Mistral Medium 3.5 vs Qwen3.5-397B-A17B

On shared benchmark coverage, MMLU PRO has Mistral Medium 3.5 at 79.8 and Qwen3.5-397B-A17B at 87.8, with Qwen3.5-397B-A17B ahead by 8 points; SWE-bench Verified has Mistral Medium 3.5 at 77.6 and Qwen3.5-397B-A17B at 76.2, with Mistral Medium 3.5 ahead by 1.4 points; Google-Proof Q&A has Mistral Medium 3.5 at 71.3 and Qwen3.5-397B-A17B at 89.3, with Qwen3.5-397B-A17B ahead by 18 points. The largest visible gap is 18 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 is close: both models cover vision, multimodal input, reasoning mode, function calling, and tool use. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.

For cost, Mistral Medium 3.5 lists $1.50/1M input and $7.50/1M output tokens on the cheapest tracked provider, while Qwen3.5-397B-A17B lists $0.39/1M input and $2.34/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-397B-A17B lower by about $2.33 per million blended tokens. Availability is 3 providers versus 4, so concentration risk also matters.

Choose Mistral Medium 3.5 when vision-heavy evaluation are central to the workload. Choose Qwen3.5-397B-A17B when vision-heavy evaluation, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.