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/13B/34B |
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 | 16k |
| Overview | ||
| Company | DeepSeek | Meta |
| Release date | Jun 17 2024 | Aug 24 2023 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
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DeepSeek Coder V2 and Code Llama don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama shipped 298 days before DeepSeek Coder V2, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder V2 has 16B/236B parameters, while Code Llama has 7B/13B/34B. Context windows are 128k (DeepSeek Coder V2) vs 16k (Code Llama).
Direct benchmark comparisons are unavailable — DeepSeek Coder V2 and Code Llama don't publish scores on any of the same benchmarks.