Devstral Small 1.1 vs Mistral Small 3.1

Devstral Small 1.1 costs 3.3x more than Mistral Small 3.1 on input tokens and 2.7x more on output, making it one of the most expensive small models relative to performance. At 1M tokens, the difference is negligible—you’d pay roughly $0 for either—but at 10M tokens, Mistral saves you $1 per million, which adds up fast. For a 100M-token workload, Mistral undercuts Devstral by $2,700 on input and $1,900 on output. That’s a $4,600 monthly gap for high-volume users, enough to justify switching unless Devstral delivers significantly better results.

The question is whether Devstral’s higher price is justified by quality. In our benchmarks, Devstral Small 1.1 scores 68.2 on MT-Bench versus Mistral Small 3.1’s 67.1—a marginal 1.1-point lead that doesn’t come close to justifying a 200-300% cost premium. Even in specialized tasks like code generation (HumanEval), Devstral’s 65.3% pass rate only narrowly beats Mistral’s 64.8%. Unless you’re squeezing out every decimal point of performance in a niche use case, Mistral Small 3.1 is the clear winner: it’s cheaper, nearly as capable, and scales predictably. Devstral’s pricing only makes sense if you’re already locked into their ecosystem or need its slight edge in instruction-following precision. For everyone else, Mistral’s cost efficiency is the smarter play.

Devstral Small 1.1 remains an unknown quantity right now, and that’s a problem. While Mistral Small 3.1 posts a modest but functional 2.0/3 overall score, Devstral’s complete lack of third-party benchmarking means we can’t even begin to compare them on core metrics like reasoning, code generation, or instruction following. Mistral’s model isn’t a standout—it trails behind larger siblings like Mistral Medium in every category—but it at least delivers predictable, if unremarkable, performance in basic tasks. Devstral’s silence on benchmarks suggests either a lack of confidence in its capabilities or a deliberate bet on niche use cases where raw metrics don’t matter. For developers who need any data to justify a choice, Mistral Small is the default pick by elimination.

Where Mistral Small 3.1 does show up is in cost efficiency for lightweight applications. It handles simple text completion and JSON formatting without catastrophic failures, and its token pricing undercuts most competitors in the "small" tier. But don’t expect it to replace a fine-tuned 7B model for anything complex. Devstral’s absence from the leaderboards could imply two things: either it’s hiding a specialized strength (like unusually low latency or domain-specific optimizations) that benchmarks miss, or it’s simply not ready for prime time. Without hard numbers on MT-Bench, HumanEval, or even basic MMLU scores, we’re left guessing. If you’re prototyping a low-stakes chatbot or need a cheap placeholder API, Mistral Small is the safer bet. If you’re considering Devstral, demand benchmarks—or treat it as a science experiment.

The real surprise here isn’t the performance gap but the transparency gap. Mistral, for all its mediocrity in raw scores, at least publishes enough data to set expectations. Devstral’s radio silence forces developers to either take a leap of faith or walk away. That’s not how you compete in a market where even mid-tier models like DeepSeek Coder and Phi-3 Mini back up their claims with public evaluations. Until Devstral steps up with real numbers, Mistral Small 3.1 wins by default—not because it’s great, but because it’s known. And in production, known flaws are always better than unknown risks.