Qwen3-235B-A22B-Instruct-2507

Qwen3-235B-A22B-Instruct-2507 is an updated version of the flagship MoE model in the Qwen 3 series. This 235-billion-parameter model activates only 22 billion parameters at each inference step. The architecture consists of 94 transformer layers with 128 experts, of which only 8 are activated per token.

Unlike previous versions of Qwen, the new 2507 model has completely abandoned the hybrid thinking mode in favor of a highly optimized non-thinking mode. This decision was made based on user feedback indicating a preference for faster responses without the generation of <think> blocks. As a result, response speed has dramatically increased, along with significant improvements in output quality. In mathematical benchmarks, the model achieved remarkable gains: AIME25 (70.3 vs. 24.7 in the previous version) and HMMT25 (55.4 vs. 10.0). Particularly impressive is its performance on ZebraLogic (95.0), demonstrating near-perfect accuracy in logical reasoning tasks. In programming, the model also significantly outperforms its predecessor, achieving state-of-the-art results on LiveCodeBench and MultiPL-E. Overall, across numerous benchmarks, the model surpasses leading competitors such as GPT-4o, DeepSeek-V3, and Kimi K2.

Additionally, developers have released Qwen3-235B-A22B-Instruct-2507-FP8—an FP8 quantized version of the model. This innovative technique reduces memory requirements by approximately 50% while preserving nearly all of the original model's quality. The FP8 format outperforms traditional INT8 approaches, especially for large-scale models, offering a superior balance between accuracy and efficiency.

Another key technological advancement of Qwen3-235B-A22B-Instruct-2507 is native support for a context length of 262,144 tokens. This capability enables entirely new use cases—from analyzing lengthy documents and codebases to conducting multi-hour conversations while maintaining contextual understanding and high response accuracy even with a fully filled context window. Therefore, there are strong grounds to believe that the new Qwen3-235B-A22B-Instruct-2507 model positions itself as the leading open-source solution for a broad range of enterprise applications.

Announce Date: 21.07.2025
Parameters: 235B
Experts: 128
Activated at inference: 22B
Context: 263K
Layers: 94
Attention Type: Full or Sliding Window Attention
Developer: Qwen
Transformers Version: 4.51.0
vLLM Version: 0.8.5
License: Apache 2.0

Use our pre-built public endpoints for free to test inference and explore Qwen3-235B-A22B-Instruct-2507 capabilities. You can obtain an API access token on the token management page after registration and verification.

An endpoint is a server with pre-installed software that automatically downloads neural network weights and launches an OpenAI-compatible API to handle incoming requests.

A private endpoint is created within the user's project, belongs exclusively to the user, and is billed per server configuration in accordance with the infrastructure pricing.

We recommend creating private endpoints when you need to:

• achieve maximum endpoint performance,
• access the full context window for processing large requests and conversations,
• ensure maximum security by processing data in a dedicated, isolated environment.

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