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.
- Reasoning
- Tool Calling
- Structured Output
Added Mar 27, 2026
Model weightsPricing
Auto routing · per 1M tokens- Input
- $0.70
- Output
- $2.50
- Cache read
- $0.18
Specifications
- Context window
- 200K
- Max output
- 131.1K
- Parameters
- 744B / 40B
- Total / active
- Avg output (7d)
- 2.2K tokens
- Longer than 94% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
26.1
Coding Index
55.8
Agentic Index
23.9
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
20.3%
Better than 42% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
964 Elo
Better than 47% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1119 Elo
Better than 55% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
8.4%
Better than 35% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
73.7%
Better than 70% of models compared
Reasoning
HLE
Humanity's Last Exam
30.1%
Better than 78% 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 48% of models compared
SciCode
Python programming for scientific computing
44.8%
Better than 37% 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 70% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
31.0%
Last updated Oct 9, 2026
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