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. | 16B/236B | 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. | 128k | 40k |
| Overview | ||
| Company | DeepSeek | Mistral |
| Release date | Jun 17 2024 | Jun 10 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek Coder V2vsClaude Haiku 5.5Magistral SmallvsClaude Haiku 5.5DeepSeek Coder V2vsGPT-6.1 SolMagistral SmallvsGPT-6.1 SolDeepSeek Coder V2vsGemini 4 ArgonMagistral SmallvsGemini 4 ArgonDeepSeek Coder V2vsMuse Spark OpenMagistral SmallvsMuse Spark OpenDeepSeek Coder V2vsGrok 4.7Magistral SmallvsGrok 4.7DeepSeek Coder V2vsKimi K3Magistral SmallvsKimi K3Frequently asked questions
DeepSeek Coder V2 and Magistral Small don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek Coder V2 shipped 358 days before Magistral Small, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder V2 has 16B/236B parameters, while Magistral Small has 24B. Context windows are 128k (DeepSeek Coder V2) vs 40k (Magistral Small). DeepSeek Coder V2 is open weight, while Magistral Small is closed.
Direct benchmark comparisons are unavailable — DeepSeek Coder V2 and Magistral Small don't publish scores on any of the same benchmarks.