The core technical features of Qwen3-235B-A22B-Thinking-2507 include the following:
- Hybrid Expert Architecture (MoE): The model uses an advanced hybrid expert architecture with 235 billion total parameters, but only 22 billion of them are activated per inference, achieving a balance between performance and efficiency.
- Extremely long context support: Supports context lengths of up to 256K (262,144) tokens, enabling it to handle complex document and multi-round dialog tasks.
- Powerful reasoning: Optimized for logical reasoning, mathematical, scientific, and academic tasks, capable of outputting step-by-step reasoning processes that include labels.
- Multi-language support: Support for more than 100 languages, suitable for multilingual command following and translation tasks.
- Efficient deployment: A quantized version of FP8 is provided, which significantly reduces hardware requirements, optimizes inference performance, and is compatible with a variety of inference frameworks such as transformers, sglang, and vLLM.
This answer comes from the articleQwen3-235B-A22B-Thinking-2507: A large-scale language model to support complex reasoningThe
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