Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | GPT-4o mini | Mistral Small 4 (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0.15 | $0.15 |
| Output ($/M tokens) | $0.6 | $0.6 |
Verdict. GPT-4o mini and Mistral Small 4 (Non-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 1.0× the per-million-token cost, Mistral Small 4 (Non-reasoning) is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). Mistral Small 4 (Non-reasoning) makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. GPT-4o mini is strongest on Coding Index (11.4), Intelligence Index (6.7). Mistral Small 4 (Non-reasoning) leads on Intelligence Index (12.3).
Speed. On throughput, Mistral Small 4 (Non-reasoning) generates tokens at 137 tok/s versus 101 tok/s — about 26% faster. On time-to-first-token, Mistral Small 4 (Non-reasoning) responds in 810ms vs 1030ms, which matters most for chat-style UIs.
Provider. OpenAI and Mistral sell to overlapping but distinct developer audiences: OpenAI 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): GPT-4o mini costs $13.50 ($162/year); Mistral Small 4 (Non-reasoning) costs $13.50 ($162/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): GPT-4o mini ≈ $1.95/run, Mistral Small 4 (Non-reasoning) ≈ $1.95/run. At agent/realtime scale (200M input / 100M output per million requests): GPT-4o mini ≈ $90/run, Mistral Small 4 (Non-reasoning) ≈ $90/run.
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
Both models have similar pricing. Check the detailed breakdown above for input vs output token costs.
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 (Non-reasoning) generates tokens faster at 137 tok/s vs 101 tok/s. However, Mistral Small 4 (Non-reasoning) has lower time-to-first-token (0.81s vs 1.03s).
Choose based on your priorities: both are similarly priced, both perform similarly on benchmarks, and Mistral Small 4 (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.