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. | 7B/13B/34B | 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. | 16k | 128k |
| 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. | — | 46.8% |
| Overview | ||
| Company | Meta | Mistral |
| Release date | Aug 24 2023 | May 21 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Code LlamavsClaude Haiku 5.5Devstral SmallvsClaude Haiku 5.5Code LlamavsGPT-6.1 SolDevstral SmallvsGPT-6.1 SolCode LlamavsGemini 4 ArgonDevstral SmallvsGemini 4 ArgonCode LlamavsGrok 4.7Devstral SmallvsGrok 4.7Code LlamavsDeepSeek-V4.1-Flash-1017Devstral SmallvsDeepSeek-V4.1-Flash-1017Code LlamavsKimi K3Devstral SmallvsKimi K3Frequently asked questions
Code Llama and Devstral Small don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 636 days before Devstral Small, so benchmark comparisons should account for the intervening progress.
Code Llama has 7B/13B/34B parameters, while Devstral Small has 24B. Context windows are 16k (Code Llama) vs 128k (Devstral Small).
Direct benchmark comparisons are unavailable — Code Llama and Devstral Small don't publish scores on any of the same benchmarks.