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Qwen 3 235b A22B 2507 Thinking

qwen/qwen3-235b-a22b-thinking-2507
Provider logo

Qwen 3 235b A22B 2507 Thinking

qwen/qwen3-235b-a22b-thinking-2507

The thinking version of Qwen 3 235b A22B 2507, with enhanced reasoning capabilities and step-by-step problem solving.

Added Sep 11, 2025

Model weights

Context Window

256.0K

Max Output

262.1K

Avg output tokens (7d)

1.6K tokens

82%

Input Price (Auto)

$0.30/1M

Output Price (Auto)

$0.50/1M

Cache Read (Auto)

$0.15/1M

Benchmarks

Performance metrics and benchmarks

Sourced from Artificial Analysis.

Intelligence Index

12.7

Better than 52% of models compared

Coding Index

22.1

Better than 26% of models compared

Agentic Index

1.3

Better than 18% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

2.2%

Better than 24% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

190 Elo

Better than 13% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

495 Elo

Better than 21% of models compared

Document reasoning

GDP.pdf

Professional PDF reasoning: all-pass rate

4.8%

Better than 29% of models compared

AA-LCR v1.1

Long context reasoning with updated grading

72.0%

Better than 71% of models compared

Reasoning

HLE

Humanity's Last Exam

15.9%

Better than 66% of models compared

IFBench

Instruction-following benchmark

51.2%

Better than 61% of models compared

CritPt

Research-level physics reasoning

0.0%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 18% of models compared

SciCode

Python programming for scientific computing

41.4%

Better than 32% of models compared

LiveCodeBench

Contamination-free coding benchmark

78.8%

Better than 90% of models compared

Math

AIME 2025

American Invitational Mathematics Examination 2025

91.0%

Better than 92% of models compared

AIME

American Invitational Mathematics Examination

94.0%

Better than 98% of models compared

Math-500

Diverse mathematical problem solving benchmark

98.4%

Better than 95% of models compared

Knowledge

MMLU-Pro

Professional and academic subject knowledge

84.3%

Better than 89% of models compared

AA-Omniscience Accuracy

Proportion of correctly answered questions

22.8%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

89.8%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

79.0%

Better than 65% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

13.6%

Better than 50% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

53.2%

Better than 54% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

72.0%

Better than 71% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

0.0%

Last updated Sep 11, 2026

Artificial Analysis

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