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Favicon de z-ai

Z.ai: GLM 4.7

z-ai/glm-4.7

Poids du modèle
Comparer

GLM-4.7 is Z.ai’s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while delivering more natural conversational experiences and superior front-end aesthetics.

Modalités
Prix en entrée / sortie
0,40 $US / 1,75 $USpar 1M
Contexte
205K
Publié
22 déc. 2025
Comparer
FournisseursTarificationPerformancesDisponibilitéÉvaluations comparativesApplicationsActivitéFAQExplorer

Fournisseurs

Différentes entreprises hébergent le même modèle. OpenRouter achemine votre requête vers l’une d’elles selon le mode de routage choisi — Balanced (prix + vitesse), Nitro (le plus rapide) ou Floor (le moins cher).

Tarification

Le prix moyen que les clients paient réellement pour ce modèle, à côté des prix affichés par les fournisseurs. Grâce à la mise en cache et aux remises, le prix réellement payé est souvent bien inférieur au prix affiché.

Performances

Le débit est la vitesse à laquelle le modèle écrit (jetons par seconde — plus il est élevé, mieux c’est). La latence est le temps total d’aller-retour (plus il est faible, mieux c’est). Le TTFT est le délai avant le premier jeton — le temps avant de voir quelque chose apparaître (plus il est faible, mieux c’est).

Disponibilité

La disponibilité correspond au pourcentage des 3 derniers jours pendant lequel au moins un fournisseur répondait aux requêtes. La disponibilité du service correspond au pourcentage de temps pendant lequel l’inférence a été assurée avec succès. OpenRouter surveille en continu les fournisseurs et utilise le meilleur fournisseur suivant lorsque l’un d’eux renvoie une erreur.

Évaluations comparatives

Scores aux évaluations standardisées et réponses de ce modèle aux prompts communs des tests de référence. Des pourcentages plus élevés sont meilleurs — et chaque rang indique la position de ce modèle parmi les modèles évalués sur cette source.

Résumé des scores aux tests de performance pour Z.ai: GLM 4.7 (Artificial Analysis, Design Arena et Vals AI)
SourceTest de performanceScore
Artificial AnalysisGLM-4.7 (Non-reasoning) GPQA Diamond66.4%
Artificial AnalysisGLM-4.7 (Non-reasoning) HLE6.4%
Artificial AnalysisGLM-4.7 (Non-reasoning) IFBench54.6%
Artificial AnalysisGLM-4.7 (Non-reasoning) τ²-Bench Telecom94.2%
Artificial AnalysisGLM-4.7 (Non-reasoning) AA-LCR40.7%
Artificial AnalysisGLM-4.7 (Non-reasoning) CritPt0.0%
Artificial AnalysisGLM-4.7 (Non-reasoning) LiveCodeBench56.2%
Artificial AnalysisGLM-4.7 (Non-reasoning) Terminal-Bench Hard30.3%
Artificial AnalysisGLM-4.7 (Non-reasoning) MMLU-Pro79.4%
Artificial AnalysisGLM-4.7 (Non-reasoning) AIME 202548.0%
Artificial AnalysisGLM-4.7 (Reasoning) Coding Index45,3
Artificial AnalysisGLM-4.7 (Reasoning) GPQA Diamond85.9%
Artificial AnalysisGLM-4.7 (Reasoning) HLE27.4%
Artificial AnalysisGLM-4.7 (Reasoning) IFBench67.9%
Artificial AnalysisGLM-4.7 (Reasoning) τ²-Bench Telecom95.9%
Artificial AnalysisGLM-4.7 (Reasoning) AA-LCR71.0%
Artificial AnalysisGLM-4.7 (Reasoning) τ-Bench Banking12.2%
Artificial AnalysisGLM-4.7 (Reasoning) GDPval-AA26.1%
Artificial AnalysisGLM-4.7 (Reasoning) CritPt1.7%
Artificial AnalysisGLM-4.7 (Reasoning) LiveCodeBench89.4%
Artificial AnalysisGLM-4.7 (Reasoning) Terminal-Bench Hard31.8%
Artificial AnalysisGLM-4.7 (Reasoning) Terminal-Bench 2.145.3%
Artificial AnalysisGLM-4.7 (Reasoning) MMLU-Pro85.6%
Artificial AnalysisGLM-4.7 (Reasoning) AIME 202595.0%
Design ArenaGLM 4.7 Agents Arena Androidnative Elo1108
Design ArenaGLM 4.7 Agents Arena Full Stack Elo1050
Design ArenaGLM 4.7 Agents Arena Godotgamedev Elo1058
Design ArenaGLM 4.7 Agents Arena Mobile Apps Elo1105
Design ArenaGLM 4.7 Models Arena 3D Elo1187
Design ArenaGLM 4.7 Models Arena Asciiart Elo1162
Design ArenaGLM 4.7 Models Arena Code Categories Elo1215
Design ArenaGLM 4.7 Models Arena Data Visualization Elo1195
Design ArenaGLM 4.7 Models Arena Game Development Elo1187
Design ArenaGLM 4.7 Models Arena SVG Elo1139
Design ArenaGLM 4.7 Models Arena UI Component Elo1194
Design ArenaGLM 4.7 Models Arena Website Elo1224
Vals AIGLM 4.7 AIME93.3%
Vals AIGLM 4.7 CaseLaw v254.9%
Vals AIGLM 4.7 CorpFin v246.4%
Vals AIGLM 4.7 Finance Agent v1.146.0%
Vals AIGLM 4.7 GPQA Diamond80.0%
Vals AIGLM 4.7 IOI v17.6%
Vals AIGLM 4.7 LiveCodeBench82.2%
Vals AIGLM 4.7 LegalBench83.4%
Vals AIGLM 4.7 MedCode32.8%
Vals AIGLM 4.7 MedQA93.7%
Vals AIGLM 4.7 MedScribe68.6%
Vals AIGLM 4.7 MGSM88.2%
Vals AIGLM 4.7 MMLU Pro82.7%
Vals AIGLM 4.7 ProofBench v16.0%
Vals AIGLM 4.7 SWE-bench69.4%
Vals AIGLM 4.7 TaxEval v268.8%
Vals AIGLM 4.7 Terminal-Bench 2.038.2%

Applications

Applications publiques qui envoient le plus de trafic à ce modèle. Un bon indicateur des charges de travail réelles en production — et un aperçu des cas d’usage auxquels ce modèle convient le mieux.

Activité

Volume de jetons et trafic des requêtes vers ce modèle au fil du temps.

API

Code prêt à l’emploi pour appeler ce modèle. L’API d’OpenRouter est compatible avec OpenAI — la plupart des SDK fonctionnent en remplaçant simplement l’URL de base. Seul le slug du modèle ci-dessous change d’un modèle à l’autre.

Actuellement indisponible via le routage dans la région UE. Documentation sur le routage intrarégional

Explorer d’autres modèles

Classements des modèles d’IAClassement
Latence / débit
10% off
0,60 $US0,5400 $US2,20 $US1,980 $US0,11 $US0,09900 $US4,42s25 tps
99,97 %
0,7000 $US2,500 $US--4,57s23 tps
98,76 %
0,6000 $US2,200 $US0,11000 $US4,08s25 tps
99,97 %
0,4000 $US1,750 $US0,08000 $US4,33s8 tps
99,96 %
27% off
0,55 $US0,4004 $US2,65 $US1,929 $US0,11 $US0,08008 $US4,30s9 tps
93,62 %
Non utilisé pour le routage Standard :Pourquoi ces points de terminaison ne sont pas utilisés
Flex
0,3200 $US1,400 $US0,06400 $US12,99s20 tps
100,00 %
Débit
25jetons/s

P50, meilleur résultat tous fournisseurs confondus

Latence
4,08s

P50, meilleur fournisseur

Évaluations comparatives AutoExacto
GPQA DiamondTAU-BenchGoogle83,9 %76,5 %auto-routing83,5 %72,6 %Z.ai84,1 %71,9 %NovitaAI83,8 %71,6 %AtlasCloud83,7 %71,3 %
+3 fournisseurs supplémentaires
Disponibilité (3 j)Le modèle était joignable. Requête acheminée vers un fournisseur.
100,00 %
Disponibilité (3 j)Le modèle a renvoyé un résultat d’inférence, quel que soit le fournisseur. Les erreurs et les réponses vides sont comptabilisées à son détriment.
99,25 %

Disponibilité au cours des 3 derniers jours

72 dernières heures
Disponibilité 99,25 %
Il y a 3 joursIl y a 2 joursHierMaintenant

Disponibilité au cours des dernières 24 heures

OpenRouter Availability
99,26 %
Without Routing
97,32 %

Lorsqu’une erreur survient chez un fournisseur en amont, nous pouvons rétablir le service en routant la requête vers un autre fournisseur opérationnel, si les filtres de votre requête le permettent. Vous pouvez accéder par programmation aux données de disponibilité de chaque fournisseur via l’ API des points de terminaison. Découvrez nos options d’ équilibrage de charge et de routage des fournisseurs.

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SillyTavern is the LLM frontend for power users, a chat interface that connects to any model and gives deep control through character creation, roleplay, and prompt customization.
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Questions fréquentes

GLM-4.7 is Z.ai’s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while delivering more natural conversational experiences and superior front-end aesthetics.

GLM 4.7 coûte 0,40 $US/M de jetons d’entrée et 1,75 $US/M de jetons de sortie, avec des tarifs distincts pour Cache Read à 0,08 $US/M tokens.

GLM 4.7 dispose d’une fenêtre de contexte de 204 800 jetons. Il prend en charge jusqu’à 131 072 jetons de complétion.

Oui. GLM 4.7 accepte tools et tool_choice pour les appels de fonctions chez 4 des 5 fournisseurs qui le proposent, et les requêtes qui envoient des outils sont acheminées vers ces fournisseurs. Il prend également en charge les sorties structurées via un schéma JSON dans response_format.

GLM 4.7 est proposé par 5 fournisseurs sur OpenRouter : DeepInfra, Venice, NovitaAI, Z.ai et Mancer. Les requêtes sont acheminées vers le meilleur fournisseur disponible, avec un basculement automatique vers les autres, et vous pouvez fixer ou exclure des fournisseurs avec le routage des fournisseurs.

GLM 4.7 est sorti le 22 décembre 2025.

Autres modèles de Z.ai

GLM 5.3 FlashX Legal

GLM-5.3-FlashX-Legal (GLM-Legal) is Z.ai's legal-domain variant of GLM-5.3-FlashX, a native multimodal model that accepts text, image, and video input and produces text. It adds legal-domain mid-training and post-training with reinforcement learning in task environments modeled on real legal workflows, targeting document understanding, legal reasoning, and multi-step task execution such as legal research, contract review, cross-document consistency checks, and drafting or revising legal documents.

It keeps the GLM-5.3-FlashX 1M-token context window, mandatory thinking mode, function calling, structured output, and streaming, and is designed to run in agent setups connected to legal data and document-editing tools (MCP services).

TexteContexte de 1,0M0,37 $US / 1,25 $US
GLM 5.3 Prime

GLM-5.3-Prime is the high-speed variant of Z.ai's GLM-5.3, inheriting its full capabilities while delivering 1.5–2× the output throughput through inference acceleration. It supports text input and output with a 1M-token context window and up to 128K output tokens, and targets coding and agentic workloads, including long-horizon multi-turn agent orchestration, real-time conversation, and streaming code generation.

Reasoning is always on and cannot be disabled. Reasoning efforts low, high, and max are supported; max is the default.

TexteContexte de 1M2,80 $US / 8,80 $US
GLM 5.3 FlashX

GLM-5.3-FlashX is the high-speed variant of Z.ai's GLM-5.3-Flash, a native multimodal model delivering inference speeds of up to 200 tokens/s. Built on the same hybrid sparse and linear attention architecture (320B total parameters, 18B active), it is suited for efficient coding, visual understanding, and long-horizon agent tasks with a 1M-token context window.

TexteContexte de 1,0M0,37 $US / 1,25 $US
GLM Flash Latest

This model always redirects to the latest model in the GLM Flash family.

TexteContexte de 1,0M
GLM 5.3 Flash

GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while reducing compute overhead.

TexteContexte de 1,0M0,0285 $US / 0,0923 $US
GLM 5.3 Flash (batch)

GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while reducing compute overhead.

TexteContexte de 1,0M0,05 $US / 0,18 $US
GLM Latest

This model always redirects to the latest GLM model from Z.ai.

TexteContexte de 1,0M
GLM 5.3

GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves on GLM-5.2 in coding and in the balance between performance and token efficiency.

Reasoning is always on and cannot be disabled. Reasoning efforts low, high, and max are supported; max is the default.

TexteContexte de 1,0M0,0699 $US / 1,40 $US
GLM 5.3 (batch)

GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves on GLM-5.2 in coding and in the balance between performance and token efficiency.

Reasoning is always on and cannot be disabled. Reasoning efforts low, high, and max are supported; max is the default.

TexteContexte de 1,0M0,45 $US / 2 $US
GLM 5.2

GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering, and complex multi-step automation.

Reasoning efforts high and xhigh are supported; xhigh maps to max reasoning. It is particularly strong at coding and tool use across long-running tasks, able to maintain engineering context and follow standards consistently through a full development workflow, from requirements to multi-platform deployment, in a single task.

TexteContexte de 1,0M0,006 $US / 6,80 $US
GLM 5.1

GLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interactions, GLM-5.1 can work independently and continuously on a single task for more than 8 hours, autonomously planning, executing, and improving itself throughout the process, ultimately delivering complete, engineering-grade results.

TexteContexte de 205K0,90 $US / 2,90 $US
GLM 5V Turbo

GLM-5V-Turbo is Z.ai’s first native multimodal agent foundation model, built for vision-based coding and agent-driven tasks. It natively handles image, video, and text inputs, excels at long-horizon planning, complex coding, and task execution, and works seamlessly with agents to complete the full loop of “perceive → plan → execute“.

TexteContexte de 203K1,20 $US / 4 $US
GLM 5 Turbo

GLM-5 Turbo is a new model from Z.ai designed for fast inference and strong performance in agent-driven environments such as OpenClaw scenarios. It is deeply optimized for real-world agent workflows involving long execution chains, with improved complex instruction decomposition, tool use, scheduled and persistent execution, and overall stability across extended tasks.

TexteContexte de 203K1,20 $US / 4 $US
GLM 5

GLM-5 is Z.ai’s flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With advanced agentic planning, deep backend reasoning, and iterative self-correction, GLM-5 moves beyond code generation to full-system construction and autonomous execution.

TexteContexte de 205K0,60 $US / 1,92 $US
GLM 4.7 Flash

As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning, and tool collaboration, and has achieved leading performance among open-source models of the same size on several current public benchmark leaderboards.

TexteContexte de 200K0,0605 $US / 0,40 $US
GLM 4.6V

GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K tokens, processes complex page layouts and charts directly as visual inputs, and integrates native multimodal function calling to connect perception with downstream tool execution. The model also enables interleaved image-text generation and UI reconstruction workflows, including screenshot-to-HTML synthesis and iterative visual editing.

TexteContexte de 131K0,30 $US / 0,90 $US
GLM 4.6

Compared with GLM-4.5, this generation brings several key improvements:

Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks. Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages. Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability. More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks. Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.

TexteContexte de 205K0,43 $US / 1,75 $US
GLM 4.5V

GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding, image Q&A, OCR, and document parsing, with strong gains in front-end web coding, grounding, and spatial reasoning. It offers a hybrid inference mode: a "thinking mode" for deep reasoning and a "non-thinking mode" for fast responses. Reasoning behavior can be toggled via the reasoning enabled boolean. Learn more in our docs

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GLM 4.5

GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly enhanced capabilities in reasoning, code generation, and agent alignment. It supports a hybrid inference mode with two options, a "thinking mode" designed for complex reasoning and tool use, and a "non-thinking mode" optimized for instant responses. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs

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GLM 4.5 Air

GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter size. GLM-4.5-Air also supports hybrid inference modes, offering a "thinking mode" for advanced reasoning and tool use, and a "non-thinking mode" for real-time interaction. Users can control the reasoning behaviour with the reasoning enabled boolean. Learn more in our docs

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