Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Qwen3 VL 32B (Reasoning) | Mistral Small 4 (Reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.16 | $0.15 |
| Output ($/M tokens) | $0.64 | $0.6 |
Verdict. Qwen3 VL 32B (Reasoning) and Mistral Small 4 (Reasoning) split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.1× the per-million-token cost, Mistral Small 4 (Reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mistral Small 4 (Reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. Qwen3 VL 32B (Reasoning) is strongest on Intelligence Index (11.9). Mistral Small 4 (Reasoning) leads on Coding Index (26.6), Intelligence Index (11.5).
Speed. On throughput, Mistral Small 4 (Reasoning) generates tokens at 169 tok/s versus 88 tok/s — about 48% faster. On time-to-first-token, Mistral Small 4 (Reasoning) responds in 760ms vs 2560ms, 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 VL 32B (Reasoning) costs $14.40 ($173/year); Mistral Small 4 (Reasoning) costs $13.50 ($162/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Qwen3 VL 32B (Reasoning) ≈ $2.08/run, Mistral Small 4 (Reasoning) ≈ $1.95/run. At agent/realtime scale (200M input / 100M output per million requests): Qwen3 VL 32B (Reasoning) ≈ $96/run, Mistral Small 4 (Reasoning) ≈ $90/run. Mistral Small 4 (Reasoning) 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 Small 4 (Reasoning) is cheaper overall. Its blended price (3:1 input/output ratio) is $0.26/M tokens vs $0.28/M for Qwen3 VL 32B (Reasoning).
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.
Mistral Small 4 (Reasoning) generates tokens faster at 169 tok/s vs 88 tok/s. However, Mistral Small 4 (Reasoning) has lower time-to-first-token (0.76s vs 2.56s).
Choose based on your priorities: Mistral Small 4 (Reasoning) for lower cost, both perform similarly on benchmarks, and Mistral Small 4 (Reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.