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 | 7B |
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 | 32k |
| Overview | ||
| Company | DeepSeek | Mistral |
| Release date | Jun 17 2024 | Jul 16 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek Coder V2vsClaude Haiku 5.5Mathstral 7BvsClaude Haiku 5.5DeepSeek Coder V2vsGPT-6.1 SolMathstral 7BvsGPT-6.1 SolDeepSeek Coder V2vsGemini 4 ArgonMathstral 7BvsGemini 4 ArgonDeepSeek Coder V2vsMuse Spark OpenMathstral 7BvsMuse Spark OpenDeepSeek Coder V2vsGrok 4.7Mathstral 7BvsGrok 4.7DeepSeek Coder V2vsKimi K3Mathstral 7BvsKimi K3Frequently asked questions
DeepSeek Coder V2 and Mathstral 7B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek Coder V2 shipped 29 days before Mathstral 7B, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder V2 has 16B/236B parameters, while Mathstral 7B has 7B. Context windows are 128k (DeepSeek Coder V2) vs 32k (Mathstral 7B).
Direct benchmark comparisons are unavailable — DeepSeek Coder V2 and Mathstral 7B don't publish scores on any of the same benchmarks.