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GLM 5.2 Thinking

z-ai/glm-5.2:thinking
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GLM 5.2 Thinking

z-ai/glm-5.2:thinking
Back

GLM-5.2 with thinking enabled for harder long-horizon coding, autonomous agent workflows, complex engineering optimization, and real-world development tasks.

Added Jun 15, 2026

Model weights

Context Window

1.0M

Max Output

131.1K

Avg output tokens (7d)

1.6K tokens

85%

Input Price (Auto)

$0.14/1M

Output Price (Auto)

$0.44/1M

Cache Read (Auto)

$0.026/1M

Capabilities

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

33.7

Better than 86% of models compared

Coding Index

68.8

Better than 80% of models compared

Agentic Index

38.4

Better than 78% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

28.4%

Better than 49% of models compared

Harvey LAB-AA

Legal agentic work criterion pass rate

91.0%

Better than 64% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

1230 Elo

Better than 65% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

1358 Elo

Better than 74% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

10.4%

Better than 41% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

78.3%

Better than 81% of models compared

MLCR-AA

Medical long-context reasoning

7.2%

Better than 23% of models compared

Reasoning

HLE

Humanity's Last Exam

41.1%

Better than 89% of models compared

IFBench

Instruction-following benchmark

73.3%

Better than 91% of models compared

CritPt

Research-level physics reasoning

20.9%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

1.0%

Better than 42% of models compared

SciCode

Python programming for scientific computing

51.2%

Better than 59% of models compared

Knowledge

AA-Omniscience Accuracy

Proportion of correctly answered questions

24.3%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

26.3%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

89.5%

Better than 88% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

50.8%

Better than 95% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

99.1%

Better than 99% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

78.3%

Better than 81% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

42.9%

Last updated Oct 1, 2026

Artificial Analysis

Providers

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