Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Mistral Medium 3.5 | Qwen3.7 Max |
|---|---|---|
| Input ($/M tokens) | $1.5 | $2.5 |
| Output ($/M tokens) | $7.5 | $7.5 |
Verdict. Qwen3.7 Max takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Both models sit in the mid-tier bracket for output-token pricing. At 1.0× the per-million-token cost, Qwen3.7 Max is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Qwen3.7 Max makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Mistral Medium 3.5 is strongest on Coding Index (46.9), Intelligence Index (30.4). Qwen3.7 Max leads on Coding Index (66.0), Intelligence Index (46.7).
Speed. On throughput, Qwen3.7 Max generates tokens at 212 tok/s versus 132 tok/s — about 38% faster. On time-to-first-token, Qwen3.7 Max responds in 2240ms vs 2280ms, which matters most for chat-style UIs.
Provider. Mistral and Alibaba sell to overlapping but distinct developer audiences: Mistral tends to ship frontier reasoning models with premium positioning, while Alibaba often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Mistral Medium 3.5 costs $157.50 ($1890/year); Qwen3.7 Max costs $187.50 ($2250/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Mistral Medium 3.5 ≈ $22.50/run, Qwen3.7 Max ≈ $27.50/run. At agent/realtime scale (200M input / 100M output per million requests): Mistral Medium 3.5 ≈ $1050/run, Qwen3.7 Max ≈ $1250/run. Mistral Medium 3.5 becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Mistral Medium 3.5 is cheaper overall. Its blended price (3:1 input/output ratio) is $3.00/M tokens vs $3.75/M for Qwen3.7 Max.
Qwen3.7 Max wins 2 out of 12 benchmarks compared to 0 for Mistral Medium 3.5. See the detailed benchmark chart above for per-category results.
Qwen3.7 Max generates tokens faster at 212 tok/s vs 132 tok/s. However, Qwen3.7 Max has lower time-to-first-token (2.24s vs 2.28s).
Choose based on your priorities: Mistral Medium 3.5 for lower cost, Qwen3.7 Max for stronger benchmark performance, and Qwen3.7 Max for faster generation. For latency-sensitive apps, check the TTFT comparison above.