Introduction
Qwen3-235B-A22B-Instruct-2507 is the latest flagship Mixture-of-Experts (MoE) large language model from Qwen (Alibaba), released in July 2025. With 235 billion parameters (22B activated per inference), it is engineered for superior performance in instruction following, logical reasoning, mathematics, science, coding, tool usage, and multilingual understanding. The model natively supports a massive 256K (262,144) token context window, making it highly effective for long-context applications and complex tasks.
Key highlights:
Outstanding performance in instruction following, reasoning, comprehension, math, science, programming, and tool use
Substantial gains in multilingual long-tail knowledge coverage
Enhanced alignment with user preferences for subjective and open-ended tasks
Non-thinking mode only (does not generate<think></think>blocks)
Benchmark Comparison
| Benchmark | Deepseek-V3 | GPT-4o | Claude Opus 4 | Kimi K2 | Qwen3-235B-A22B | Qwen3-235B-A22B-Instruct-2507 |
|---|---|---|---|---|---|---|
| MMLU-Pro | 81.2 | 79.8 | 86.6 | 81.1 | 75.2 | 83.0 |
| MMLU-Redux | 90.4 | 91.3 | 94.2 | 92.7 | 89.2 | 93.1 |
| GPQA | 68.4 | 66.9 | 74.9 | 75.1 | 62.9 | 77.5 |
| SuperGPQA | 57.3 | 51.0 | 56.5 | 57.2 | 48.2 | 62.6 |
| SimpleQA | 27.2 | 40.3 | 22.8 | 31.0 | 12.2 | 54.3 |
| CSimpleQA | 71.1 | 60.2 | 68.0 | 74.5 | 60.8 | 84.3 |
| AIME25 (Reasoning) | 46.6 | 26.7 | 33.9 | 49.5 | 24.7 | 70.3 |
| LiveCodeBench v6 | 45.2 | 35.8 | 44.6 | 48.9 | 32.9 | 51.8 |
| Arena-Hard v2 | 45.6 | 61.9 | 51.5 | 66.1 | 52.0 | 79.2 |
| WritingBench | 74.5 | 75.5 | 79.2 | 86.2 | 77.0 | 85.2 |
Qwen3-235B-A22B-Instruct-2507 shows significant improvements over its predecessor and is highly competitive with leading models such as GPT-4o, Claude Opus 4, and Kimi K2, particularly in reasoning, coding, and multilingual tasks.
Community & Social Feedback
Reddit r/LocalLLaMA:
- Users are enthusiastic about the improved non-thinking mode and overall quality, especially for those who prefer not to use chain-of-thought (CoT) reasoning.
- Some report slow local performance on large hardware, but quantized versions (Q4_K_XL, dwq, etc.) help accessibility.
- The model’s ability to handle full 256K context and its coding/reasoning benchmarks are widely praised.
- Community sentiment is positive, with many considering Qwen models among the best open-source LLMs.
Social Media (X/Twitter):
- The release is described as “outperforming Kimi-K2, DeepSeek-V3, and Claude-Opus4,” with notable improvements in long-context handling and multilingual coverage.
- Users highlight its enhanced alignment with user preferences and performance in subjective/open-ended tasks.
How to Try
You can try Qwen3-235B-A22B-Instruct-2507 for free at:
https://qwq32.com/free-models/qwen-qwen3-235b-a22b-07-25-free
References
Qwen3-235B-A22B-Instruct-2507 sets a new benchmark for open-source LLMs with its massive context window, exceptional multilingual and reasoning abilities, and strong community support.