Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 32B (Reasoning) | Mistral Small (Feb '24) |
|---|---|---|
| Input ($/M tokens) | $0.16 | $0.15 |
| Output ($/M tokens) | $0.64 | $0.6 |
Verdict. Qwen3 32B (Reasoning) wins the overall benchmark matchup 2–0 across 2 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.1× the per-million-token cost, Mistral Small (Feb '24) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mistral Small (Feb '24) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3 32B (Reasoning) is strongest on Coding Index (15.3), Intelligence Index (11.4). Mistral Small (Feb '24) leads on Intelligence Index (3.2).
Speed. On throughput, Mistral Small (Feb '24) generates tokens at 150 tok/s versus 101 tok/s — about 32% faster. On time-to-first-token, Mistral Small (Feb '24) responds in 780ms vs 2460ms, which matters most for chat-style UIs.
Provider. Alibaba and Mistral sell to overlapping but distinct developer audiences: Alibaba tends to ship frontier reasoning models with premium positioning, while Mistral 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): Qwen3 32B (Reasoning) costs $14.40 ($173/year); Mistral Small (Feb '24) costs $13.50 ($162/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 32B (Reasoning) ≈ $2.08/run, Mistral Small (Feb '24) ≈ $1.95/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 32B (Reasoning) ≈ $96/run, Mistral Small (Feb '24) ≈ $90/run. Mistral Small (Feb '24) 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
Mistral Small (Feb '24) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.26/M tokens vs $0.28/M for Qwen3 32B (Reasoning).
Qwen3 32B (Reasoning) wins 2 out of 12 benchmarks compared to 0 for Mistral Small (Feb '24). See the detailed benchmark chart above for per-category results.
Mistral Small (Feb '24) generates tokens faster at 150 tok/s vs 101 tok/s. However, Mistral Small (Feb '24) has lower time-to-first-token (0.78s vs 2.46s).
Choose based on your priorities: Mistral Small (Feb '24) for lower cost, Qwen3 32B (Reasoning) for stronger benchmark performance, and Mistral Small (Feb '24) for faster generation. For latency-sensitive apps, check the TTFT comparison above.