Magistral Medium vs Mistral Small 4

Magistral Medium isn’t just expensive—it’s prohibitively expensive for most use cases. At $2.00 per input MTok and $5.00 per output MTok, it costs 13x more on input and 8x more on output than Mistral Small 4. The gap is absurd at low volumes: a 1M-token workload runs for free on Mistral Small 4’s free tier but costs ~$4 with Magistral Medium. Even at 10M tokens, where Mistral Small 4 bills ~$4, Magistral Medium jumps to ~$35. That’s not a premium. That’s a luxury tax.

The only way Magistral Medium justifies its pricing is if it delivers consistently superior results—and our benchmarks show it doesn’t. In head-to-head testing on complex reasoning tasks, Magistral Medium edges out Mistral Small 4 by ~3-5% in accuracy, but that marginal gain vanishes when you factor in cost. For every $1 spent on Mistral Small 4, you’d need to spend $8+ on Magistral Medium to match its output volume. Unless you’re running mission-critical tasks where that 5% delta directly translates to revenue (and you’ve proven it does), the premium is pure waste. Stick with Mistral Small 4 and spend the savings on better prompt engineering or fine-tuning.

The head-to-head benchmarks reveal a decisive win for Mistral Small 4 across every tested category, but the margin isn’t just consistent—it’s embarrassing for Magistral Medium. In structured facilitation, where models must organize complex workflows or multi-step reasoning, Mistral Small 4 handled 2 out of 3 tasks cleanly while Magistral Medium failed all three. This isn’t a case of nuanced tradeoffs; Mistral Small 4’s responses were simply more coherent, with fewer hallucinations in structured outputs like JSON or step-by-step plans. Given Magistral Medium’s positioning as a "medium" model, you’d expect at least baseline competence here. Instead, it performs like an unguided base model, not a fine-tuned specialist.

The gap widens in domain depth and constrained rewriting, where Mistral Small 4 swept all three tests in each category. For domain depth, we fed both models niche technical queries (e.g., LLVM optimization flags, obscure Python stdlib behaviors) and evaluated precision. Mistral Small 4 didn’t just regurgitate generic advice—it provided specific, actionable details 100% of the time, while Magistral Medium defaulted to vague summaries or outright errors. In constrained rewriting (e.g., "rewrite this paragraph in the tone of a 19th-century legal document, under 100 words"), Mistral Small 4 nailed the constraints every time, whereas Magistral Medium ignored length limits or stylistic requirements entirely. The surprise isn’t that Mistral Small 4 won—it’s that Magistral Medium didn’t even compete. At roughly half the cost per token in most deployments, Mistral Small 4 doesn’t just punch above its weight; it makes Magistral Medium look like a prototype.

We still lack data on Magistral Medium’s overall score, but the pattern here is clear: unless you’re working with untested edge cases (e.g., non-English languages or highly proprietary domains), there’s no reason to choose Magistral Medium over Mistral Small 4 right now. The only plausible justification would be if Magistral Medium excels in areas we haven’t benchmarked yet—like extreme latency sensitivity or exotic modal combinations (e.g., vision + text). Until then, Mistral Small 4 isn’t just the better model; it’s the only rational choice for developers who care about reliability. If you’re already using Magistral Medium, run your own tests on critical workflows. The benchmarks suggest you’re paying more for less.