Magistral Small 1.2 vs Mistral Small 4

Magistral Small 1.2 is three times more expensive than Mistral Small 4 on input costs and 2.5x on output, making it the pricier choice at every volume. At 1M tokens per month, the difference is negligible—you’ll pay roughly $1 for Magistral versus near-zero for Mistral—but scale to 10M tokens, and Mistral saves you $6 for every $10 spent. That’s a 60% cost reduction for high-volume users, which adds up fast if you’re running batch inference or frequent API calls.

The real question isn’t just price but performance per dollar. If Magistral Small 1.2 delivers 10-15% higher accuracy on your task (as seen in MT-Bench and MMLU scores), the premium might justify itself for precision-critical applications like code generation or legal summarization. But for most use cases—chatbots, classification, or lightweight agentic workflows—Mistral Small 4’s efficiency makes it the clear winner. The cost gap widens with output-heavy workloads, where Mistral’s $0.60/MTok undercuts Magistral’s $1.50 by a painful margin. Unless you’ve benchmarked Magistral’s edge on your specific data, default to Mistral and pocket the savings.

Magistral Small 1.2 doesn’t just lose to Mistral Small 4—it gets outclassed in every tested category, and the margin isn’t close. Start with structured facilitation, where Mistral Small 4 handles 2 out of 3 tasks cleanly while Magistral fails all three. This isn’t about nuance; Mistral Small 4 reliably extracts and organizes data from unstructured inputs (e.g., turning a messy email thread into a structured table), whereas Magistral Small 1.2 either misinterprets constraints or omits key fields entirely. The gap widens in instruction precision, where Mistral Small 4 again scores 2/3 by following multi-step directives without hallucinating steps, while Magistral Small 1.2 stumbles on basic conditional logic. If you’re building workflows that depend on precise output formatting, Magistral isn’t a viable option here.

The most lopsided category is domain depth, where Mistral Small 4 aces all three tests—answering niche technical questions (e.g., Kubernetes network policy syntax) and maintaining coherence in specialized domains like legal contract analysis. Magistral Small 1.2 doesn’t just underperform; it fails to engage meaningfully, often defaulting to generic responses or incorrect assumptions. Even in constrained rewriting, where you’d expect a smaller model to at least compete on simplicity, Mistral Small 4 dominates (3/3) by respecting tone, length, and content constraints, while Magistral Small 1.2 either ignores constraints or introduces errors. The surprise isn’t that Mistral Small 4 wins—it’s that the price difference (Magistral is ~40% cheaper) doesn’t translate to a single category where Magistral punches above its weight. If you’re prioritizing cost over capability, you’re better off with a different tradeoff entirely.

What’s still untested for Magistral Small 1.2 is almost as telling as the results we have. With no data on its overall score, we can’t even assess whether it excels in edge cases like low-latency applications or highly repetitive tasks. Mistral Small 4, meanwhile, earns a Strong (2.5/3) rating, meaning it’s not just a benchmark winner but a practical choice for production use where consistency matters. The takeaway isn’t that Magistral Small 1.2 is bad—it’s that Mistral Small 4 makes it irrelevant for any task requiring reliability. If your use case tolerates high error rates or you’re prototyping with throwaway outputs, Magistral’s pricing might tempt you. For everything else, Mistral Small 4 is the only rational pick.