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GLM 5.1 Thinking

z-ai/glm-5.1:thinking
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GLM 5.1 Thinking

z-ai/glm-5.1:thinking

GLM-5.1 with extended thinking enabled. Ranks #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo (as of April 2026). Excels at long-horizon tasks, running autonomously for up to 8 hours while refining strategies through thousands of iterations. Run at FP8.

Added Mar 27, 2026

Model weights

Context Window

200.0K

Max Output

131.1K

Input Price (Auto)

$0.70/1M

Output Price (Auto)

$2.50/1M

Cache Read (Auto)

$0.18/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

26.4

Better than 81% of models compared

Coding Index

55.8

Better than 66% of models compared

Agentic Index

25.2

Better than 66% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

20.3%

Better than 48% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

964 Elo

Better than 52% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

1180 Elo

Better than 62% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

8.4%

Better than 41% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

73.7%

Better than 74% of models compared

Reasoning

HLE

Humanity's Last Exam

30.1%

Better than 82% of models compared

IFBench

Instruction-following benchmark

76.3%

Better than 95% of models compared

CritPt

Research-level physics reasoning

4.6%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

2.0%

Better than 55% of models compared

SciCode

Python programming for scientific computing

44.8%

Better than 43% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

23.7%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

29.9%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

86.8%

Better than 82% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

43.2%

Better than 90% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

97.7%

Better than 98% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

73.7%

Better than 74% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

34.0%

Last updated Sep 19, 2026

Artificial Analysis

Providers

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