Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Mistral Large 3 | Qwen3.7 Plus |
|---|---|---|
| Input ($/M tokens) | $0.5 | $0.4 |
| Output ($/M tokens) | $1.5 | $1.6 |
Verdict. Qwen3.7 Plus 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 budget bracket for output-token pricing. At 0.9× the per-million-token cost, Mistral Large 3 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mistral Large 3 makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Mistral Large 3 is strongest on Coding Index (20.1), Intelligence Index (15.9). Qwen3.7 Plus leads on Coding Index (55.9), Intelligence Index (39.4).
Speed. On throughput, Mistral Large 3 generates tokens at 78 tok/s versus 56 tok/s — about 28% faster. On time-to-first-token, Mistral Large 3 responds in 1110ms vs 2130ms, 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 Large 3 costs $37.50 ($450/year); Qwen3.7 Plus costs $36.00 ($432/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Mistral Large 3 ≈ $5.50/run, Qwen3.7 Plus ≈ $5.20/run. At agent/realtime scale (200M input / 100M output per million requests): Mistral Large 3 ≈ $250/run, Qwen3.7 Plus ≈ $240/run. Qwen3.7 Plus 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.
Data from Artificial Analysis API — 12 benchmarks
Qwen3.7 Plus is cheaper overall. Its blended price (3:1 input/output ratio) is $0.70/M tokens vs $0.75/M for Mistral Large 3.
Qwen3.7 Plus wins 2 out of 12 benchmarks compared to 0 for Mistral Large 3. See the detailed benchmark chart above for per-category results.
Mistral Large 3 generates tokens faster at 78 tok/s vs 56 tok/s. Mistral Large 3 also has lower time-to-first-token (1.11s vs 2.13s).
Choose based on your priorities: Qwen3.7 Plus for lower cost, Qwen3.7 Plus for stronger benchmark performance, and Mistral Large 3 for faster generation. For latency-sensitive apps, check the TTFT comparison above.