Qwen3-235B-A22B-Thinking-2507

In the 2507 update, developers discontinued the hybrid mode, introducing two highly optimized versions of the flagship Qwen3-235B-A22B model. Qwen3-235B-A22B-Thinking-2507 is the dedicated Thinking version, featuring doubled reasoning length and significantly enhanced chain-of-thought algorithms. The model's architecture remains unchanged—it is still a Mixture-of-Experts (MoE) model with 235 billion total parameters and 128 experts, of which only 22 billion parameters and 8 experts are activated per token, ensuring computational efficiency while preserving the knowledge capacity of the full 235-billion-parameter system. Additionally, developers have implemented native support for a context length of 262,144 tokens, unlocking new possibilities for analyzing lengthy documents, codebases, and performing multi-step reasoning. Alongside the main version, an FP8-quantized model has also been released.

Evaluating the capabilities of Qwen3-235B-A22B-Thinking-2507, it demonstrates phenomenal performance improvements on benchmarks, particularly in agent-based tasks, where it achieves gains of up to 108% on TAU2-Telecom, 93% on TAU2-Airline, and 78% on TAU2-Retail compared to the previous version. In mathematical competitions, the model reaches 92.3% on AIME25, trailing only OpenAI's o4-mini (92.7%), while outperforming all others on HMMT25 with a score of 83.9%. In programming, the model sets new standards with a 74.1% score on LiveCodeBench v6. Similarly, in scientific reasoning, it achieves 81.1% on GPQA, surpassing Claude Opus 4 Thinking's 79.6%.

Qwen3-235B-A22B-Thinking-2507 is ideally suited for solving complex tasks requiring deep analysis: mathematical proofs and Olympiad-level problems, development of sophisticated algorithms and architectural designs, scientific research and data analysis, legal analysis and document drafting—along with many other applications where the emphasis is not on response speed, but on accuracy and logical coherence.

Announce Date: 25.07.2025
Parameters: 235B
Experts: 128
Activated: 22B
Context: 263K
Attention Type: Full or Sliding Window Attention
VRAM requirements: 154.3 GB using 4 bits quantization
Developer: Alibaba
Transformers Version: 4.51.0
License: Apache 2.0

Use our pre-built public endpoints to test inference and explore Qwen3-235B-A22B-Thinking-2507 capabilities.

Rent your own physically dedicated instance with hourly or long-term monthly billing.

We recommend deploying private instances in the following scenarios:

• maximize endpoint performance,
• enable full context for long sequences,
• ensure top-tier security for data processing in an isolated, dedicated environment,
• use custom weights, such as fine-tuned models or LoRA adapters.

Contact our dedicated neural networks support team at nn@immers.cloud or send your request to the sales department at sale@immers.cloud.