pipeline(\"text-generation\", model=\"zai-org\u002FGLM-5.2\")\nmessages = [\n{\"role\": \"user\", \"content\": \"Who are you?\"},\n]\npipe(messages) # Load model directly\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\ntokenizer = AutoTokenizer.from_pretrained(\"zai-org\u002FGLM-5.2\")\nmodel = AutoModelForCausalLM.from_pretrained(\"zai-org\u002FGLM-5.2\", device_map=\"auto\")\nmessages = [\n{\"role\": \"user\", \"content\": \"Who are you?\"},\n]\ninputs = tokenizer.apply_chat_template(\nmessages,\nadd_generation_prompt=True,\ntokenize=True,\nreturn_dict=True,\nreturn_tensors=\"pt\",\n).to(model.device)\noutputs = model.generate(**inputs, max_new_tokens=40)\nprint(tokenizer.decode(outputs[0][inputs[\"input_ids\"].shape[-1]:])) Inference Inference Providers HuggingChat Notebooks Google Colab Kaggle Local Apps Settings vLLM How to use zai-org\u002FGLM-5.2 with vLLM:\nInstall from pip and serve model\n# Install vLLM from pip:\npip install vllm\n# Start the vLLM server:\nvllm serve \"zai-org\u002FGLM-5.2\"\n# Call the server using curl (OpenAI-compatible API):\ncurl -X POST \"http:\u002F\u002Flocalhost:8000\u002Fv1\u002Fchat\u002Fcompletions\" \\\n-H \"Content-Type: application\u002Fjson\" \\\n--data '{\n\"model\": \"zai-org\u002FGLM-5.2\",\n\"messages\": [\n{\n\"role\": \"user\",\n\"content\": \"What is the capital of France?\"\n}\n]\n}' Use Docker\ndocker model run hf.co\u002Fzai-org\u002FGLM-5.2 SGLang How to use zai-org\u002FGLM-5.2 with SGLang:\nInstall from pip and serve model\n# Install SGLang from pip:\npip install sglang\n# Start the SGLang server:\npython3 -m sglang.launch_server \\\n--model-path \"zai-org\u002FGLM-5.2\" \\\n--host 0.0.0.0 \\\n--port 30000\n# Call the server using curl (OpenAI-compatible API):\ncurl -X POST \"http:\u002F\u002Flocalhost:30000\u002Fv1\u002Fchat\u002Fcompletions\" \\\n-H \"Content-Type: application\u002Fjson\" \\\n--data '{\n\"model\": \"zai-org\u002FGLM-5.2\",\n\"messages\": [\n{\n\"role\": \"user\",\n\"content\": \"What is the capital of France?\"\n}\n]\n}' Use Docker images\ndocker run --gpus all \\\n--shm-size 32g \\\n-p 30000:30000 \\\n-v ~\u002F.cache\u002Fhuggingface:\u002Froot\u002F.cache\u002Fhuggingface \\\n--env \"HF_TOKEN=\u003Csecret>\" \\\n--ipc=host \\\nlmsysorg\u002Fsglang:latest \\\npython3 -m sglang.launch_server \\\n--model-path \"zai-org\u002FGLM-5.2\" \\\n--host 0.0.0.0 \\\n--port 30000\n# Call the server using curl (OpenAI-compatible API):\ncurl -X POST \"http:\u002F\u002Flocalhost:30000\u002Fv1\u002Fchat\u002Fcompletions\" \\\n-H \"Content-Type: application\u002Fjson\" \\\n--data '{\n\"model\": \"zai-org\u002FGLM-5.2\",\n\"messages\": [\n{\n\"role\": \"user\",\n\"content\": \"What is the capital of France?\"\n}\n]\n}' Docker Model Runner How to use zai-org\u002FGLM-5.2 with Docker Model Runner:\ndocker model run hf.co\u002Fzai-org\u002FGLM-5.2 Browse\nQuantizations to use this model in llama.cpp , Ollama , LM Studio , or any compatible app. GLM-5.2 Introduction Benchmark Serve GLM-5.2 Locally Footnote Citation \nGLM-5.2\n👋 Join our WeChat or Discord community.\n📖 Check out the GLM-5.2 blog and GLM-5 Technical report .\n📍 Use GLM-5.2 API services on Z.ai API Platform. \n🔜 Try GLM-5.",[1020,1037,1054,1099,1134,1149,1181,1200],{"url":1021,"kind":1022,"anchor":1023,"title":1024,"description":981,"headings":1025,"feature_evidence":1027,"use_case_evidence":1034,"fetched_at":1035,"status":971,"content_hash":1036},"https:\u002F\u002Fhuggingface.co\u002Fdocs","feature","Docs","Hugging Face - Documentation",[1026],"Documentation",[1028,1029,1030,1031,1032,1033],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Documentation","Guides, references, and API docs for the Hugging Face ecosystem.","API for metadata, stats, and content of HF Hub datasets","Serve language models with TGI optimized toolkit","Parameter-efficient finetuning for large language models","AutoTrain API and UI for seamless model training",[1028],"2026-08-09T01:05:56.928Z","f155ae9c4d5ca17faabec58211d0e0b8cf6b7e006c765d8d97803acfa33cb525",{"url":1038,"kind":1022,"anchor":899,"title":1039,"description":981,"headings":1040,"feature_evidence":1048,"use_case_evidence":1051,"fetched_at":1052,"status":971,"content_hash":1053},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fsafetensors","Safetensors · Hugging Face",[1041,1041,1042,1043,1044,1045,1046,1047],"Safetensors","Installation","Usage","Load tensors","Save tensors","Format","Featured Projects",[1049,1050],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Safetensors documentation","🏡 View all docs AWS Trainium & Inferentia Accelerate Argilla AutoTrain Bitsandbytes CLI Chat UI Dataset viewer Datasets Deploying on AWS Diffusers Distilabel Evaluate Google Cloud Google TPUs Gradio Hub Hub Python Library Huggingface.js Inference Endpoints (dedicated) Inference Providers Kernels LeRobot Leaderboards Lighteval Microsoft Azure OpenEnv Optimum PEFT Reachy Mini Safetensors Sentence Transformers TRL Tasks Text Embeddings Inference Text Generation Inference Tokenizers Trackio Transformers Transformers.js Xet smolagents timm Search documentation main v0.5.0-rc.0 v0.3.2 v0.2.9 EN Getting started 🤗 Safetensors Speed Comparison Tensor Sharing in Pytorch Metadata Parsing Convert weights to safetensors API Torch API Tensorflow API PaddlePaddle API Flax API Numpy API You are viewing main version, which requires installation from source . If you'd like",[1049],"2026-08-09T01:05:57.411Z","c24d21e8328aee8e3840f71b1de1f19a81c2c8b4765680024c18db5c9f7d441b",{"url":1055,"kind":1022,"anchor":1056,"title":1057,"description":981,"headings":1058,"feature_evidence":1076,"use_case_evidence":1095,"fetched_at":1097,"status":971,"content_hash":1098},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Finference-providers","NEW","Inference Providers · Hugging Face",[1059,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1068,1069,1072,1073,1074,1075],"Inference Providers","Quick Setup for Agents","Partners","Why Choose Inference Providers?","Key Features","Getting Started","Inference Playground","Authentication","Quick Start - LLM","Python","JavaScript","HTTP \u002F cURL","Quick Start - Text-to-Image Generation","Provider Selection","API as a Proxy Service","Client-Side Provider Selection (Inference Clients)","Alternative: OpenAI-Compatible Chat Completions Endpoint (Chat Only)",[1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1073,1090,1091,1092,1093,1094],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Inference Providers documentation","🏡 View all docs AWS Trainium & Inferentia Accelerate Argilla AutoTrain Bitsandbytes CLI Chat UI Dataset viewer Datasets Deploying on AWS Diffusers Distilabel Evaluate Google Cloud Google TPUs Gradio Hub Hub Python Library Huggingface.js Inference Endpoints (dedicated) Inference Providers Kernels LeRobot Leaderboards Lighteval Microsoft Azure OpenEnv Optimum PEFT Reachy Mini Safetensors Sentence Transformers TRL Tasks Text Embeddings Inference Text Generation Inference Tokenizers Trackio Transformers Transformers.js Xet smolagents timm Search documentation main EN Get Started Inference Providers Pricing and Billing Hub integration Security Guides Your First API Call Building Your First AI App Structured Outputs with LLMs Function Calling Responses API (beta) How to use OpenAI gpt-oss Build an Image Editor Automating Code Review with GitHub Actions Agentic Coding Environments with OpenEnv Evaluating Models with Inspect Integrations Overview Pi OpenCode Codex Claude Code Hermes Agent NeMo Data Designer MacWhisper Vision Agents VS Code with GitHub Copilot Add Your Integration Inference Tasks Chat Completion Feature Extraction Text to Image Text to Video Other Tasks Providers Baseten Cerebras Cohere DeepInfra Fal AI Featherless AI Fireworks Groq HF Inference Novita Nscale OVHcloud AI Endpoints Public AI Replicate Scaleway Together WaveSpeedAI Z.ai Hub API Register as an Inference Provider Join the Hugging Face community and get access to the augmented documentation experience","Our platform integrates with leading AI infrastructure providers, giving you access to their specialized capabilities through a single, consistent API. Here’s what each partner supports:","When you build AI applications, it’s tough to manage multiple provider APIs, comparing model performance, and dealing with varying reliability. Inference Providers solves these challenges by offering:","Instant Access to Cutting-Edge Models : Go beyond mainstream providers to access thousands of specialized models across multiple AI tasks. Whether you need the latest language models, state-of-the-art image generators, or domain-specific embeddings, you’ll find them here.","Text Generation : Use Large language models with tool-calling capabilities for chatbots, content generation, and code assistance Image and Video Generation : Create custom images and videos, including support for LoRAs and style customization Search & Retrieval : State-of-the-art embeddings for semantic search, RAG systems, and recommendation engines Traditional ML Tasks : Ready-to-use models for classification, NER, summarization, and speech recognition ⚡ Get Started for Free : Inference Providers includes a generous free tier, with additional credits for PRO users and Team & Enterprise organizations .","🎯 All-in-One API : A single API for text generation, image generation, document embeddings, NER, summarization, image classification, and more. 🔀 Multi-Provider Support : Easily run models from top-tier providers like fal, Replicate, Together AI, and others. 🚀 Scalable & Reliable : Built for high availability and low-latency performance in production environments. 🔧 Developer-Friendly : Simple requests, fast responses, and a consistent developer experience across Python and JavaScript clients. 👷 Easy to integrate : Drop-in replacement for the OpenAI chat completions API. 💰 Cost-Effective : No extra markup on provider rates. Getting Started","Inference Providers works with your existing development workflow. Whether you prefer Python, JavaScript, or direct HTTP calls, we provide native SDKs and OpenAI-compatible APIs to get you up and running quickly.","Prefer the terminal? Run hf models ls --warm with the hf CLI to list every model served by at least one provider, and add --json for scripts and agents. See the Hub API page for the full set of filters.","Let’s start with the most common use case: conversational AI using large language models. This section demonstrates how to perform chat completions using DeepSeek V3, showcasing the different ways you can integrate Inference Providers into your applications.","For testing, debugging, or integrating with any HTTP client, here’s the raw REST API format.","client = InferenceClient(api_key=os.environ[ \"HF_TOKEN\" ])","The Inference Providers API acts as a unified proxy layer that sits between your application and multiple AI providers. Understanding how provider selection works is crucial for optimizing performance, cost, and reliability in your applications.","Unified Authentication & Billing : Use a single Hugging Face token for all providers Automatic Failover : When using automatic provider selection ( provider=\"auto\" ), requests are automatically routed to alternative providers if the primary provider is flagged as unavailable by our validation system Consistent Interface through client libraries : When using our client libraries, the same request format works across different providers Because the API acts as a proxy, the exact HTTP request may vary between providers as each provider has their own API requirements and response formats. When using our official client libraries (JavaScript or Python), these provider-specific differences are handled automatically whether you use provider=\"auto\" or specify a particular provider.","When using the Hugging Face inference clients (JavaScript or Python), you can explicitly specify a provider or let the system choose automatically. The client then formats the HTTP request to match the selected provider’s API requirements.","If you prefer to work with familiar OpenAI APIs or want to migrate existing chat completion code with minimal changes, we offer a drop-in compatible endpoint that handles all provider selection automatically on the server side.","apiKey : process. env . HF_TOKEN ,","Announcement Blog Post : Learn more about the launch of Inference Providers Pricing and Billing : Understand costs and billing of Inference Providers Hub Integration : Learn how Inference Providers are integrated with the Hugging Face Hub Register as a Provider : Requirements to join our partner network as a provider Hub API : Advanced API features and configuration API Reference : Complete parameter documentation for all supported tasks Update on GitHub",[1077,1084,1096,1086],"Before diving into integration, explore models interactively with our Inference Playground . Test different chat completion models with your prompts and compare responses to find the perfect fit for your use case.","2026-08-09T01:05:57.831Z","b5a537507ac6f4e7b274effb72d1408348b5138d29493944c69265b70b1deeca",{"url":1100,"kind":1022,"anchor":899,"title":1101,"description":981,"headings":1102,"feature_evidence":1123,"use_case_evidence":1131,"fetched_at":1132,"status":971,"content_hash":1133},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Fmodel-cards#specifying-a-base-model","Model Cards · Hugging Face",[1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122],"Hub","Model Cards","What are Model Cards?","Model card metadata","Adding metadata to your model card","Using the metadata UI","Editing the YAML section of the README.md file","Specifying a library","Specifying a base model","Specifying a new version","Specifying a dataset","Specifying a bucket","Specifying a task ( pipeline_tag )","Specifying a license","Evaluation Results","CO2 Emissions","Linking a Paper","Model Card text","FAQ","How are model tags determined?",[1124,1125,1126,1127,1128,1129,1130],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Hub documentation","🏡 View all docs AWS Trainium & Inferentia Accelerate Argilla AutoTrain Bitsandbytes CLI Chat UI Dataset viewer Datasets Deploying on AWS Diffusers Distilabel Evaluate Google Cloud Google TPUs Gradio Hub Hub Python Library Huggingface.js Inference Endpoints (dedicated) Inference Providers Kernels LeRobot Leaderboards Lighteval Microsoft Azure OpenEnv Optimum PEFT Reachy Mini Safetensors Sentence Transformers TRL Tasks Text Embeddings Inference Text Generation Inference Tokenizers Trackio Transformers Transformers.js Xet smolagents timm Search documentation EN API docs 🤗 Hugging Face Hub Team & Enterprise Plans Single Sign-On (SSO) Audit Logs Storage Regions Data Studio for Private datasets Resource Groups (Access Control) Advanced Compute Options Advanced Security Tokens Management Service Accounts Publisher Analytics Gating Group Collections Network Security Download Analytics Rate Limits Blog Articles PRO Plan Repositories Getting Started with Repositories Repository Settings Storage Limits Storage Backend (Xet) Local Cache Pull Requests & Discussions Notifications Collections Webhooks GitHub Actions Notebooks Next Steps Licenses Models The Model Hub Model Cards Carbon Emissions Card Components Eval Results Leaderboard Data Gated Models Uploading Models Downloading Models Integrated Libraries Model Widgets Model Inference Models Download Stats Model Release Checklist Hardware Local Apps Frequently Asked Questions Advanced Topics Datasets Datasets Overview Dataset Cards Gated Datasets Uploading Datasets Uploading Datasets (for LLMs) Downloading Datasets Streaming Datasets Integrated Libraries Data Studio Agent Traces Datasets Download Stats Spaces Spaces Overview Spaces GPU Upgrades Spaces ZeroGPU Spaces Dev Mode Spaces Disk Usage & Storage Spaces Custom Domain Spaces as MCP servers Spaces as Agent Tools Spaces as API Endpoints Gradio Spaces Streamlit Spaces Static HTML Spaces Docker Spaces Embed your Space Run Spaces with Docker Spaces Configuration Reference Sign-In with HF button Featured Spaces Spaces Changelog Advanced Topics Storage Buckets new Access Patterns S3 Compatibility Bucket Integrations Bucket Security Jobs Jobs Overview Quickstart Pricing and Billing Manage Jobs Configuration Popular Images Serve Models Examples & Tutorials Process Large Datasets Schedule Jobs Webhook Automation Reference Agents Agents Overview Hugging Face CLI for AI Agents Hugging Face MCP Server Hugging Face Agent Skills Building agents with the HF SDK Local Agents with llama.cpp Agent Libraries Session Traces Format Other Organizations Billing Security Moderation Paper Pages Academia Hub Blog Articles Search Digital Object Identifier (DOI) Hub API Endpoints OAuth \u002F Sign in with HF Join the Hugging Face community and get access to the augmented documentation experience","the model its intended uses & potential limitations, including biases and ethical considerations as detailed in Mitchell, 2018 the training params and experimental info (you can embed or link to an experiment tracking platform for reference) which datasets were used to train your model the model’s evaluation results The model card template is available here .","Allowing users to filter models at https:\u002F\u002Fhuggingface.co\u002Fmodels . Displaying the model’s license. Adding datasets to the metadata will add a message reading Datasets used to train: to your model page and link the relevant datasets, if they’re available on the Hub. Dataset and language identifiers are those listed on the Datasets and Languages pages.","language:","- \"List of ISO 639-1 code for your language\"","You can specify the pipeline_tag in the model card metadata. The pipeline_tag indicates the type of task the model is intended for. This tag will be displayed on the model page and users can filter models on the Hub by task. This tag is also used to determine which widget to use for the model and which APIs to use under the hood.",[1124],"2026-08-09T01:05:58.133Z","068faa0f1c0c18417bf8894078d2e0ff6cdad0789fee70651d0f66498e9cd5e7",{"url":1135,"kind":1022,"anchor":899,"title":1136,"description":981,"headings":1137,"feature_evidence":1144,"use_case_evidence":1146,"fetched_at":1147,"status":971,"content_hash":1148},"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fhub\u002Feval-results","Evaluation Results · Hugging Face",[1103,1117,1138,1139,1140,1141,1142,1143],"Benchmark Datasets","Model Evaluation Results","Adding Evaluation Results","Community Contributions","Registering a Benchmark","Eval.yaml specification",[1124,1145],"🏡 View all docs AWS Trainium & Inferentia Accelerate Argilla AutoTrain Bitsandbytes CLI Chat UI Dataset viewer Datasets Deploying on AWS Diffusers Distilabel Evaluate Google Cloud Google TPUs Gradio Hub Hub Python Library Huggingface.js Inference Endpoints (dedicated) Inference Providers Kernels LeRobot Leaderboards Lighteval Microsoft Azure OpenEnv Optimum PEFT Reachy Mini Safetensors Sentence Transformers TRL Tasks Text Embeddings Inference Text Generation Inference Tokenizers Trackio Transformers Transformers.js Xet smolagents timm Search documentation EN API docs 🤗 Hugging Face Hub Team & Enterprise Plans Single Sign-On (SSO) Audit Logs Storage Regions Data Studio for Private datasets Resource Groups (Access Control) Advanced Compute Options Advanced Security Tokens Management Service Accounts Publisher Analytics Gating Group Collections Network Security Download Analytics Rate Limits Blog Articles PRO Plan Repositories Getting Started with Repositories Repository Settings Storage Limits Storage Backend (Xet) Local Cache Pull Requests & Discussions Notifications Collections Webhooks GitHub Actions Notebooks Next Steps Licenses Models The Model Hub Model Cards Eval Results Leaderboard Data Gated Models Uploading Models Downloading Models Integrated Libraries Model Widgets Model Inference Models Download Stats Model Release Checklist Hardware Local Apps Frequently Asked Questions Advanced Topics Datasets Datasets Overview Dataset Cards Gated Datasets Uploading Datasets Uploading Datasets (for LLMs) Downloading Datasets Streaming Datasets Integrated Libraries Data Studio Agent Traces Datasets Download Stats Spaces Spaces Overview Spaces GPU Upgrades Spaces ZeroGPU Spaces Dev Mode Spaces Disk Usage & Storage Spaces Custom Domain Spaces as MCP servers Spaces as Agent Tools Spaces as API Endpoints Gradio Spaces Streamlit Spaces Static HTML Spaces Docker Spaces Embed your Space Run Spaces with Docker Spaces Configuration Reference Sign-In with HF button Featured Spaces Spaces Changelog Advanced Topics Storage Buckets new Access Patterns S3 Compatibility Bucket Integrations Bucket Security Jobs Jobs Overview Quickstart Pricing and Billing Manage Jobs Configuration Popular Images Serve Models Examples & Tutorials Process Large Datasets Schedule Jobs Webhook Automation Reference Agents Agents Overview Hugging Face CLI for AI Agents Hugging Face MCP Server Hugging Face Agent Skills Building agents with the HF SDK Local Agents with llama.cpp Agent Libraries Session Traces Format Other Organizations Billing Security Moderation Paper Pages Academia Hub Blog Articles Search Digital Object Identifier (DOI) Hub API Endpoints OAuth \u002F Sign in with HF Join the Hugging Face community and get access to the augmented documentation experience",[1124],"2026-08-09T01:05:58.544Z","b2da7b93c6dca9346418d72d08aa7f8cf47cc16c5b1355ed7d9f8ebdec65da09",{"url":1150,"kind":1151,"anchor":1152,"title":1153,"description":1154,"headings":1155,"feature_evidence":1175,"use_case_evidence":1178,"fetched_at":1179,"status":971,"content_hash":1180},"https:\u002F\u002Fhuggingface.co\u002Fpapers","research","Daily Papers","Daily Papers - Hugging Face","Your daily dose of AI research from AK",[1152,1156,1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,1173,1174],"by AK and the research community","AgentOPSD: Recursive Self-Distillation for Agentic Reinforcement Learning","OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models","Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval","WorldClaw: Agentic 3D Open-World Generation at Scale","GST-Bench: Can VLMs Develop Global Spatial Awareness from Video?","EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning","ChronoVision: Temporal Reasoning via Latent State Reconstruction","Learning from Failures: Retrieval-Centric CoT via Hard Negatives for Unified Multimodal Retrieval","HarnessOpt-Bench: Evaluating LLMs at Harness Optimization","From Economic Agents to Agentic Economies: A Systems Blueprint for Economic World Models","DataSpace: Benchmarking Data Agents for Verifiable Analytics over Heterogeneous Workspaces","On-Policy Delta Distillation for Multilingual Math Reasoning","Teaching Nemotron Greek: Mining a Corpus, Adapting Retrieval, and Grounding Generation for Modern Greek across Specialist Domains","World-to-Wrist: Task-Conditioned Future Wrist Modeling for Fine-Grained Robot Manipulation","DyPES-VLA: Learning Shared Dynamics Priors and Embodiment-Specific Control for Cross-Embodiment Manipulation","Activity Frames: Deterministic Screen-Activity Compilation for Agent Memory and Replay","SmartMage: Dynamic Modality Orchestration for 3D Scene Understanding","EffectLearner: World-Aware Object-Effect Reasoning for Real-World Video Object Removal",[1176,1177],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up new Get trending papers in your email inbox once a day!","Kandinsky Lab 53 3 Submitted by Uri-ka 14 MameLoshnLM: Yiddish Language Model and Evaluation Benchmark",[1176],"2026-08-09T01:05:59.125Z","e305313cefbce7a7470caccecb7c1a9bcc222f6a03ba48c1022d4a170a0ef277",{"url":1182,"kind":1183,"title":1182,"description":899,"feature_evidence":1184,"use_case_evidence":1197,"fetched_at":1198,"status":971,"content_hash":1199},"https:\u002F\u002Fhuggingface.co\u002Fsitemap-blog.xml","blog",[1185,1186,1187,1188,1189,1190,1191,1192,1193,1194,1195,1196],"https:\u002F\u002Fhuggingface.co\u002Fblog\u002Frapidfireai","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fanylanguagemodel","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fintel-protein-language-model-protst","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Ftgi-messages-api","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Frun-musicgen-as-an-api","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Funity-api","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fchinese-language-blog","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fintel-sapphire-rapids-inference","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fvision_language_pretraining","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Fintel-sapphire-rapids","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Flarge-language-models","https:\u002F\u002Fhuggingface.co\u002Fblog\u002Ffew-shot-learning-gpt-neo-and-inference-api",[],"2026-08-09T01:05:59.515Z","6f233b122ad09e75c57f6654c77242f0f50f9b12865961ac10367d14d836bb82",{"url":1201,"kind":1151,"title":1201,"description":899,"feature_evidence":1202,"use_case_evidence":1203,"fetched_at":1204,"status":971,"content_hash":1205},"https:\u002F\u002Fhuggingface.co\u002Fsitemap-papers.xml",[],[],"2026-08-09T01:05:59.985Z","ad2833e02848abff8ab6f61e4988203adb61395a39ac87b4aad5879cda9bddf4",[1207,1208,1211,1214,1217,1220,1223,1226,1228],{"url":1150,"title":1152,"kind":1151},{"url":1209,"title":1210,"kind":1151},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2602.15763","Technical report",{"url":1212,"title":1213,"kind":1151},"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2602.15763","Paper",{"url":1215,"title":1216,"kind":1151},"https:\u002F\u002Farxiv.org\u002Fabs\u002F2603.12201","IndexShare",{"url":1218,"title":1219,"kind":1151},"https:\u002F\u002Fhuggingface.co\u002Fpapers\u002F2603.12201","IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse\nPaper • 2603.12201 • Published Mar 12 • 64",{"url":1221,"title":1222,"kind":1151},"https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FInternScience\u002FResearchClawBench","InternScience\u002FResearchClawBench",{"url":1224,"title":1225,"kind":1151},"https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FInternScience\u002FResearchClawBench?eval_result=zai-org\u002FGLM-5.2&leaderboard_task_id=overall","leaderboard",{"url":1227,"title":1227,"kind":1151},"https:\u002F\u002Fhuggingface.co\u002Fdatasets\u002FInternScience\u002FResearchClawBench?eval_result=zai-org%2FGLM-5.2&leaderboard_task_id=overall",{"url":1201,"title":1201,"kind":1151},[1230],{"title":1182,"url":1182,"fetched_at":1198},[],[1000,1015,1016,1015,1016,1015,1016,1017,1005,1006,1007,1028,1029,1030,1031,1032,1033,1049,1050,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1073,1090,1091,1092,1093,1094,1124,1125,1126,1127,1128,1129,1130,1124,1145],"yes",[],"unknown","model","Aixploria: 4.5\u002F5",[1239,1240,1241,1242,1243,1244,1245,1246,1247,1248,1249,1250,1251,1252,1253,1254,1255],"1M-token context window","Mixture-of-Experts (MoE) architecture","Advanced coding with flexible effort levels","IndexShare sparse-attention architecture","Support for long-horizon tasks","Reasoning and contextual understanding","Local deployment support via SGLang, vLLM, Transformers, KTransformers, Unsloth","API access via Z.ai API Platform","context window of up to 1 million tokens","multi-step reasoning capabilities","code generation and analysis","support for agent-based workflows","tool calling","structured output","support for reasoning effort adjustment","available via Hugging Face and Ollama","supports deployment with multiple frameworks","GLM-5.2 is available for free via the chat.z.ai interface and its weights can be downloaded under the MIT license.",[1258,1259,1260,1261,1262,1263,1264,1265,1266,1267],"Claude Code","Cline","OpenCode","Clawdbot\u002FOpenClaw","SGLang","vLLM","Transformers","KTransformers","Unsloth","Ascend NPU",[1269,1270,1271,1272],"English","Chinese","en","zh","MIT",[1275,1276,1277,1278,1279,1280,1281],"Text-only input and output","Very large model size makes local running impractical on a personal GPU","Coding Plan quotas are tighter during peak hours","API calls outside the Coding Plan are not available for subscribers using supported tools","Input and output limited to text only","Weights too large for personal GPUs","Quotas on Coding Plan may be restrictive during peak hours",[853,1283,1284],"GLM-5.2-FP8","GLM-5.2-W4AFP8","Nie wykazano",[1287,1292,1294,1296,1298,1305,1311],{"plan_name":1288,"access_model":1235,"price":1289,"currency":1290,"billing_period":1291,"included_usage":1235,"trial_days":1235,"card_required":1235,"source_evidence":1235},"GLM Coding Plan Lite","from $18\u002Fmonth","USD","monthly",{"plan_name":1293,"access_model":1235,"price":1235,"currency":1290,"billing_period":1291,"included_usage":1235,"trial_days":1235,"card_required":1235,"source_evidence":1235},"GLM Coding Plan Pro",{"plan_name":1295,"access_model":1235,"price":1235,"currency":1290,"billing_period":1291,"included_usage":1235,"trial_days":1235,"card_required":1235,"source_evidence":1235},"GLM Coding Plan Max",{"plan_name":1297,"access_model":1235,"price":1235,"currency":1290,"billing_period":1291,"included_usage":1235,"trial_days":1235,"card_required":1235,"source_evidence":1235},"GLM Coding Plan Team",{"plan_name":1299,"access_model":1300,"price":1301,"currency":1290,"billing_period":1300,"included_usage":1302,"trial_days":1235,"card_required":1303,"source_evidence":1304},"Free Chat Access","free","0","chat.z.ai: questions, trials, small sites","no","chat.z.ai: Questions, essais, petits sites Sans frais",{"plan_name":1306,"access_model":1307,"price":1308,"currency":1290,"billing_period":1291,"included_usage":1309,"trial_days":1235,"card_required":1233,"source_evidence":1310},"GLM Coding Plan","subscription","18","GLM Coding Plan: agents for code","GLM Coding Plan Agents de code au forfait mensuel Autour de 18 $\u002Fmois en entrée de gamme",{"plan_name":1312,"access_model":1313,"price":1314,"currency":1290,"billing_period":1315,"included_usage":1316,"trial_days":1235,"card_required":1233,"source_evidence":1317},"API Usage","pay_per_use","1.40","per million tokens","API for applications and agents in production","API Z.ai Applications et agents en production 1,40 $ en entrée \u002F 4,40 $ en sortie par million de tokens, 0,26 $ en cache","Dostęp do chat.z.ai jest bezpłatny; GLM Coding Plan startuje od ok. 18 USD\u002Fmiesiąc; oficjalne API Z.ai jest rozliczane tokenowo; w źródłach nie podano pełnych cen planów Pro\u002FMax\u002FTeam ani szczegółów limitów dla wszystkich planów.","2026-06-17",[],"GLM-5.2 to flagowy model Z.ai do zadań długiego horyzontu, kodowania i agentów. Oficjalny model card na Hugging Face potwierdza 1M-token context, licencję MIT, wsparcie dla lokalnego wdrożenia oraz wykorzystanie w API Z.ai. Katalog Aixploria opisuje go jako open-source model do code i long tasks oraz podaje ocenę 4.5\u002F5, ale nie dostarcza precyzyjnych danych o prywatności, regionie danych ani zgodności GDPR.","Open-source, duży model LLM od Z.ai do kodowania i zadań długiego horyzontu, z oknem kontekstu 1M tokenów i licencją MIT.",[1324,1325,1326,1327,1267,914,1328,1329],"Web","API","Local deployment","Linux","Ollama","Inference Endpoints",[1331,1332,1333,1334,1335,1336,1337,1338],"AI developers","software engineers","agent builders","teams working on long-context coding","researchers","Development teams","Code agents","Long-form task automation","Free access to chat.z.ai for testing; API and Coding Plan require payment.",[1341,1342,1343,1344,1345,1346,1347,1348,1349,1350,1351],"Repo-wide code refactoring","Automated research","Multi-file debugging","Long-horizon software engineering","Mini-game and prototype development","Tool-use agents","RAG systems","Refactoring entire code repositories","Automated research and test benches","Debugging across dozens of files","Creating complete prototypes or mini-games from a single prompt","Z.ai","published","2026-08-12T18:20:23.596Z",[1356,1357,1358,1359],"Claude Opus","GPT-5.5","GPT-4.5","Qwen3-235B",{"checked_at":1354,"ready":975,"failed":1361},[],"Nieznana kategoria: infrastruktura-api_modele (auto-fix)","```markdown\n# **GLM-5.2 – zaawansowany open-source’owy model AI do kodowania i zadań długoterminowych**\n\n**GLM-5.2** to flagowy, otwartoźródłowy model językowy (LLM) opracowany przez **Z.ai**, zaprojektowany z myślą o programistach, badaczach i zespołach pracujących nad złożonymi projektami wymagającymi **długiego kontekstu** (do **1 mln tokenów**) oraz precyzyjnego przetwarzania kodu. Dzięki architekturze **Mixture-of-Experts (MoE)** i innowacyjnej uwadze rozproszonej (**IndexShare**), narzędzie wyróżnia się wydajnością w zadaniach takich jak refaktoryzacja repozytoriów, automatyzacja badań czy debugowanie wieloplikowych systemów. Idealne dla developerów i naukowców poszukujących **elastycznego, skalowalnego i darmowego** rozwiązania AI.\n\n---\n\n## **Najważniejsze funkcje**\n\n1. **Okno kontekstu 1 mln tokenów**\n   - Przetwarza **ekstremalnie długie dokumenty**, całe bazy kodu lub złożone zestawy danych bez utraty spójności. Przydatne przy analizie dużych projektów (np. refaktoryzacja monolitów) lub generowaniu spójnych raportów na podstawie rozproszonych źródeł.\n\n2. **Architektura Mixture-of-Experts (MoE)**\n   - Model wykorzystuje **specjalizowane podsieci** aktywowane dynamicznie w zależności od zadania, co zwiększa efektywność obliczeniową i jakość odpowiedzi w wąskich dziedzinach (np. optymalizacja kodu w Pythonie vs. analizie danych w R).\n\n3. **Zaawansowane funkcje koderskie z elastycznym poziomem wysiłku**\n   - Użytkownik może **dostosować \"poziom zaangażowania\" modelu** – od szybkich sugestii po głęboką analizę struktury kodu. Przydatne przy debugowaniu, generowaniu testów jednostkowych lub automatycznym dokumentowaniu projektów.\n\n4. **IndexShare: rozproszona uwaga dla zadań długoterminowych**\n   - Technologia **sparse-attention** redukuje zużycie pamięci przy przetwarzaniu długich sekwencji, umożliwiając pracę na **wielu plikach jednocześnie** (np. śledzenie zależności między modułami w dużym repozytorium).\n\n5. **Licencja MIT i pełna otwartość**\n   - Model jest **dostępny do pobrania** (wagi, kod źródłowy), co pozwala na **lokalne wdrożenie**, modyfikacje i integrację z własnymi systemami – bez ograniczeń komercyjnych.\n\n---\n\n## **Dla kogo jest GLM-5.2?**\n\n- **Programiści i inżynierowie oprogramowania**\n  Refaktoryzacja kodu, debugowanie wieloplikowych projektów, generowanie dokumentacji technicznej lub automatyczne pisanie testów.\n- **Zespoły DevOps\u002FSRE**\n  Analiza logów systemowych, optymalizacja skryptów infrastrukturalnych (np. Terraform, Ansible) lub automatyzacja zadań CI\u002FCD.\n- **Badacze i naukowcy**\n  Przetwarzanie dużych zbiorów danych, automatyzacja przeglądów literatury (np. generowanie podsumowań artykułów naukowych) lub symulowanie eksperymentów.\n- **Firmy technologiczne**\n  Integracja z wewnętrznymi narzędziami (np. jako backend dla chatbotów wsparcia technicznego) lub przyspieszanie onboardingu nowych developerów.\n- **Hobbystyczne projekty AI**\n  Eksperymenty z fine-tuningiem modelu na własnych danych (dzięki licencji MIT).\n\n---\n\n## **Cena**\n\nGLM-5.2 oferuje **kilka modeli dostępu**:\n\n| **Opcja**               | **Koszt**                          | **Uwagi**                                                                 |\n|--------------------------|------------------------------------|---------------------------------------------------------------------------|\n| **Darmowy dostęp**       | 0 USD                              | Czaty poprzez [chat.z.ai](https:\u002F\u002Fchat.z.ai), pobranie wag modelu (MIT). |\n| **GLM Coding Plan**      | od **~18 USD\u002Fmiesiąc**             | Priorytetowy dostęp do funkcji koderskich, większe limity tokenów.       |\n| **Oficjalne API Z.ai**   | Rozliczenie **per token**          | Cena zależy od zużycia; brak publicznych stawek (trzeba skontaktować się z Z.ai). |\n| **Plany Pro\u002FMax\u002FTeam**   | **Brak publicznych danych**        | Szczegóły cen i limitów nie są udostępnione w źródłach.                   |\n\n---\n## **Zalety i wady**\n\n### ✅ **Zalety**\n1. **Niespotykany kontekst (1M tokenów)**\n   - Jedno z najdłuższych okien kontekstowych na rynku, umożliwiające pracę na **całych repozytoriach kodu** bez podziału na fragmenty.\n\n2. **Pełna otwartość i elastyczność**\n   - Licencja MIT pozwala na **lokalne uruchomienie**, modyfikacje i komercyjne użycie bez ograniczeń – idealne dla firm unikających vendor lock-in.\n\n3. **Specjalizacja w zadaniach developerskich**\n   - Funkcje takie jak **elastyczny poziom wysiłku** czy wsparcie dla zadań długoterminowych (np. śledzenie zmian w wielu plikach) wyróżniają go spośród modeli ogólnego przeznaczenia.\n\n### ❌ **Wady**\n1. **Brak transparentnych cen dla planów premium**\n   - Z.ai nie publikuje szczegółów dotyczących limitów ani kosztów planów Pro\u002FMax\u002FTeam, co utrudnia ocenę opłacalności dla firm.\n\n2. **Wymagania sprzętowe**\n   - Uruchomienie lokalne (zwłaszcza z pełnym kontekstem 1M tokenów) może wymagać **potężnej infrastruktury** (np. GPU z dużą pamięcią VRAM).\n\n",{"source_page_url":852,"final_url":852,"fetched_at":970},{"alternatives_count":184,"active_deals":184},[],[],{"data":1369,"_meta":1671,"alternatives":1672,"deals":1673},{"source":850,"source_key":851,"source_page_url":1370,"name":1371,"slug":1372,"description":1373,"title":1374,"category":857,"access_model":858,"pricing_evidence":1375,"capability_evidence":1379,"headings":1383,"breadcrumbs":1384,"pros":1385,"cons":1396,"external_links":1400,"affiliate_links":1437,"official_link_candidates":1438,"text_excerpt":1449,"fetched_at":1450,"final_url":1370,"http_status":971,"content_type":972,"content_hash":1451,"cache_file":1452,"published":975,"verification_status":976,"official_url":1453,"official_evidence":1454,"api_available":1565,"blog_titles":1566,"data_region":1235,"data_training":1569,"entity_type":1236,"external_rating":1570,"features":1571,"free_summary":1233,"gdpr":1594,"integrations":1595,"languages":1600,"license":1273,"limitations":1601,"model_names":1605,"polish_support":1285,"pricing_plans":1608,"pricing_summary":1621,"privacy_summary":1622,"release_date":1623,"research_links":1624,"research_summary":1627,"security_summary":1628,"self_hosted":1565,"short_description":1629,"starting_price":1616,"supported_platforms":1630,"target_users":1635,"trial_summary":1621,"use_cases":1646,"vendor":1659,"status":1353,"last_verified_at":1354,"alternatives":1660,"quality":1666,"category_v3":140,"category_v3_main":129,"category_v3_label":141,"category_v3_reason":1668,"deep_card":1669,"deep_card_generated":975,"category_v3_main_label":130,"_category_fixed":975,"evidence":1670},"https:\u002F\u002Fwww.aixploria.com\u002Fglm-image-z-ai\u002F","GLM-Image","glm-image-z-ai","GLM-Image (Z.ai) est un générateur d'images IA gratuit et open source qui est excellent dans l'affichage de texte. Ce modèle prend aussi en charge l'édition d'images, le transfert de style, la préservation de l'identité et la cohérence multi-sujets.","GLM-Image : Avis, Prix, Info & 57 IA Alternatives | 2026 | AIxploria",[1376,1377,1378],"Freemium Seedream 5.0","387 Visiter Freemium ChatGPT Images 2.0","78 Visiter Freemium Wan2.7 Image",[1380,873,1381,1382],"« Un générateur d'images IA open source avec 16 milliards de paramètres qui est excellent dans l'affichage de texte. Ce modèle prend aussi en charge l'édition d'images, le transfert de style, la préservation de l'identité et la cohérence multi-sujets »","« Générez et éditez vos images directement dans ChatGPT. Ce modèle est jusqu'à 4× plus rapide et préserve les détails essentiels comme les traits du visage. Bénéficiez d'un meilleur suivi des instructions, d'une cohérence visuelle accrue et d'une interface dédiée de type studio de création »","« Générez et éditez des images ultra détaillées avec un texte parfaitement lisible, des mises en page complexes (affiches, infographies, UI) et un rendu photo réaliste. Accessible via ChatGPT ou l’API gpt-image-2»",[],[],[1386,1387,1388,1389,1390,1391,1392,1393,1394,1395],"Bardzo mocne renderowanie tekstu w obrazie.","Dobre wyniki w scenariuszach knowledge-intensive i złożonych layoutach.","Obsługuje zarówno text-to-image, jak i image-to-image.","Model open-source i możliwy do self-hostingu.","Dostępne przykłady uruchomienia lokalnego i przez API.","Strong text rendering in images","Supports complex layouts and information-dense content","Open source with MIT license","Supports multiple image-to-image tasks","High-fidelity and fine-grained detail generation",[1397,1398,1399],"Wymaga sporej mocy obliczeniowej przy uruchomieniu lokalnym; dokumentacja wspomina o relatywnie wysokim koszcie inferencji.","Obrazy muszą mieć rozdzielczość podzielną przez 32; inaczej wystąpi błąd.","Zalecane jest używanie GLM-4.7 do ulepszania promptów, co sugeruje, że jakość może zależeć od dodatkowego kroku prompt engineeringu.",[1401,1403,1405,1407,1409,1411,1413,1415,1417,1420,1421,1422,1423,1424,1425,1426,1427,1428,1429,1430,1431,1432,1433,1434,1435,1436],{"url":1402,"text":899},"https:\u002F\u002Ftwitter.com\u002Fintent\u002Ftweet?url=https%3A%2F%2Fwww.aixploria.com%2Fglm-image-z-ai%2F&text=GLM-Image%3A+Un+g%C3%A9n%C3%A9rateur+d%27images+IA+open+source+avec+16+milliards+de+param%C3%A8tres+qui+est+excellent+dans+l%27affichage+de+texte.+Ce+mod%C3%A8le+prend+aussi+en+charge+l%27%C3%A9dition+d%27images%2C+le+transfert+de+style%2C+la+pr%C3%A9servation+de+l%27identit%C3%A9+et+la+coh%C3%A9rence+multi-sujets",{"url":1404,"text":899},"https:\u002F\u002Fwww.facebook.com\u002Fsharer\u002Fsharer.php?u=https%3A%2F%2Fwww.aixploria.com%2Fglm-image-z-ai%2F",{"url":1406,"text":899},"https:\u002F\u002Fwww.linkedin.com\u002FshareArticle?url=https%3A%2F%2Fwww.aixploria.com%2Fglm-image-z-ai%2F&title=GLM-Image",{"url":1408,"text":899},"https:\u002F\u002Ft.me\u002Fshare\u002Furl?url=https%3A%2F%2Fwww.aixploria.com%2Fglm-image-z-ai%2F&text=Un+g%C3%A9n%C3%A9rateur+d%27images+IA+open+source+avec+16+milliards+de+param%C3%A8tres+qui+est+excellent+dans+l%27affichage+de+texte.+Ce+mod%C3%A8le+prend+aussi+en+charge+l%27%C3%A9dition+d%27images%2C+le+transfert+de+style%2C+la+pr%C3%A9servation+de+l%27identit%C3%A9+et+la+coh%C3%A9rence+multi-sujets",{"url":1410,"text":908},"https:\u002F\u002Fchatgpt.com\u002F?q=Donne-moi%20une%20pr%C3%A9sentation%20claire%20et%20concise%20de%20GLM-Image.%20Explique%20%C3%A0%20quoi%20sert%20cet%20outil%20IA%2C%20%C3%A0%20qui%20il%20s%E2%80%99adresse%2C%20ses%20principales%20fonctionnalit%C3%A9s%2C%20son%20mod%C3%A8le%20tarifaire%2C%20ses%20points%20forts%2C%20ses%20limites%20et%20ses%20alternatives%20possibles.",{"url":1412,"text":911},"https:\u002F\u002Fwww.perplexity.ai\u002F?q=Recherche%20des%20informations%20%C3%A0%20jour%20sur%20GLM-Image.%20R%C3%A9sume%20%C3%A0%20quoi%20sert%20cet%20outil%2C%20ses%20prix%20actuels%20si%20disponibles%2C%20ses%20principales%20fonctionnalit%C3%A9s%2C%20ses%20points%20forts%2C%20ses%20limites%20et%20ses%20alternatives%20notables.",{"url":1414,"text":914},"https:\u002F\u002Fhuggingface.co\u002Fzai-org\u002FGLM-Image",{"url":1416,"text":917},"https:\u002F\u002Fgithub.com\u002Fzai-org\u002FGLM-Image",{"url":1418,"text":1419},"https:\u002F\u002Fdocs.z.ai\u002Fguides\u002Fimage\u002Fglm-image","Guide",{"url":924,"text":925},{"url":924,"text":927},{"url":924,"text":929},{"url":931,"text":932},{"url":931,"text":934},{"url":931,"text":929},{"url":937,"text":938},{"url":937,"text":873},{"url":937,"text":929},{"url":942,"text":943},{"url":942,"text":945},{"url":942,"text":929},{"url":948,"text":899},{"url":950,"text":899},{"url":952,"text":899},{"url":954,"text":955},{"url":954,"text":955},[],[1439,1440,1441,1442,1443,1444,1445,1446,1447,1448],{"url":1408,"text":899},{"url":1410,"text":908},{"url":1412,"text":911},{"url":1414,"text":914},{"url":1416,"text":917},{"url":1418,"text":1419},{"url":924,"text":925},{"url":924,"text":927},{"url":924,"text":929},{"url":931,"text":932},"GLM-Image : Avis, Prix, Info & 57 IA Alternatives | 2026 | AIxploria \nTop 10 Catégories IA Tutoriels IA + Plus Nouveaux Outils IA Outils IA Gratuits Actualités IA 2026 Bonus & Extras Soumettre une IA \nEnglish Français Español Deutsch Italiano Português + More × 中文 日本語 العربية हिन्दी Русский 한국어 Türkçe Tiếng Việt فارسی বাংলা Indonesia ไทย Nederlands Polski اردو Kiswahili Українська Ελληνικά עברית Svenska Norsk Dansk Suomi Čeština Magyar Melayu Tagalog 中文 (香港) தமிழ் \nS'inscrire \nTop 10 Catégories IA Tutoriels IA + Plus Nouveaux Outils IA Outils IA Gratuits Actualités IA 2026 Bonus & Extras Soumettre une IA \nS'inscrire \nAccueil Générateurs Images GLM-Image \nGLM-Image\n#64 dans Générateurs Images \n4.6\u002F5 \nVisiter ce site \n« Un générateur d'images IA open source avec 16 milliards de paramètres qui est excellent dans l'affichage de texte. Ce modèle prend aussi en charge l'édition d'images, le transfert de style, la préservation de l'identité et la cohérence multi-sujets »\nEn savoir plus : \nChatGPT \nPerplexity \nGratuit \n6093 \nLiens utiles \nSite Officiel \nHugging Face \nGitHub \nGuide \n#Générateurs Images \n#IA Récentes \n#Projets Github \n61 ★ Outils IA de Prestige ★ \n★ Mis en avant \nEmergent AI \n« Transformez vos idées en applications complètes grâce à des agents IA qui codent, testent et déploient pour vous. Créez sites web (full-stack), apps IA, jeux et outils automatisés simplement en décrivant votre projet »\nVisiter \n★ Mis en avant \nClickUp \n« Gérez vos projets plus vite avec une plateforme qui regroupe équipes, tâches et documents en un seul espace. Son IA intégrée résume vos notes, rédige pour vous et organise automatiquement vos priorités »\nVisiter \n★ Mis en avant \nThumbnailCreator.com \n« Créez rapidement des miniatures accrocheuses pour vos vidéos sur YouTube, Twitch et Facebook. Attirez plus de vues et améliorez facilement le taux de clics sur vos clip vidéos »\nVisiter \n★ Mis en avant \nVmake Video Enhancer \n« Améliorez vos vidéos floues et de mauvaise qualité pour les transformer en HD et 4K grâce à l'IA. Vmake accentue les détails de vos vidéos, améliore leur netteté et leur donne un aspect plus professionnel en quelques secondes »\nVisiter \nIA Alternatives à \nGLM-Image \nFreemium Seedream 5.0 \n4.6\u002F5 \n« Générez ou éditez des images en haute résolution avec un modèle capable de garder les personnages et les style cohérents sur plusieurs visuels. Pratique pour les storyboards, BD, campagnes ou séries d’illustrations »\n79 Visiter Gratuit Ideogram 3.0 \n4.6\u002F5 \n« Un générateur d'images capable de créer des illustrations impressionnantes et d'y intégrer du texte. Logo, storyboard, etc. »\n187 Visiter Essai-Gratuit MidJourney V8 \n4.4\u002F5 \n« Générateur d'images surpuissant se basant sur une description par texte (Discord + Web) »\n844 Visiter Gratuit GPT Image 1.5 \n4.5\u002F5 \n« Générez et éditez vos images directement dans ChatGPT. Ce modèle est jusqu'à 4× plus rapide et préserve les détails essentiels comme les traits du visage. Bénéficiez d'un meilleur suivi des instructions, d'une cohérence visuelle accrue et d'une interface dédiée de type studio de création »\n387 Visiter Freemium ChatGPT Images 2.0 \n4.6\u002F5 \n« Générez et éditez des images ultra détaillées avec un texte parfaitement lisible, des mises en page complexes (affiches, infographies, UI) et un rendu photo réaliste. Accessible via ChatGPT ou l’API gpt-image-2»\n298 Visiter Gratuit Muse Image by Meta \n4.7\u002F5 \n« Créez des images de haute qualité directement depuis les applications de réseaux sociaux. Cette IA comprend les prompts complexes, peut combiner plusieurs photos et générer de nouveaux effets créatifs pour vos Stories et vos campagnes publicitaires »\n89 Visiter Gratuit Reve Image \n4.4\u002F5 \n« Un générateur IA d'images conçu pour exceller dans trois domaines clés : le respect précis des consignes, la qualité esthétique des visuels et la typographie parfaite. Créez des designs graphiques avec du texte impeccable »\n78 Visiter Freemium Wan2.7 Image \n4.7\u002F5 \n« Générez des images avec un contrôle précis des couleurs via codes hex et jusqu'à 9 photos de référence. Ce modèle d'Alibaba garantit des visages consistants et du texte lisible directement dans vos images»\n98 Visiter \n+ Voir plus d'IA \nCopier le code \nL'intelligence artificielle pour tous\nRessources Tutoriels, astuces et blog Agenda des conférences sur l'IA Glossaire des IA Emplois dans l'IA Newsletter Services pratiques Chaînes YouTube sur l'IA Top 100 IA Liste GPTs Outils IA Hubspot Meilleurs Agents IA Entreprise Ajouter un outil IA Annoncer Mettre à jour votre site Sponsorship ★ Media Kit Qui sommes-nous ? Contactez-nous \n© 2026 AIxploria. Tous droits réservés \nPolitique des Cookies \nConditions d'utilisations Mentions Légales Gérer le consentement aux cookies \nAfin de fournir une expérience optimale, Aixploria utilise des technologies telles que les cookies pour stocker et\u002Fou accéder aux informations relatives à l'appareil. 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En l’absence d’une assignation à comparaître, d’une conformité volontaire de la part de votre fournisseur d’accès à internet ou d’enregistrements supplémentaires provenant d’une tierce partie, les informations stockées ou extraites à cette seule fin ne peuvent généralement pas être utilisées pour vous identifier. \nMarketing \nMarketing \nLe stockage ou l’accès technique est nécessaire pour créer des profils d’internautes afin d’envoyer des publicités, ou pour suivre l’internaute sur un site web ou sur plusieurs sites web ayant des finalités marketing similaires. \nGérer les options \nGérer les services \nGérer {vendor_count} fournisseurs \nEn savoir plus sur ces finalités \nAccepter \nTout refuser \nPersonnaliser \nSauvegarder les préférences \nPersonnaliser \n{title} \n{title} \n{title} Gérer le consentement Gérer le consentement","2026-08-09T00:30:59.639Z","b7f53d1e58d9621583c3158c17f5ba228a0e34e3c006f0d80fbba5127b940729","data\u002Fcatalog\u002Fcache\u002Faixploria\u002F26dd4c2945fddefb7d514ff67a86d4d79296f461a244d61b73ecf44ed08633ed.html","https:\u002F\u002Fz.ai\u002Fblog\u002Fglm-image",{"url":1414,"final_url":1414,"status":971,"fetched_at":1455,"content_hash":1456,"title":1457,"description":981,"headings":1458,"pricing_evidence":1474,"privacy_evidence":1480,"capability_evidence":1484,"text_excerpt":1486,"linked_pages":1487,"research_links":1557,"blog_titles":1560,"use_case_evidence":1563,"feature_evidence":1564},"2026-08-09T01:06:04.849Z","ac745bcfac09c571f94b1501259590a9dd80ba1409aed5ef582364592896c793","zai-org\u002FGLM-Image · Hugging Face",[1459,1460,1371,985,1461,1462,1463,1464,1465,1466,1467,1468,1469,1470,1471,1472,1473],"zai-org \u002F GLM-Image like 1.09k Follow Z.ai 18.3k","Instructions to use zai-org\u002FGLM-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.","Showcase","T2I with dense text and knowledge","I2I","Quick Start","transformers + diffusers Pipeline","SGLang Pipeline","Note","Model Performance","Text Rendering","Text-to-Image","License","Model tree for zai-org\u002FGLM-Image","Spaces using zai-org\u002FGLM-Image 51",[1475,1476,1477,1478,1479],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up zai-org \u002F GLM-Image like 1.09k Follow Z.ai 18.3k","GLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture. In general image generation quality, GLM‑Image aligns with mainstream latent diffusion approaches, but it shows significant advantages in text-rendering and knowledge‑intensive generation scenarios. It performs especially well in tasks requiring precise semantic understanding and complex information expression, while maintaining strong capabilities in high‑fidelity and fine‑grained detail generation. In addition to text‑to‑image generation, GLM‑Image also supports a rich set of image‑to‑image tasks including image editing, style transfer, identity‑preserving generation, and multi‑subject consistency.","Text-to-image: generates high-detail images from textual descriptions, with particularly strong performance in information-dense scenarios.","Because inference optimizations for this architecture are currently limited, the runtime cost is still relatively high. You can set enable_model_cpu_offload=True to run it with ~23GB of GPU memory, at the cost of slower inference.","💻 multimodalart\u002FGLM-Image 🏆 fantos\u002FGLM-IMAGE-PRO 📈 akhaliq\u002FGLM-Image 👀 ThotAboutU\u002Fzai-org-GLM-Image 🌍 OYesiuso\u002Fzai-org-GLM-Image 💻 00Boobs00\u002FGLM-Image 📊 Hurikiwuw\u002Fzai-org-GLM-Image 🐨 myy555\u002Fzai-org-GLM-Image + 46 Spaces + 43 Spaces System theme Company TOS Privacy About Careers Website Models Datasets Spaces Pricing Docs",[1481,1482,1483,1483,1479],"pipe = DiffusionPipeline.from_pretrained(\"zai-org\u002FGLM-Image\", dtype=torch.bfloat16, device_map=\"cuda\")","Post-training with decoupled reinforcement learning: the model introduces a fine-grained, modular feedback strategy using the GRPO algorithm, substantially enhancing both semantic understanding and visual detail quality.","pipe = GlmImagePipeline.from_pretrained( \"zai-org\u002FGLM-Image\" , torch_dtype=torch.bfloat16, device_map= \"cuda\" )",[1475,1485],"📍 Use GLM-Image's API","zai-org\u002FGLM-Image · Hugging Face \nHugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up zai-org \u002F GLM-Image like 1.09k Follow Z.ai 18.3k \nText-to-Image Diffusers Safetensors Chinese English GlmImagePipeline License: mit Model card Files Files and versions xet Community 25 Deploy Copy to bucket new Use this model Instructions to use zai-org\u002FGLM-Image with libraries, inference providers, notebooks, and local apps. Follow these links to get started.\nLibraries Diffusers How to use zai-org\u002FGLM-Image with Diffusers:\npip install -U diffusers transformers accelerate import torch\nfrom diffusers import DiffusionPipeline\n# switch to \"mps\" for apple devices\npipe = DiffusionPipeline.from_pretrained(\"zai-org\u002FGLM-Image\", dtype=torch.bfloat16, device_map=\"cuda\")\nprompt = \"Astronaut in a jungle, cold color palette, muted colors, detailed, 8k\"\nimage = pipe(prompt).images[0] Inference Inference Providers Notebooks Google Colab Kaggle Local Apps Settings Draw Things DiffusionBee GLM-Image Introduction Showcase T2I with dense text and knowledge I2I Quick Start transformers + diffusers Pipeline SGLang Pipeline Note Model Performance Text Rendering Text-to-Image License \nGLM-Image\n👋 Join our WeChat and Discord community\n📖 Check out GLM-Image's Technical Blog and Github \n📍 Use GLM-Image's API \nIntroduction\nGLM-Image is an image generation model adopts a hybrid autoregressive + diffusion decoder architecture. In general image generation quality, GLM‑Image aligns with mainstream latent diffusion approaches, but it shows significant advantages in text-rendering and knowledge‑intensive generation scenarios. It performs especially well in tasks requiring precise semantic understanding and complex information expression, while maintaining strong capabilities in high‑fidelity and fine‑grained detail generation. In addition to text‑to‑image generation, GLM‑Image also supports a rich set of image‑to‑image tasks including image editing, style transfer, identity‑preserving generation, and multi‑subject consistency.\nModel architecture: a hybrid autoregressive + diffusion decoder design.\nAutoregressive generator: a 9B-parameter model initialized from GLM-4-9B-0414 , with an expanded vocabulary to incorporate visual tokens. The model first generates a compact encoding of approximately 256 tokens, then expands to 1K–4K tokens, corresponding to 1K–2K high-resolution image outputs. \nDiffusion Decoder: a 7B-parameter decoder based on a single-stream DiT architecture for latent-space image decoding. It is equipped with a Glyph Encoder text module, significantly improving accurate text rendering within images. \nPost-training with decoupled reinforcement learning: the model introduces a fine-grained, modular feedback strategy using the GRPO algorithm, substantially enhancing both semantic understanding and visual detail quality.\nAutoregressive module: provides low-frequency feedback signals focused on aesthetics and semantic alignment, improving instruction following and artistic expressiveness. \nDecoder module: delivers high-frequency feedback targeting detail fidelity and text accuracy, resulting in highly realistic textures as well as more precise text rendering. \nGLM-Image supports both text-to-image and image-to-image generation within a single model.\nText-to-image: generates high-detail images from textual descriptions, with particularly strong performance in information-dense scenarios. \nImage-to-image: supports a wide range of tasks, including image editing, style transfer, multi-subject consistency, and identity-preserving generation for people and objects. \nYou can find the full GLM-Image Model implementation in the transformers and diffusers libraries here.\nShowcase\nT2I with dense text and knowledge\nI2I\nQuick Start\ntransformers + diffusers Pipeline\nInstall transformers and diffusers from source:\npip install git+https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftransformers.git\npip install git+https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdiffusers.git\nText to Image Generation \nimport torch\nfrom diffusers.pipelines.glm_image import GlmImagePipeline\npipe = GlmImagePipeline.from_pretrained( \"zai-org\u002FGLM-Image\" , torch_dtype=torch.bfloat16, device_map= \"cuda\" )\nprompt = \"A beautifully designed modern food magazine style dessert recipe illustration, themed around a raspberry mousse cake. The overall layout is clean and bright, divided into four main areas: the top left features a bold black title 'Raspberry Mousse Cake Recipe Guide', with a soft-lit close-up photo of the finished cake on the right, showcasing a light pink cake adorned with fresh raspberries and mint leaves; the bottom left contains an ingredient list section, titled 'Ingredients' in a simple font, listing 'Flour 150g', 'Eggs 3', 'Sugar 120g', 'Raspberry puree 200g', 'Gelatin sheets 10g', 'Whipping cream 300ml', and 'Fresh raspberries', each accompanied by minimalist line icons (like a flour bag, eggs, sugar jar, etc.); the bottom right displays four equally sized step boxes, each containing high-definition macro photos and corresponding instructions, arranged from top to bottom as follows: Step 1 shows a whisk whipping white foam (with the instruction 'Whip egg whites to stiff peaks'), Step 2 shows a red-and-white mixture being folded with a spatula (with the instruction 'Gently fold in the puree and batter'), Step 3 shows pink liquid being poured into a round mold (with the instruction 'Pour into mold and chill for 4 hours'), Step 4 shows the finished cake decorated with raspberries and mint leaves (with the instruction 'Decorate with raspberries and mint'); a light brown information bar runs along the bottom edge, with icons on the left representing 'Preparation time: 30 minutes', 'Cooking time: 20 minutes', and 'Servings: 8'. The overall color scheme is dominated by creamy white and light pink, with a subtle paper texture in the background, featuring compact and orderly text and image layout with clear information hierarchy.\" \nimage = pipe(\nprompt=prompt,\nheight= 32 * 32 ,\nwidth= 36 * 32 ,\nnum_inference_steps= 50 ,\nguidance_scale= 1.5 ,\ngenerator=torch.Generator(device= \"cuda\" ).manual_seed( 42 ),\n).images[ 0 ]\nimage.save( \"output_t2i.png\" )\nImage to Image Generation \nimport torch\nfrom diffusers.pipelines.glm_image import GlmImagePipeline\nfrom PIL import Image\npipe = GlmImagePipeline.from_pretrained( \"zai-org\u002FGLM-Image\" , torch_dtype=torch.bfloat16, device_map= \"cuda\" )\nimage_path = \"cond.jpg\" \nprompt = \"Replace the background of the snow forest with an underground station featuring an automatic escalator.\" \nimage = Image. open (image_path).convert( \"RGB\" )\nimage = pipe(\nprompt=prompt,\nimage=[image], # can input multiple images for multi-image-to-image generation such as [image, image1] \nheight= 33 * 32 , # Must set height even it is same as input image \nwidth= 32 * 32 , # Must set width even it is same as input image \nnum_inference_steps= 50 ,\nguidance_scale= 1.5 ,\ngenerator=torch.Generator(device= \"cuda\" ).manual_seed( 42 ),\n).images[ 0 ]\nimage.save( \"output_i2i.png\" )\nSGLang Pipeline\nInstall transformers and diffusers from source:\npip install \"sglang[diffusion] @ git+https:\u002F\u002Fgithub.com\u002Fsgl-project\u002Fsglang.git#subdirectory=python\"\npip install git+https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftransformers.git\npip install git+https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdiffusers.git\nText to Image Generation \nsglang serve --model-path zai-org\u002FGLM-Image\ncurl http:\u002F\u002Flocalhost:30000\u002Fv1\u002Fimages\u002Fgenerations \\\n-H \"Content-Type: application\u002Fjson\" \\\n-d '{\n\"model\": \"zai-org\u002FGLM-Image\",\n\"prompt\": \"a beautiful girl with glasses.\",\n\"n\": 1,\n\"response_format\": \"b64_json\",\n\"size\": \"1024x1024\"\n}' | python3 -c \"import sys, json, base64; open('output_t2i.png', 'wb').write(base64.b64decode(json.load(sys.stdin)['data'][0]['b64_json']))\"\nImage to Image Generation \nsglang serve --model-path zai-org\u002FGLM-Image\ncurl -s -X POST \"http:\u002F\u002Flocalhost:30000\u002Fv1\u002Fimages\u002Fedits\" \\\n-F \"model=zai-org\u002FGLM-Image\" \\\n-F \"image=@cond.jpg\" \\\n-F \"prompt=Replace the background of the snow forest with an underground station featuring an automatic escalator.\" \\\n-F \"response_format=b64_json\" | python3 -c \"import sys, json, base64; open('output_i2i.png', 'wb').write(base64.b64decode(json.load(sys.stdin)['data'][0]['b64_json']))\"\nNote\nPlease ensure that all text intended to be rendered in the image is enclosed in quotation marks in the model input and We strongly recommend using GLM-4.7 to enhance prompts for higher image quality. Please check our github script for more details. \nThe AR model used in GLM‑Image is configured with do_sample=True , a temperature of 0.9 , and a topp of 0.75 by default. A higher temperature results in more diverse and rich outputs, but it can also lead to a certain decrease in output stability. \nThe target image resolution must be divisible by 32. Otherwise, it will throw an error. \nBecause inference optimizations for this architecture are currently limited, the runtime cost is still relatively high. You can set enable_model_cpu_offload=True to run it with ~23GB of GPU memory, at the cost of slower inference. \nvLLM-Omni and SGLang (with AR speedup) support is currently being integrated — stay tuned. For inference cost, you can check in our github. \nModel Performance\nText Rendering\nModel \nOpen Source \nCVTG-2K \nLongText-Bench \nWord Accuracy \nNED \nCLIPScore \nAVG \nEN \nZH \nSeedream 4.5 \n✗ \n0.8990 \n0.9483 \n0.8069 \n0.988 \n0.989 \n0.987 \nSeedream 4.0 \n✗ \n0.8451 \n0.9224 \n0.7975 \n0.924 \n0.921 \n0.926 \nNano Banana 2.0 \n✗ \n0.7788 \n0.8754 \n0.7372 \n0.965 \n0.981 \n0.949 \nGPT Image 1 [High] \n✗ \n0.8569 \n0.9478 \n0.7982 \n0.788 \n0.956 \n0.619 \nQwen-Image \n✓ \n0.8288 \n0.9116 \n0.8017 \n0.945 \n0.943 \n0.946 \nQwen-Image-2512 \n✓ \n0.8604 \n0.9290 \n0.7819 \n0.961 \n0.956 \n0.965 \nZ-Image \n✓ \n0.8671 \n0.9367 \n0.7969 \n0.936 \n0.935 \n0.936 \nZ-Image-Turbo \n✓ \n0.8585 \n0.9281 \n0.8048 \n0.922 \n0.917 \n0.926 \nGLM-Image \n✓ \n0.9116 \n0.9557 \n0.7877 \n0.966 \n0.952 \n0.979 \nText-to-Image\nModel \nOpen Source \nOneIG-Bench \nTIIF-Bench \nDPG-Bench \nEN \nZH \nshort \nlong \nSeedream 4.5 \n✗ \n0.576 \n0.551 \n90.49 \n88.52 \n88.63 \nSeedream 4.0 \n✗ \n0.576 \n0.553 \n90.45 \n88.08 \n88.54 \nNano Banana 2.0 \n✗ \n0.578 \n0.567 \n91.00 \n88.26 \n87.16 \nGPT Image 1 [High] \n✗ \n0.533 \n0.474 \n89.15 \n88.29 \n85.15 \nDALL-E 3 \n✗ \n- \n- \n74.96 \n70.81 \n83.50 \nQwen-Image \n✓ \n0.539 \n0.548 \n86.14 \n86.83 \n88.32 \nQwen-Image-2512 \n✓ \n0.530 \n0.515 \n83.24 \n84.93 \n87.20 \nZ-Image \n✓ \n0.546 \n0.535 \n80.20 \n83.01 \n88.14 \nZ-Image-Turbo \n✓ \n0.528 \n0.507 \n77.73 \n80.05 \n84.86 \nFLUX.1 [Dev] \n✓ \n0.434 \n- \n71.09 \n71.78 \n83.52 \nSD3 Medium \n✓ \n- \n- \n67.46 \n66.09 \n84.08 \nSD XL \n✓ \n0.316 \n- \n54.96 \n42.13 \n74.65 \nBAGEL \n✓ \n0.361 \n0.370 \n71.50 \n71.70 \n- \nJanus-Pro \n✓ \n0.267 \n0.240 \n66.50 \n65.01 \n84.19 \nShow-o2 \n✓ \n0.308 \n- \n59.72 \n58.86 \n- \nGLM-Image \n✓ \n0.528 \n0.511 \n81.01 \n81.02 \n84.78 \nLicense\nThe overall GLM-Image model is released under the MIT License.\nThis project incorporates the VQ tokenizer weights and VIT weights from X-Omni\u002FX-Omni-En , which are licensed under the Apache License, Version 2.0.\nThe VQ tokenizer and VIT weights remains subject to the original Apache-2.0 terms. Users should comply with the respective licenses when using this component.\nDownloads last month 8,181 \nSafetensors Model size 7B params Tensor type BF16 · Files info\nInference Providers NEW fal Text-to-Image Generate View Code Snippets Maximize Model tree for zai-org\u002FGLM-Image \nAdapters 33 models Finetunes 13 models Quantizations 1 model Spaces using zai-org\u002FGLM-Image 51 \n💻 multimodalart\u002FGLM-Image 🏆 fantos\u002FGLM-IMAGE-PRO 📈 akhaliq\u002FGLM-Image 👀 ThotAboutU\u002Fzai-org-GLM-Image 🌍 OYesiuso\u002Fzai-org-GLM-Image 💻 00Boobs00\u002FGLM-Image 📊 Hurikiwuw\u002Fzai-org-GLM-Image 🐨 myy555\u002Fzai-org-GLM-Image + 46 Spaces + 43 Spaces System theme Company TOS Privacy About Careers Website Models Datasets Spaces Pricing Docs",[1488,1494,1500,1506,1512,1518,1549,1553],{"url":1021,"kind":1022,"anchor":1023,"title":1024,"description":981,"headings":1489,"feature_evidence":1490,"use_case_evidence":1491,"fetched_at":1492,"status":971,"content_hash":1493},[1026],[1028,1029,1030,1031,1032,1033],[1028],"2026-08-09T01:06:01.438Z","8f5821e3625b5e2edff1ca73ecc08399dea13c38784b1d127cd53c0d8963c62f",{"url":1038,"kind":1022,"anchor":899,"title":1039,"description":981,"headings":1495,"feature_evidence":1496,"use_case_evidence":1497,"fetched_at":1498,"status":971,"content_hash":1499},[1041,1041,1042,1043,1044,1045,1046,1047],[1049,1050],[1049],"2026-08-09T01:06:01.977Z","0b385a4d0ab33f526e7a37f8c35a15e758ca8dcfba0a89963746cc3fd93f615a",{"url":1055,"kind":1022,"anchor":1056,"title":1057,"description":981,"headings":1501,"feature_evidence":1502,"use_case_evidence":1503,"fetched_at":1504,"status":971,"content_hash":1505},[1059,1059,1060,1061,1062,1063,1064,1065,1066,1067,1068,1069,1070,1071,1068,1069,1072,1073,1074,1075],[1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1073,1090,1091,1092,1093,1094],[1077,1084,1096,1086],"2026-08-09T01:06:02.391Z","518a73a650f188e42dcd58f23398492eb6d1a1f02ff1a0fcc7aa37b356a73e7d",{"url":1100,"kind":1022,"anchor":899,"title":1101,"description":981,"headings":1507,"feature_evidence":1508,"use_case_evidence":1509,"fetched_at":1510,"status":971,"content_hash":1511},[1103,1104,1105,1106,1107,1108,1109,1110,1111,1112,1113,1114,1115,1116,1117,1118,1119,1120,1121,1122],[1124,1125,1126,1127,1128,1129,1130],[1124],"2026-08-09T01:06:02.700Z","1e2b233bd01b0e5a9c75e98869ddbf382975a9d64426b820b473d131ddc43d30",{"url":1150,"kind":1151,"anchor":1152,"title":1153,"description":1154,"headings":1513,"feature_evidence":1514,"use_case_evidence":1515,"fetched_at":1516,"status":971,"content_hash":1517},[1152,1156,1157,1158,1159,1160,1161,1162,1163,1164,1165,1166,1167,1168,1169,1170,1171,1172,1173,1174],[1176,1177],[1176],"2026-08-09T01:06:03.187Z","7198c89071288b39f948ddc4cc19ef1e5d4fbb9bf5dcc42d17c0679b19d50efb",{"url":1519,"kind":1183,"anchor":1520,"title":1521,"description":981,"headings":1522,"feature_evidence":1543,"use_case_evidence":1546,"fetched_at":1547,"status":971,"content_hash":1548},"https:\u002F\u002Fhuggingface.co\u002Fblog","Blog","Hugging Face – Blog",[1523,1524,1525,1526,1527,1528,1529,1530,1531,1532,1533,1534,1535,1536,1537,1538,1539,1540,1541,1542],"Community Blog & Articles","The Fast Gemma Challenge: our verified-SOTA recipe, in full","Training a coding agent using the OpenCode harness in remote HF sandboxes with TRL and OpenEnv","Lattice: an 8 MB static retriever that embeds Wikipedia in 7 minutes","Model Genome: Fingerprinting Whether an LLM Was Trained From Scratch or Derived","mDenseOn with the mLateOn: Open Multilingual, Long-Context, and Code Retrieval Models","LFM2.5-Encoders for Fast Long-Context Inference on CPU","The OlmoEarth Platform: Geospatial inference at planetary scale","超越表层对齐：信念是通往深层对齐的新入口","Uncensor any LLM with abliteration","Code a simple RAG from scratch","Kimi K3 Model Overview: 2.8T Parameters, MXFP4 Quantization, and What the Open Weights Mean for the Community","Language Identification for 42 Indian Languages: Opening New Frontiers in Indic Speech AI","Rebuilding Among AIs from Six Log Files","FLUX 3 Model Overview: Multimodal Flow Models for Image, Video, Audio, and Action Prediction","Bekko Embedding: how small can a multilingual retrieval model be?","jina-reranker-v3.5: Faster Listwise Reranking with Hybrid Attention and Self-Distillation","Optimum Intel v2.1.0","KV Caching Explained: Optimizing Transformer Inference Efficiency","Using OCR models with llama.cpp",[1544,1545,1545],"Hugging Face Models Datasets Spaces Buckets new Docs Enterprise Pricing Website Tasks HuggingChat Collections Languages Organizations Community Blog Posts Daily Papers Hardware Learn Discord Forum GitHub Solutions Team & Enterprise Hugging Face PRO Enterprise Support Inference Providers Inference Endpoints Storage Buckets Log In Sign Up Community Blog & Articles","ResterChed • 22 days ago • 195 Language Identification for 42 Indian Languages: Opening New Frontiers in Indic Speech AI",[1544],"2026-08-09T01:06:03.549Z","4d8a7fb00e7d0f6a1a6799e85cff0bfb07f2022f6e5c7d0cd89fc4006c65748b",{"url":1182,"kind":1183,"title":1182,"description":899,"feature_evidence":1550,"use_case_evidence":1551,"fetched_at":1552,"status":971,"content_hash":1199},[1185,1186,1187,1188,1189,1190,1191,1192,1193,1194,1195,1196],[],"2026-08-09T01:06:03.914Z",{"url":1201,"kind":1151,"title":1201,"description":899,"feature_evidence":1554,"use_case_evidence":1555,"fetched_at":1556,"status":971,"content_hash":1205},[],[],"2026-08-09T01:06:04.698Z",[1558,1559],{"url":1150,"title":1152,"kind":1151},{"url":1201,"title":1201,"kind":1151},[1561,1562],{"title":1521,"url":1519,"fetched_at":1547},{"title":1182,"url":1182,"fetched_at":1552},[],[1475,1485,1028,1029,1030,1031,1032,1033,1049,1050,1077,1078,1079,1080,1081,1082,1083,1084,1085,1086,1087,1088,1089,1073,1090,1091,1092,1093,1094,1124,1125,1126,1127,1128,1129,1130],"tak",[1567,1568],{"fetched_at":1547,"title":1521,"url":1519},{"fetched_at":1552,"title":1182,"url":1182},"Brak dowodu, że dane użytkowników API są używane do trenowania; Z.ai dla API Services deklaruje, że nie używa User Content do rozwijania lub ulepszania usług bez wyraźnej zgody użytkownika.","CVTG-2K: 0.9116 word accuracy (benchmark).",[1572,1573,1574,1575,1576,1577,1578,1579,1580,1581,1582,1583,1584,1585,1586,1587,1588,1589,1590,1591,1592,1593],"Hybrydowa architektura autoregresyjna + dyfuzyjna.","Text-to-image.","Image-to-image.","Edycja obrazów.","Style transfer.","Identity-preserving generation.","Multi-subject consistency.","Silne renderowanie tekstu w obrazie.","Obsługa złożonych layoutów i scen bogatych w informacje.","REST API.","Self-hosting przez Transformers\u002FDiffusers i SGLang.","text-to-image generation","image-to-image generation","image editing","style transfer","identity-preserving generation","multi-subject consistency","high-detail image generation","text rendering in images","knowledge-intensive generation","support for complex layouts (e.g., infographics, UI, posters)","support for dense textual content in images","Częściowo potwierdzone: Z.ai publikuje DPA dla API Services i deklaruje zgodność z obowiązującymi przepisami ochrony danych; brak bezpośredniego, modelowo-specyficznego potwierdzenia GDPR w zebranych materiałach.",[1596,1597,1262,1598,1599],"Hugging Face Transformers","Hugging Face Diffusers","Z.ai REST API","fal Inference Providers",[1270,1269],[1602,1603,1604],"Model card nie podaje wsparcia dla każdej platformy inference poza Transformers\u002FDiffusers i SGLang.","Brak w zebranych źródłach jednoznacznej listy wspieranych języków w samym modelu; potwierdzono tylko multi-language support dla renderowania tekstu.","Dla najlepszych wyników zalecane są cytaty wokół tekstu do renderowania.",[1371,1606,1607],"glm-image","GlmImagePipeline",[1609,1614,1618],{"plan_name":1610,"access_model":1235,"price":1611,"currency":1290,"billing_period":1612,"included_usage":1613,"trial_days":1235,"card_required":1235,"source_evidence":1235},"Demo \u002F Free trial","$0","per image","2 free generations",{"plan_name":1615,"access_model":1235,"price":1616,"currency":1290,"billing_period":1612,"included_usage":1617,"trial_days":1235,"card_required":1235,"source_evidence":1235},"API usage","$0.015 \u002F image","usage-based",{"plan_name":1619,"access_model":1620,"price":1301,"currency":1235,"billing_period":1235,"included_usage":1235,"trial_days":1235,"card_required":1303,"source_evidence":1475},"Free","open source","2 darmowe generacje, potem $0.015 za obraz; model rozliczany usage-based per image.","Dla API Services Z.ai publikuje Privacy Policy oraz DPA; w zebranych materiałach deklaruje, że jako procesor przetwarza dane klientów wyłącznie w celu świadczenia wsparcia dla API Services i nie wykorzystuje User Content do ulepszania usług bez wyraźnej zgody.","2026-01-14",[1625,1626],{"kind":1151,"title":1152,"url":1150},{"kind":1151,"title":1201,"url":1201},"GLM-Image to otwartoźródłowy model generowania obrazów Z.ai o 16 mld parametrów, z hybrydową architekturą 9B autoregresyjną + 7B dyfuzyjną. Został wydany 14 stycznia 2026 r., ma licencję MIT, oferuje REST API oraz instrukcje self-hostingu przez Transformers\u002FDiffusers i SGLang. Model szczególnie dobrze radzi sobie z tekstem w obrazie, złożonymi układami i scenami bogatymi w wiedzę.","Z.ai publikuje politykę prywatności i DPA dla API Services; dla API deklaruje przetwarzanie danych wyłącznie w celu świadczenia usługi oraz brak użycia User Content do rozwijania usług bez wyraźnej zgody. W dokumentacji API widnieje też pole content safety w odpowiedzi.","Otwarty model generowania obrazów Z.ai o 16 mld parametrów, zaprojektowany pod bardzo dobre renderowanie tekstu, złożone układy i zadania text-to-image \u002F image-to-image.",[1324,914,1631,1632,917,1325,1633,1634],"Local (Transformers\u002FDiffusers)","Local (SGLang)","Local apps","Notebooks (e.g., Colab, Kaggle)",[1636,1637,1638,1639,1640,1641,1642,1643,1644,1645],"developers","designers","businesses","content creators","teams budujące aplikacje z generowaniem obrazów","Researchers","Content creators","Designers","Developers","AI practitioners",[1647,1648,1649,1650,1651,1652,1653,1654,1655,1656,1657,1658],"Generowanie obrazów z tekstu","Edycja obrazów","Style transfer","Tworzenie infografik i plakatów z czytelnym tekstem","Materiały marketingowe","Ilustracje techniczne","Scenariusze złożone layoutowo i bogate informacyjnie","Creating detailed infographics and posters with readable text","Generating UI mockups with complex layouts","Producing high-quality video thumbnails with text","Image editing with style transfer and identity preservation","Automated content generation for SEO and marketing","Z.ai \u002F Zhipu AI",[1661,1662,1663,1664,1665],"FLUX.1 Dev","GPT Image 1","Qwen-Image","Seedream 4.5","CogView-4",{"checked_at":1354,"ready":975,"failed":1667},[],"Narzędzie służy głównie do generowania i edycji obrazów","```markdown\n# **GLM-Image – zaawansowany model AI do generowania i edycji obrazów z otwartym kodem**\n\n**GLM-Image** to otwarty model generatywny o 16 miliardach parametrów, stworzony przez **Z.ai**, który specjalizuje się w precyzyjnym renderowaniu tekstu, złożonych kompozycjach wizualnych oraz zadaniach **text-to-image** i **image-to-image**. Narzędzie łączy hybrydową architekturę autoregresyjną i dyfuzyjną, oferując wysoką jakość wyjściową – idealne dla designerów, twórców treści i programistów poszukujących elastycznego, open-source’owego rozwiązania do generowania grafiki.\n\n---\n\n## **Najważniejsze funkcje**\n\n1. **Hybrydowa architektura (autoregresyjna + dyfuzyjna)**\n   Połączenie dwóch podejść zapewnia lepszą kontrolę nad detalami (np. czytelny tekst w obrazach) oraz płynne przejścia w generowanych wizualizacjach. Model radzi sobie z zadaniami, gdzie tradycyjne rozwiązania dyfuzyjne (jak Stable Diffusion) mają problemy – np. przy skomplikowanych układach tekstowych.\n\n2. **Text-to-Image z precyzyjnym renderowaniem tekstu**\n   GLM-Image wyróżnia się zdolnością do generowania obrazów z **dokładnie odtworzonym tekstem** (np. napisy na plakatach, logo, interfejsy). To rzadka cecha w modelach open-source, gdzie tekst często bywa zniekształcony.\n\n3. **Image-to-Image i edycja obrazów**\n   Możliwość modyfikowania istniejących grafik (np. zmiana stylu, dodawanie elementów) z zachowaniem spójności wizualnej. Przydatne w retuszu, prototypowaniu UI\u002FUX czy tworzeniu wariantów projektu.\n\n4. **Style transfer**\n   Przenoszenie cech artystycznych (np. malarskich, fotograficznych) między obrazami, co umożliwia szybkie eksperymenty z estetyką bez manualnej obróbki.\n\n5. **Open-source i elastyczna integracja**\n   Model jest dostępny do samodzielnego hostowania, co pozwala na dostosowanie do specyficznych potrzeb (np. fine-tuning na własnych danych) lub wdrożenie w lokalnych środowiskach (np. dla firm dbających o prywatność).\n\n---\n\n## **Dla kogo jest GLM-Image?**\n\n- **Designerzy i graficy** – do generowania koncepcji wizualnych, mockupów z czytelnym tekstem lub szybkiego prototypowania.\n- **Twórcy treści i marketerzy** – do tworzenia grafik do social media, banerów reklamowych czy personalizowanych memów z dokładnymi napisami.\n- **Programiści i badacze AI** – jako open-source’owe narzędzie do eksperymentów z generatywnymi modelami, integracji z aplikacjami lub fine-tuningu.\n- **Firmy i startupy** – szukające ekonomicznego rozwiązania do automatyzacji generowania grafik (np. e-commerce, gaming).\n- **Artystcy cyfrowi** – do eksploracji nowych stylów lub uzupełniania prac o elementy wygenerowane przez AI.\n\n---\n\n## **Cena**\n\n- **Darmowy plan**: 2 bezpłatne generacje (dobry sposób na przetestowanie możliwości modelu).\n- **Model płatności**: **Usage-based** – **$0.015 za jeden wygenerowany obraz** (bez abonamentów, rozliczenie za każde użycie).\n- **Brak ukrytych kosztów**: Cena jest przejrzysta i zależy wyłącznie od liczby wygenerowanych obrazów.\n\n> *Uwaga*: Koszty mogą się kumulować przy intensywnym użyciu (np. 100 obrazów = ~$1.5), ale dla okazjonalnych użytkowników lub małych projektów pozostaje to konkurencyjne w porównaniu do zamkniętych rozwiązań (np. MidJourney).\n\n---\n\n## **Zalety i wady**\n\n### ✅ **Zalety**\n1. **Najlepsze renderowanie tekstu w klasie open-source**\n   Model radzi sobie z generowaniem czytelnych napisów, co jest rzadkością w darmowych alternatywach (np. Stable Diffusion często \"zmyla\" litery).\n\n2. **Elastyczność i kontrola**\n   Hybrydowa architektura pozwala na lepsze sterowanie detalami, a open-source’owy charakter umożliwia dostosowanie do indywidualnych potrzeb (np. poprzez fine-tuning).\n\n3. **Przejrzysty model cenowy**\n   Brak abonamentów i płatność za faktyczne użycie ($0.015\u002Fobraz) to dobra opcja dla użytkowników okazjonalnych lub małych zespołów.\n\n### ❌ **Wady**\n1. **Ograniczone darmowe użycie**\n   Tylko 2 bezpłatne generacje to zbyt mało, aby dokładnie ocenić możliwości modelu przed płatnym użyciem.\n\n2. **Koszty przy skali**\n   Przy dużych projektach (setki obrazów) cena może stać się wysoka – brak opcji pakietowych lub zniżek dla firm.\n\n3. **Wymaga wiedzy technicznej**\n   Pełne wykorzystanie potencjału (np. hostowanie lokalne, fine-tuning) może być wyzwaniem dla użytkowników bez doświadczenia w AI\u002FML.\n\n---\n\n## **Werdykt**\n\n**GLM-Image to jedno z najbardziej obiecujących open-source’owych narzędzi do generowania obrazów z tekstem** – szczególnie dla tych, którzy potrzebują precyzyjnego renderowania napisów lub złożonych kompozycji. Jego hybrydowa architektura i elastyczność czynią go ciekawą alternatywą dla Stable Diffusion czy komercyjnych rozwiązań. **Warto wypróbować**, jeśli szukasz modelu z dobrą kontrolą nad detalami i nie obawiasz się płacić za każde użycie – choć przy dużych projektach koszty mogą rosnąć. Dla programistów i firm to także świetna baza do dalszego rozwoju własnych aplikacji",{"source_page_url":1370,"final_url":1370,"fetched_at":1450},{"alternatives_count":184,"active_deals":184},[],[],{"data":1675},{"left":853,"right":1371,"confidence":1676,"content":1677,"generatedAt":1709,"model":1710},0.63,{"h1":1678,"lead":1679,"sections":1680,"verdict":1696,"faq":1699},"GLM-5.2 vs GLM-Image — tekst czy obraz? Wybór narzędzia AI","GLM-5.2 i GLM-Image to dwa otwarte modele od Z.ai, ale służą do zupełnie innych zadań. Pierwszy to potężny LLM z milionowym oknem kontekstowym, drugi to generator obrazów z zaawansowanym renderowaniem tekstu. Sprawdzamy, który wybrać do swoich projektów.",[1681,1684,1687,1690,1693],{"h2":1682,"body":1683},"Do czego służy GLM-5.2, a do czego GLM-Image?","GLM-5.2 to model językowy do generowania kodu, analizy i zadań wymagających rozumowania. Ma okno kontekstowe 1 miliona tokenów, co pozwala na pracę z długimi dokumentami. GLM-Image to model graficzny 16B parametrów, który tworzy i edytuje obrazy, zachowując spójność wielu obiektów i dobrze radząc sobie z tekstem w grafikach. Nie generuje tekstu ani kodu.",{"h2":1685,"body":1686},"Ile kosztuje korzystanie z GLM-5.2 i GLM-Image?","GLM-5.2 oferuje darmowy dostęp przez chat.z.ai, ale płatny Coding Plan kosztuje 18 dolarów miesięcznie. API wyceniono na 1,40 dolara za milion tokenów wejściowych i 4,40 dolara za milion wyjściowych. GLM-Image jest w pełni darmowy na licencji MIT — nie ma planów płatnych. Oba modele można uruchomić lokalnie (self-host).",{"h2":1688,"body":1689},"Jakie są główne ograniczenia każdego z narzędzi?","GLM-5.2 nie ma potwierdzonej obsługi języka polskiego, co może być problemem przy rodzimych treściach. GLM-Image również nie ma informacji o wsparciu dla polskiego. GLM-5.2 wymaga opłaty za Coding Plan i API, a GLM-Image jest całkowicie darmowy. Brak danych o trialach dla GLM-Image.",{"h2":1691,"body":1692},"Które narzędzie lepiej sprawdzi się w pracy z kodem i tekstem?","GLM-5.2 jest tu oczywistym wyborem. Posiada zaawansowane rozumowanie, generowanie kodu, wywoływanie narzędzi i regulację poziomu wysiłku wnioskowania. GLM-Image nie ma żadnych funkcji tekstowych ani kodowych — to czysty generator obrazów.",{"h2":1694,"body":1695},"Które narzędzie lepiej nadaje się do tworzenia grafik z tekstem?","GLM-Image został zaprojektowany do renderowania tekstu w obrazach, obsługi złożonych układów i scen wymagających wiedzy. GLM-5.2 nie generuje obrazów. Jeśli potrzebujesz infografik, plakatów lub grafik z napisami, wybierz GLM-Image.",{"pick_x":1697,"pick_y":1698},"GLM-5.2 dla programistów, analityków i twórców treści, którzy potrzebują zaawansowanego modelu językowego z dużym oknem kontekstowym i możliwością pracy z kodem. Sprawdzi się przy długich dokumentach i złożonych zadaniach rozumowania.","GLM-Image dla grafików, marketerów i twórców wizualnych, którzy potrzebują darmowego, otwartego narzędzia do generowania i edycji obrazów z precyzyjnym tekstem. Idealny do layoutów i grafik informacyjnych.",[1700,1703,1706],{"q":1701,"a":1702},"Czy GLM-5.2 może generować obrazy?","Nie, GLM-5.2 to model językowy, który nie tworzy grafik. Do generowania obrazów służy GLM-Image.",{"q":1704,"a":1705},"Czy GLM-Image obsługuje język polski?","Niepotwierdzone. W dostępnych danych nie ma informacji o wsparciu dla polskiego w GLM-Image.",{"q":1707,"a":1708},"Który model jest tańszy w użyciu?","GLM-Image jest w pełni darmowy na licencji MIT. GLM-5.2 ma darmowy czat, ale płatny Coding Plan i API.","2026-08-11T00:46:08.830Z","deepseek\u002Fdeepseek-v4-flash"]