Mistral Large 3 675B Instruct vs Qwen3-235B-A22B

On shared benchmark coverage, MMLU PRO has Mistral Large 3 675B Instruct at 85.5 and Qwen3-235B-A22B at 82.8, with Mistral Large 3 675B Instruct ahead by 2.7 points; Google-Proof Q&A has Mistral Large 3 675B Instruct at 43.9 and Qwen3-235B-A22B at 86.1, with Qwen3-235B-A22B ahead by 42.2 points; LiveCodeBench has Mistral Large 3 675B Instruct at 82.8 and Qwen3-235B-A22B at 80.4, with Mistral Large 3 675B Instruct ahead by 2.4 points. The largest visible gap is 42.2 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on vision: Mistral Large 3 675B Instruct and multimodal input: Mistral Large 3 675B Instruct. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, Mistral Large 3 675B Instruct lists $0.50/1M input and $1.50/1M output tokens on the cheapest tracked provider, while Qwen3-235B-A22B lists $0.09/1M input and $0.58/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3-235B-A22B lower by about $0.56 per million blended tokens. Availability is 6 providers versus 5, so concentration risk also matters.

Choose Mistral Large 3 675B Instruct when vision-heavy evaluation and broader provider choice are central to the workload. Choose Qwen3-235B-A22B when provider fit and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.