AI Model Profile
Qwen3-235B-A22B
Qwen3-235B-A22B is a flagship Qwen3 mixture-of-experts model described in Qwen release materials as open-weight and useful across coding, math, general capabilities, and agent workflows.
Verification & Sources
- Status
- Verified
- Source links
- 2
- Freshness
- Verified June 18, 2026
- Last verified
- June 18, 2026
- Last updated
- June 17, 2026
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Benchmark Caveat
Provider documentation and provider-published capability notes are directional, not universal rankings. Real results depend on prompts, tools, latency targets, pricing tier, safety filters, context length, and workload mix.
Best for
Candidate for open-weight, local/private, and research evaluations where teams can manage deployment complexity.
Skip if
Skip if your team needs independently benchmarked performance claims or contractual guarantees not present in the official provider documentation.
Strengths
Official Qwen materials describe open weights, multiple deployment paths, hybrid thinking modes, and large context.
Weaknesses
Deployment cost, latency, and hardware needs vary widely by serving stack and quantization.
Agent suitability
Candidate for agent experiments because Qwen materials describe tool and agent capabilities.
Kingy AI take
Use this profile as a source-backed reference point, not a ranking. Re-check official provider docs before making production or budget decisions.
Full Model Notes
Qwen3-235B-A22B is a flagship Qwen3 mixture-of-experts model described in Qwen release materials as open-weight and useful across coding, math, general capabilities, and agent workflows.
Coding notes
Official Qwen materials describe coding capability in the Qwen3 release.
Reasoning notes
Qwen materials describe thinking and non-thinking modes for task-specific reasoning control.
Creative notes
Primarily text-focused in this profile; not a media-generation model.
Research notes
Candidate for open-weight research, local deployment tests, and long-context experiments.
License notes
Qwen's Qwen3 release post describes Qwen3 open-weight models under Apache 2.0; verify exact model-card terms before redistribution.
Hardware requirements
Official Qwen materials point to deployment frameworks and local tools; actual hardware depends on model variant, quantization, and serving stack.
