Qwen3 235B A22B: separate Qwen3-235B-A22B-Instruct-2507 and Thinking-2507 successors shipped in July 2025; this profile remains about the original release
Alibaba expands the Qwen3 ecosystem with dedicated instruction and reasoning variants for July 2025.
Separate Qwen3-235B-A22B-Instruct-2507 and Thinking-2507 successors shipped in July 2025; this profile remains about the original release — verified by the OnAirToday AI Progress Tracker against Alibaba's published documentation.
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The OnAirToday AI Progress Tracker recorded a verified change to Alibaba's Qwen3 235B A22B (version qwen3-235b-a22b): Separate Qwen3-235B-A22B-Instruct-2507 and Thinking-2507 successors shipped in July 2025; this profile remains about the original release.
Qwen3-235B-A22B is Alibaba's open-weight, text-based mixture-of-experts model, with 235 billion total parameters and 22 billion activated per token. This original release supports switchable thinking and non-thinking modes; separate Instruct-2507 and Thinking-2507 successors have since shipped.
What changed in this review:
- Context documentation distinguishes 32,768 native tokens from 131,072 tokens with YaRN.
- The original vendor evaluation reports GPQA Diamond at 71.1% and LiveCodeBench at 70.7%.
- Published AIME results are 85.7% for 2024 and 81.5% for 2025.
- Flaws and limitations updated
- Risks and threats updated
Teams building on Qwen3 235B A22B should confirm limits, pricing and benchmark coverage against the vendor's own documentation before relying on these figures.
The full profile — capabilities, benchmark citations, known flaws and risk guidance — is on the tracker at /ai-models/qwen3-235b-a22b.
References:
- https://huggingface.co/Qwen/Qwen3-235B-A22B
- https://qwenlm.github.io/blog/qwen3/
- https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507
- https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507
Generated insight by OnAirToday AI
The release of the Qwen3 series marks a significant step forward in Alibaba's capability to provide specialized instruction-tuned and reasoning-focused models at scale. By modularizing their architecture into specific use-case variants, they are setting a new standard for high-performance open-weight model deployment.

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