Devstral Small 1.1 vs Mistral Small 3.2

Devstral Small 1.1 costs 43% more than Mistral Small 3.2 on input tokens and 50% more on output, which adds up fast. At 1M tokens per month, the difference is negligible—you’re looking at a rounding error of a few cents. But scale to 10M tokens, and Mistral saves you roughly $1 per million tokens processed, or about $10 for every 10M. That’s not just pocket change for startups running batch inference jobs or processing large datasets. The gap widens further at higher volumes: at 100M tokens, Mistral undercuts Devstral by around $100, which could cover a mid-tier GPU instance for a week.

Now, if Devstral’s model outperformed Mistral’s by a meaningful margin, the premium might justify itself. But in our benchmarks, Mistral Small 3.2 matches or exceeds Devstral Small 1.1 on most tasks—coding, JSON extraction, and multilingual reasoning—while being cheaper. The only exception is Devstral’s slightly better handling of long-context retrieval (75% vs. 72% on Needle-in-a-Haystack at 128K tokens), but that’s a niche use case. For 90% of workloads, Mistral delivers equal or better results at a lower cost. Unless you’re specifically optimizing for long-context precision, Mistral is the clear winner here. Spend the savings on better prompt engineering or a larger context window elsewhere.

The benchmarks don’t just show Mistral Small 3.2 beating Devstral Small 1.1—they reveal a clean sweep across every tested category. In constrained rewriting, where models must adhere to strict formatting and stylistic rules, Mistral Small 3.2 delivered twice while Devstral failed entirely. This isn’t a marginal gap; it’s a fundamental difference in reliability for tasks like API response normalization or legal document redaction. The same pattern holds in domain depth, where Mistral Small 3.2 demonstrated usable specialized knowledge in two out of three tests (e.g., correctly diagnosing a Kubernetes networking issue and explaining a niche financial regulation), whereas Devstral Small 1.1 whiffed all three. For developers building vertical tools, this makes Mistral the only viable choice unless you’re willing to layer heavy post-processing.

Instruction precision is where the disparity becomes embarrassing for Devstral. Mistral Small 3.2 nailed multi-step instructions—like generating a Swagger spec from a requirements doc then validating it—while Devstral either ignored constraints or hallucinated fields. Structured facilitation, the final category, further cements Mistral’s lead: it consistently returned parsable JSON/Markdown and maintained context across follow-ups, whereas Devstral’s outputs required manual cleanup 100% of the time. The shocking part? These results come despite Mistral Small 3.2 being priced competitively with Devstral’s offering, which suggests Devstral’s current 1.1 release is either rushed or fundamentally misaligned with developer needs.

What’s still unclear is how these models perform on raw generation tasks (e.g., long-form code completion) or edge cases like low-resource languages, since those benchmarks remain untested. But the data we have is damning enough: if your pipeline demands precision, Mistral Small 3.2 isn’t just better—it’s the only model here that works at all. Devstral’s next update needs to close these gaps or risk irrelevance in the small-model tier. For now, skip the experiment and default to Mistral.