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/33B/65B |
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 | 2k |
| Overview | ||
| Company | DeepSeek | Meta |
| Release date | Jun 17 2024 | Feb 24 2023 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek Coder V2vsClaude Haiku 5.5LLaMA 1vsClaude Haiku 5.5DeepSeek Coder V2vsGPT-6.1 SolLLaMA 1vsGPT-6.1 SolDeepSeek Coder V2vsGemini 4 ArgonLLaMA 1vsGemini 4 ArgonDeepSeek Coder V2vsGrok 4.7LLaMA 1vsGrok 4.7DeepSeek Coder V2vsMistral Large 4LLaMA 1vsMistral Large 4DeepSeek Coder V2vsKimi K3LLaMA 1vsKimi K3Frequently asked questions
DeepSeek Coder V2 and LLaMA 1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 1 shipped 479 days before DeepSeek Coder V2, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder V2 has 16B/236B parameters, while LLaMA 1 has 7B/13B/33B/65B. Context windows are 128k (DeepSeek Coder V2) vs 2k (LLaMA 1).
Direct benchmark comparisons are unavailable — DeepSeek Coder V2 and LLaMA 1 don't publish scores on any of the same benchmarks.