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. | 236B | 8B/70B |
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 | 8k |
| Overview | ||
| Company | DeepSeek | Meta |
| Release date | Sep 5 2024 | Apr 18 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V2.5vsClaude Haiku 5.5LLaMA 3 (8B/70B)vsClaude Haiku 5.5DeepSeek V2.5vsGPT-6.1 SolLLaMA 3 (8B/70B)vsGPT-6.1 SolDeepSeek V2.5vsGemini 4 ArgonLLaMA 3 (8B/70B)vsGemini 4 ArgonDeepSeek V2.5vsGrok 4.7LLaMA 3 (8B/70B)vsGrok 4.7DeepSeek V2.5vsMistral Large 4LLaMA 3 (8B/70B)vsMistral Large 4DeepSeek V2.5vsKimi K3LLaMA 3 (8B/70B)vsKimi K3Frequently asked questions
DeepSeek V2.5 and LLaMA 3 (8B/70B) don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3 (8B/70B) shipped 140 days before DeepSeek V2.5, so benchmark comparisons should account for the intervening progress.
DeepSeek V2.5 has 236B parameters, while LLaMA 3 (8B/70B) has 8B/70B. Context windows are 128k (DeepSeek V2.5) vs 8k (LLaMA 3 (8B/70B)).
Direct benchmark comparisons are unavailable — DeepSeek V2.5 and LLaMA 3 (8B/70B) don't publish scores on any of the same benchmarks.