AI Model Release Tracker

GLM-5Open Weight

Released

GLM-5 is an AI model released by Z.ai on Wednesday, Feb 11 2026, 51 days after GLM-4.7. It is an open-weight model — the trained weights are available to download and run. It is a 744B parameterThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. model with a 200k token context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider.. Benchmark results (shown below) cover BullshitBench v2, SWE-Bench Verified, SWE-Bench Multilingual, Terminal-Bench 2.0, BrowseComp, Humanity's Last Exam, and 1 more.

API pricing

InputWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it.
$1.00
Cached inputA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again.
$0.20
OutputWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one.
$3.20
  • Reasoning or thinking is supported.
USD per 1M tokensEvery price here is for one million tokens. A token is roughly three-quarters of a word, so a million tokens is about 750,000 words of text.Verified October 19, 2026Read from docs.z.ai. Click the check to open the source.

Available from

GMICloud—$0.60$1.92200Kfp8
StreamLake—$0.60$1.92200Kfp8
Baidu—$0.70$2.24200Kfp8
SiliconFlow—$0.95$2.55200Kfp8
Amazon Bedrock—$1.00$3.20200K—
Novita—$1.00$3.20200Kfp8
Venice—$1.00$3.20200Kfp8
Z.AI—$1.00$3.20200Kfp8
Veniceturbo$1.20$4.00200K—
USD per 1M tokensEvery price here is for one million tokens. A token is roughly three-quarters of a word, so a million tokens is about 750,000 words of text.Checked November 12, 2026Third-party rates from openrouter.ai, not the lab’s own list price.

Benchmarks

Coding

SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better.
77.8%
#21 of 80
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better.
73.3%
#17 of 26

Terminal & CLI

Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better.
56.2%
#16 of 28

Agentic & tool use

BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better.
75.9%
#25 of 35

Reasoning & science

Humanity's Last ExamMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better.
50.4%with tools
#29 of 60
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better.
86%
#39 of 101

Robustness

BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better.via BullshitBench
28%
#65 of 100

Compare GLM-5 with

GLM-5

Suggested comparisons

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About

GLM-5 was introduced in February 2026 as a mixture-of-experts model with 744 billion total parameters and about 40 billion active per token. Z.ai expanded pretraining and incorporated DeepSeek Sparse Attention to improve long-context efficiency. The release focused on coding and agentic engineering.

Z.ai described an asynchronous reinforcement-learning system called slime as part of its training infrastructure and released the weights under the MIT License. The model card reported separate results for different tool and context-management setups. In particular, context-managed browsing results should be distinguished from ordinary BrowseComp evaluations when comparing agent systems.

Frequently asked questions

GLM-5 was released by Z.ai on Wednesday, Feb 11 2026.

All Z.ai releases

13 tracked