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Qwen/Qwen3-235B-A22B-Instruct-2507

Qwen3-235B-A22B-Instruct-2507 is the updated version of the Qwen3-235B-A22B non-thinking mode, featuring Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.

Qwen3-235B-A22B-Instruct-2507 is the updated version of the Qwen3-235B-A22B non-thinking mode, featuring Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.

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Qwen/Qwen3-235B-A22B-Instruct-2507 cover image

Qwen3-235B-A22B-Instruct-2507

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Highlights

We introduce the updated version of the Qwen3-235B-A22B non-thinking mode, named Qwen3-235B-A22B-Instruct-2507, featuring the following key enhancements:

  • Significant improvements in general capabilities, including instruction following, logical reasoning, text comprehension, mathematics, science, coding and tool usage.
  • Substantial gains in long-tail knowledge coverage across multiple languages.
  • Markedly better alignment with user preferences in subjective and open-ended tasks, enabling more helpful responses and higher-quality text generation.
  • Enhanced capabilities in 256K long-context understanding.

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Model Overview

Qwen3-235B-A22B-Instruct-2507 has the following features:

  • Type: Causal Language Models
  • Training Stage: Pretraining & Post-training
  • Number of Parameters: 235B in total and 22B activated
  • Number of Paramaters (Non-Embedding): 234B
  • Number of Layers: 94
  • Number of Attention Heads (GQA): 64 for Q and 4 for KV
  • Number of Experts: 128
  • Number of Activated Experts: 8
  • Context Length: 262,144 natively.

NOTE: This model supports only non-thinking mode and does not generate **\<think>****\</think>** blocks in its output. Meanwhile, specifying enable_thinking=False is no longer required.

For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our blog, GitHub, and Documentation.

Performance

Deepseek-V3-0324GPT-4o-0327Claude Opus 4 Non-thinkingKimi K2Qwen3-235B-A22B Non-thinkingQwen3-235B-A22B-Instruct-2507
Knowledge
MMLU-Pro81.279.886.681.175.283.0
MMLU-Redux90.491.394.292.789.293.1
GPQA68.466.974.975.162.977.5
SuperGPQA57.351.056.557.248.262.6
SimpleQA27.240.322.831.012.254.3
CSimpleQA71.160.268.074.560.884.3
Reasoning
AIME2546.626.733.949.524.770.3
HMMT2527.57.915.938.810.055.4
ARC-AGI9.08.830.313.34.341.8
ZebraLogic83.452.6-89.037.795.0
LiveBench 2024112566.963.774.676.462.575.4
Coding
LiveCodeBench v6 (25.02-25.05)45.235.844.648.932.951.8
MultiPL-E82.282.788.585.779.387.9
Aider-Polyglot55.145.370.759.059.657.3
Alignment
IFEval82.383.987.489.883.288.7
Arena-Hard v2*45.661.951.566.152.079.2
Creative Writing v381.684.983.888.180.487.5
WritingBench74.575.579.286.277.085.2
Agent
BFCL-v364.766.560.165.268.070.9
TAU-Retail49.660.3#81.470.765.271.3
TAU-Airline32.042.8#59.653.532.044.0
Multilingualism
MultiIF66.570.4-76.270.277.5
MMLU-ProX75.876.2-74.573.279.4
INCLUDE80.182.1-76.975.679.5
PolyMATH32.225.530.044.827.050.2

*: For reproducibility, we report the win rates evaluated by GPT-4.1.

#: Results were generated using GPT-4o-20241120, as access to the native function calling API of GPT-4o-0327 was unavailable.

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