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

Qwen3-235B-A22B-Thinking-2507 is the Qwen3's new model with scaling the thinking capability of Qwen3-235B-A22B, improving both the quality and depth of reasoning. It features the following key enhancements:

• Significantly improved performance on reasoning tasks, including logical reasoning, mathematics, science, coding, and academic benchmarks that typically require human expertise — achieving state-of-the-art results among open-source thinking models.
• Markedly better general capabilities, such as instruction following, tool usage, text generation, and alignment with human preferences.
• Enhanced 256K long-context understanding capabilities.

NOTE: This version has an increased thinking length. We strongly recommend its use in highly complex reasoning tasks.

Model Overview

Qwen3-235B-A22B-Thinking-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 thinking mode.

Additionally, to enforce model thinking, the default chat template automatically includes **\<think>**. Therefore, it is normal for the model's output to contain only **\</think>** without an explicit opening <think> tag.

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

Performance

	Deepseek-R1-0528	OpenAI O4-mini	OpenAI O3	Gemini-2.5 Pro	Claude4 Opus Thinking	Qwen3-235B-A22B Thinking	Qwen3-235B-A22B-Thinking-2507
Knowledge
MMLU-Pro	85.0	81.9	85.9	85.6	-	82.8	84.4
MMLU-Redux	93.4	92.8	94.9	94.4	94.6	92.7	93.8
GPQA	81.0	81.4*	83.3*	86.4	79.6	71.1	81.1
SuperGPQA	61.7	56.4	-	62.3	-	60.7	64.9
Reasoning
AIME25	87.5	92.7*	88.9*	88.0	75.5	81.5	92.3
HMMT25	79.4	66.7	77.5	82.5	58.3	62.5	83.9
LiveBench 20241125	74.7	75.8	78.3	82.4	78.2	77.1	78.4
HLE	17.7#	18.1*	20.3	21.6	10.7	11.8#	18.2#
Coding
LiveCodeBench v6 (25.02-25.05)	68.7	71.8	58.6	72.5	48.9	55.7	74.1
CFEval	2099	1929	2043	2001	-	2056	2134
OJBench	33.6	33.3	25.4	38.9	-	25.6	32.5
Alignment
IFEval	79.1	92.4	92.1	90.8	89.7	83.4	87.8
Arena-Hard v2$	72.2	59.3	80.8	72.5	59.1	61.5	79.7
Creative Writing v3	86.3	78.8	87.7	85.9	83.8	84.6	86.1
WritingBench	83.2	78.4	85.3	83.1	79.1	80.3	88.3
Agent
BFCL-v3	63.8	67.2	72.4	67.2	61.8	70.8	71.9
TAU2-Retail	64.9	71.0	76.3	71.3	-	40.4	71.9
TAU2-Airline	60.0	59.0	70.0	60.0	-	30.0	58.0
TAU2-Telecom	33.3	42.0	60.5	37.4	-	21.9	45.6
Multilingualism
MultiIF	63.5	78.0	80.3	77.8	-	71.9	80.6
MMLU-ProX	80.6	79.0	83.3	84.7	-	80.0	81.0
INCLUDE	79.4	80.8	86.6	85.1	-	78.7	81.0
PolyMATH	46.9	48.7	49.7	52.2	-	54.7	60.1

* For OpenAI O4-mini and O3, we use a medium reasoning effort, except for scores marked with *, which are generated using high reasoning effort.

# According to the official evaluation criteria of HLE, scores marked with # refer to models that are not multi-modal and were evaluated only on the text-only subset.

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

& For highly challenging tasks (including PolyMATH and all reasoning and coding tasks), we use an output length of 81,920 tokens. For all other tasks, we set the output length to 32,768.