Mistral Medium 3.5 vs Qwen 3.6 Plus

Choose Mistral Medium 3.5 if you want…

• ✓Faster output (~132.8 tokens/sec)

Choose Qwen 3.6 Plus if you want…

• ✓Higher intelligence (Artificial Analysis index 40)
• ✓Lower price ($1.13 / 1M blended)
• ✓Larger context window (1M)

Mistral Medium 3.5

Mistral Medium 3.5 is Mistral's multimodal flagship on just4o.chat, served through the Vercel AI Gateway as mistral/mistral-medium-3.5. It combines frontier text reasoning with native image understanding in a single model, so you can hand it a screenshot, chart, or document image alongside your prompt and get a grounded answer back. A 256k-token context window handles long inputs comfortably, and function calling makes it suitable for agentic and tool-driven work. It is a Premium model on just4o.chat, billed at 3 premium requests per send before length multipliers, reflecting its higher provider output price of $7.50 per million tokens ($1.50 per million input). The honest trade-offs: no native web search, and as a vision-capable generalist rather than a dedicated reasoning model, it does not expose a step-by-step thinking budget the way Magistral does. For multimodal analysis, visual document work, and high-quality general assistance where image input matters, it is the model to reach for.

Qwen 3.6 Plus

At $0.50 per million input tokens, Qwen 3.6 Plus punches well above its price band — scoring 78.8 on SWE-bench Verified and 61.6 on Terminal-Bench 2.0, where it outpaces Claude 4.5 Opus on agentic coding tasks. The 1 million token context window lets you drop in entire codebases for security audits, multi-file refactors, or long-horizon agent sessions without chunking or worrying about cost. Always-on chain-of-thought reasoning is baked into the architecture rather than toggled per request, and native tool-calling makes it well-suited for multi-step workflows. Developers building high-volume API applications have reported generating hundreds of millions of tokens during its preview period — its first-day usage crossed one trillion tokens across platforms. That said, the long context is not a silver bullet: retrieval accuracy degrades in the middle of very long inputs, and real-world testing has surfaced instruction-following inconsistencies and occasional tool-calling failures that more mature providers handle more reliably. For cost-sensitive production deployments where coding and document analysis are the core workload, few models compete at this price.