Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 24B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 2M | 256k |
| These models have no shared benchmark scores. | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 68% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 55.7% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 22.5% |
| Overview | ||
| Company | Mistral | |
| Release date | Feb 5 2025 | Dec 9 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
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Gemini 2.0 Pro and Devstral Small 2 (24B) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 2.0 Pro shipped 307 days before Devstral Small 2 (24B), so benchmark comparisons should account for the intervening progress.
Context windows are 2M (Gemini 2.0 Pro) vs 256k (Devstral Small 2 (24B)). Gemini 2.0 Pro is closed, while Devstral Small 2 (24B) is open weight.
Direct benchmark comparisons are unavailable — Gemini 2.0 Pro and Devstral Small 2 (24B) don't publish scores on any of the same benchmarks.