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/67B | 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. | 4k | 16k |
| Overview | ||
| Company | DeepSeek | Meta |
| Release date | Nov 29 2023 | Aug 24 2023 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-LLMvsClaude Haiku 5.5Code LlamavsClaude Haiku 5.5DeepSeek-LLMvsGPT-6.1 SolCode LlamavsGPT-6.1 SolDeepSeek-LLMvsGemini 4 ArgonCode LlamavsGemini 4 ArgonDeepSeek-LLMvsGrok 4.7Code LlamavsGrok 4.7DeepSeek-LLMvsMistral Large 4Code LlamavsMistral Large 4DeepSeek-LLMvsKimi K3Code LlamavsKimi K3Frequently asked questions
DeepSeek-LLM 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 97 days before DeepSeek-LLM, so benchmark comparisons should account for the intervening progress.
DeepSeek-LLM has 7B/67B parameters, while Code Llama has 7B/13B/34B. Context windows are 4k (DeepSeek-LLM) vs 16k (Code Llama).
Direct benchmark comparisons are unavailable — DeepSeek-LLM and Code Llama don't publish scores on any of the same benchmarks.