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 weightsPricing
Auto routing · per 1M tokens- Input
- $0.42
- Output
- $1.32
- Cache read
- $0.078
Specifications
- Context window
- 1M
- Max output
- 131.1K
- Parameters
- 744B / 40B
- Total / active
- Avg output (7d)
- 1.7K tokens
- Longer than 88% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
33.7
Coding Index
68.8
Agentic Index
38.4
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)
1229 Elo
Better than 64% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1374 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 58% 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
43.7%
Last updated Oct 9, 2026
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