Agentic Coding
Handles multi-step coding agent loops while retaining context across turns, scoring 73.8% on SWE-bench Verified and 66.7% on SWE-bench Multilingual.
Z.ai
GLM 4.7
GLM-4.7 is a 358-billion-parameter large language model developed by Z.ai (formerly Zhipu AI/THUDM) and released in December 2025. It is designed specifically for agentic workflows, multi-step coding tasks, terminal automation, and complex mathematical and scientific reasoning. The model is available under an MIT license, making it usable for both commercial and non-commercial applications. It supports a 131,072-token context window, allowing it to handle long documents and extended coding sessions. What distinguishes GLM-4.7 from earlier GLM releases is a set of three reasoning mechanisms: Interleaved Thinking, which applies reasoning before every response and tool call; Preserved Thinking, which retains reasoning context across conversation turns to maintain consistency; and Turn-level Thinking, which lets developers toggle reasoning depth on or off per turn. On benchmarks, the model scores 73.8% on SWE-bench Verified, 95.7% on AIME 2025, and 87.4% on τ²-Bench. It is best suited for developers and researchers building agent pipelines, automated coding tools, or applications requiring reliable multi-step planning.
Dec 22, 2025
131,072 context
16,384 tokens output
Agentic Coding
Terminal Automation
Mathematical Reasoning
Multi-Step Planning
Tool Use
Long Context Processing
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Z.ai
Model ID
The routed model identifier exposed by upstream providers.
z-ai/glm-4.7
Input Context Window
The number of tokens supported by the input context window.
131,072
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
16,384 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Dec 22, 2025
4 months ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
Unknown
API Providers
The providers that offer this model. This is not an exhaustive list.
DeepInfra, Parasail, SiliconFlow, AtlasCloud, Novita, Venice, Z.AI, Google, Phala, Cerebras
Modalities
Types of data this model can process.
Text
What is GLM 4.7
A fuller summary of positioning, capabilities, and source-specific details for GLM 4.7.
GLM-4.7 is a 358-billion-parameter large language model developed by Z.ai (formerly Zhipu AI/THUDM) and released in December 2025. It is designed specifically for agentic workflows, multi-step coding tasks, terminal automation, and complex mathematical and scientific reasoning. The model is available under an MIT license, making it usable for both commercial and non-commercial applications. It supports a 131,072-token context window, allowing it to handle long documents and extended coding sessions.
What distinguishes GLM-4.7 from earlier GLM releases is a set of three reasoning mechanisms: Interleaved Thinking, which applies reasoning before every response and tool call; Preserved Thinking, which retains reasoning context across conversation turns to maintain consistency; and Turn-level Thinking, which lets developers toggle reasoning depth on or off per turn. On benchmarks, the model scores 73.8% on SWE-bench Verified, 95.7% on AIME 2025, and 87.4% on τ²-Bench. It is best suited for developers and researchers building agent pipelines, automated coding tools, or applications requiring reliable multi-step planning.
Capabilities
What GLM 4.7 supports
Terminal Automation
Executes command sequencing, error recovery, and multi-step shell automation, achieving 41.0% on Terminal Bench.
Mathematical Reasoning
Solves advanced math and science problems, scoring 95.7% on AIME 2025 and 97.1% on HMMT Feb. 2025.
Multi-Step Planning
Sequences actions across complex tasks using structured reasoning, scoring 87.4% on τ²-Bench.
Tool Use
Calls external tools reliably within agent loops using Interleaved Thinking, which applies reasoning before each tool invocation.
Long Context Processing
Processes inputs up to 131,072 tokens, supporting extended documents, codebases, and multi-turn conversations.
Multilingual Support
Handles coding and reasoning tasks across multiple languages, as reflected by its dedicated SWE-bench Multilingual score of 66.7%.
Configurable Reasoning
Turn-level Thinking lets developers enable or disable deep reasoning per conversation turn, trading response depth for speed as needed.
Science Reasoning
Addresses graduate-level science questions, achieving 85.7% on GPQA-Diamond.
Pricing for GLM 4.7
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.40
Per million tokens
Output tokens
$1.75
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
Cache read
$0.08
maxTemperature
1
maxResponseSize
16,384 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
DeepInfra
Parasail
SiliconFlow
AtlasCloud
Novita
Venice
Z.AI
Google
Phala
Cerebras
Provider Endpoints
Endpoint-level provider data currently available for this model.
DeepInfra
Parasail
SiliconFlow
AtlasCloud
Novita
Venice
Z.AI
Phala
Cerebras
Configuration & Parameters
The configurable options currently documented for this model.
Reasoning Effort
Toggle GroupSupported Request Parameters
Parameters currently listed by OpenRouter or the local catalog for this model.
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2025
American math olympiad problems (2025)
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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HLE
Questions that challenge frontier models across many domains
|
|
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LiveCodeBench
Real-world coding tasks from recent competitions
|
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
|
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SciCode
Scientific research coding and numerical methods
|
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SWE-bench Verified
Real GitHub issues requiring multi-file code fixes
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about GLM 4.7
GLM 4.7 discussions are most active in r/LocalLLaMA, r/SillyTavernAI, r/unsloth. Top Reddit threads cluster around benchmark and model-comparison threads, safety and censorship questions, coding workflow discussions.
The strongest match in this snapshot has 756 upvotes and 230 comments.
glm 4.7 is gonna be gone soon in nvidia nim, what other free models/providers are there that are good? cause im a broke student and cant afford to pay for models especially in my countries economy. I seriously DO NOT want to go back to gemini 2.5 flash
Ive been trying out GLM 4.7 nvidia nim for a bit, and honestly its pretty amazing, im running it with 4.0 fatman preset currently but one thing i cant really understand is why are the messages so short? I dont know if its something with my preset. its not bad thing really but ive been gemini pilled with long ass responses. so ive been wondering if anyone knows how to make GLM 4.7's messages longer? thanks
These are all the models that i am interested in using, and they are all that i can afford at the moment. Would be great if you can also suggest other models as well!
I aim for a more emotional, less descriptive and flowery type of dialogues.
this is just my personal opinion and experience. but ever since the release of 4.7, i've been having a lot of issues. especially with those annoying ai patterns and very unnatural dialogue, things i simply didn't have when using 4.6
even though my prompt includes instructions for natural, human-like writing and dialogue, glm 4.7 feels like a chaotic machine that can't interpret things properly. i changed the prompt and the parameters multiple times, and it still felt very strange. there were small improvements here and there, but overall it didn't feel immersive at all and just ended up frustrating me
maybe it needs more specific instructions, different settings… idk. all i know is that i'm tired of spending days trying to make 4.7 work for my roleplay style without any success
FAQ
Common questions about GLM 4.7
What is the context window size for GLM-4.7?
GLM-4.7 supports a context window of 131,072 tokens, which allows it to process long documents, extended codebases, and lengthy multi-turn conversations in a single session.
What license does GLM-4.7 use?
GLM-4.7 is released under the MIT license, which permits both commercial and non-commercial use without royalty restrictions.
What is the knowledge cutoff for GLM-4.7?
Based on the available metadata, GLM-4.7 has a training date of December 2025. A specific knowledge cutoff date beyond this has not been published in the provided metadata.
How many parameters does GLM-4.7 have?
GLM-4.7 has 358 billion parameters, making it a large-scale model intended for demanding tasks such as agentic coding, complex reasoning, and terminal automation.
What are the three thinking mechanisms introduced in GLM-4.7?
GLM-4.7 introduces Interleaved Thinking (reasoning before every response and tool call), Preserved Thinking (retaining reasoning context across conversation turns), and Turn-level Thinking (allowing developers to toggle reasoning on or off per turn).
Who developed GLM-4.7 and where can I access it?
GLM-4.7 was developed by Z.ai, formerly known as Zhipu AI/THUDM. It is available on Hugging Face, via NVIDIA NIM, and through the Z.ai API. The model weights and related code are also accessible on GitHub.
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