Qwen3 235B-A22B
Alibaba's flagship Qwen3. Competitive with GPT-4 class models.
Task Fit
Not marked for typed decisions in the current library.
Tool use, repo work, terminal workflows, and coding benchmarks.
Code generation, debugging, refactoring, and benchmark signal.
General writing, Q&A, and assistant use.
Document QA benefits from long context and instruction following.
Not marked for vision in the current library.
Not marked for image generation in the current library.
Not marked for video generation in the current library.
Not marked for voice in the current library.
Evidence level
How this is assessed →Memory and hardware-fit values are planning estimates unless a tested report is linked.
Variants and Quant Artifacts
Choose the artifact first; hardware fit follows from RAM, VRAM, format, and runtime.
| Quant | Format | Quality | Min RAM | Reco RAM | Runtime | Action |
|---|---|---|---|---|---|---|
| Q2_K | gguf | compact | 128GB | 192GB | ollama, llama.cpp, lm-studio | Plan with this |
| Q3_K_M | gguf | compact | 128GB | 192GB | ollama, llama.cpp, lm-studio | Plan with this |
| Q4_K_M | gguf | balanced | 145GB | 192GB | ollama, llama.cpp, lm-studio | Plan with this |
Recommended Hardware
Lowest estimated 5-year cost that can run this model.
Plenty of fast-memory headroom for this model.
Highest local performance signal among compatible hardware.
Benchmarks
Sources and compatibility
Execution evidence
Run Qwen3 235B-A22B with a documented recipe
Recipes connect hardware, a model artifact, tools, settings, verification, and a reportable result.
No verified recipe is linked to this record yet.
Compatibility estimates remain available in the planner. A recipe appears here only after its exact stack and verification protocol are documented.
Similar Models
Qwen3.8-2.4T-A95B is Alibaba Qwen's open-weight, text-only flagship MoE model with 2.4T total parameters and 95B activated parameters. It uses mandatory thinking, supports tool use, has a native 262K context window extensible to about 1.01M tokens, and ships in official BF16 and FP8 repositories. Its multi-terabyte weights target distributed data-center inference with vLLM, SGLang, or TokenSpeed rather than consumer local hardware.
Qwen3.8-27B is Alibaba Qwen's Apache 2.0 open-weight dense vision-language model for coding, professional work, research, and long-horizon agents. It has 27B parameters, a native 262K context window extensible to 1M tokens, flexible reasoning effort, tool use, official BF16 and FP8 weights, and a broad community quantization ecosystem for consumer GPUs and Apple Silicon.
Qwen3.8-Flash-Next is Alibaba Qwen's open-weight multimodal MoE preview of the architecture planned for Qwen4. Its language model has 125B parameters with 6B activated per token, plus 51B n-gram embeddings and 4B MTP parameters. It combines Gated DeltaNet, Qwen Sparse Attention, gated residuals, vision input, tool use, flexible reasoning, a native 262K context window extensible to 1M tokens, and official serving support through vLLM, SGLang, KTransformers, and TokenSpeed.