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  1. Models
  2. Text
  3. Z.AI

GLM 5.2 Thinking

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

  • Reasoning
  • Tool Calling
  • Structured Output

Added Jun 15, 2026

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.176
Output
$1.54
Cache read
$0.069
Compare provider prices

Specifications

Context window
1M
Max output
131.1K
Parameters
744B / 40B
Total / active
Avg output (7d)
1.7K tokens
Longer than 89% of models

Benchmarks

Sourced from Artificial Analysis.

Coding Index

68.8

Coding Index: 68.8
Better than 81% of models compared

Agentic Index

38.4

Agentic Index: 38.4
Better than 76% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

28.4%

Better than 45% of models compared

AutomationBench-AA Tasks Completed

Fully completed workflows without guardrail violations

6.2%

Better than 45% of models compared

AA-Briefcase v1.1

Agentic knowledge work (Elo)

1227 Elo

Better than 61% of models compared

GDPval-AA v2.1

Economically valuable tasks (Elo)

1382 Elo

Better than 71% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

10.4%

Better than 40% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

78.3%

Better than 79% of models compared

MLCR-AA

Medical long-context reasoning

12.8%

Better than 44% of models compared

Reasoning

HLE

Humanity's Last Exam

41.1%

Better than 87% 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 39% of models compared

SciCode

Python programming for scientific computing

51.2%

Better than 56% of models compared

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

89.5%

Better than 87% 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 79% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

44.1%

Last updated Oct 29, 2026

Artificial Analysis

Providers

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