Qwen3-235B-A22B-Thinking-2507

Product Introduction

1. Qwen3-235B-A22B-Thinking-2507 is a state-of-the-art open-source Mixture-of-Experts (MoE) language model with 235 billion total parameters and 22 billion activated parameters, optimized for deep reasoning and complex problem-solving. It combines advanced architecture improvements with extended training to deliver superior performance in agentic tasks, academic benchmarks, and multi-step reasoning scenarios.
2. The core value of this model lies in its ability to handle highly sophisticated reasoning workflows while maintaining efficiency through sparse activation, making it suitable for both research and enterprise applications requiring scalable AI solutions.

Main Features

1. The model employs a MoE architecture with 128 experts and 8 activated experts per token, enabling efficient computation while scaling to 235 billion parameters for handling intricate reasoning tasks.
2. It natively supports a 256K-token context window, allowing seamless processing of long-form content such as academic papers, legal documents, or multi-step coding projects without information loss.
3. Enhanced tool integration capabilities enable direct API connectivity and compatibility with frameworks like Qwen-Agent for automated tool calling, code interpretation, and real-time data processing in agentic workflows.

Problems Solved

1. Addresses the growing need for AI systems capable of human-level reasoning in technical domains by achieving SOTA results on benchmarks like LiveCodeBench (74.1), CFEval (2134), and academic challenges including HMMT25 (83.9).
2. Serves AI researchers developing advanced reasoning systems, enterprise teams building complex AI agents, and developers creating applications requiring long-context understanding with precise instruction following.
3. Enables use cases such as automated scientific paper analysis, multi-modal data processing pipelines, competitive programming solutions, and enterprise-scale knowledge management systems with chain-of-thought verification.

Unique Advantages

1. Outperforms comparable models like Deepseek-R1 and Claude4 Opus in reasoning tasks through specialized architectural optimizations, including a 94-layer structure with grouped query attention (64 Q heads, 4 KV heads) for balanced compute efficiency.
2. Implements a unique "thinking mode" enforced through automatic chat template modifications, ensuring structured intermediate reasoning steps ( tokens) while maintaining compatibility with standard inference frameworks.
3. Combines open-source accessibility with commercial-grade performance, offering 81.1% accuracy on GPQA and 87.8% on IFEval benchmarks while supporting flexible deployment through Hugging Face, vLLM, and enterprise API endpoints.

Frequently Asked Questions (FAQ)

1. What hardware is required for deployment? The model requires GPU clusters with tensor parallelism (recommended 8-way TP) and at least 80GB VRAM per node for full 256K context utilization, though reduced context configurations can run on smaller setups.
2. How does the thinking mode affect output formatting? The model automatically generates intermediate reasoning steps marked with tokens, requiring post-processing to extract final answers while maintaining compatibility with standard chat templates through Jinja2 parsing.
3. Can it integrate with existing tooling frameworks? Yes, it supports native integration with Qwen-Agent for tool calling and provides OpenAI-compatible API endpoints when deployed through vLLM or SGLang, enabling drop-in replacement for existing AI pipelines.