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 | 2B/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 | 8k |
| Overview | ||
| Company | DeepSeek | |
| Release date | Jun 17 2024 | Feb 21 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek Coder V2vsClaude Haiku 5.5Gemma 1vsClaude Haiku 5.5DeepSeek Coder V2vsGPT-6.1 SolGemma 1vsGPT-6.1 SolDeepSeek Coder V2vsMuse Spark OpenGemma 1vsMuse Spark OpenDeepSeek Coder V2vsGrok 4.7Gemma 1vsGrok 4.7DeepSeek Coder V2vsMistral Large 4Gemma 1vsMistral Large 4DeepSeek Coder V2vsKimi K3Gemma 1vsKimi K3Frequently asked questions
DeepSeek Coder V2 and Gemma 1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemma 1 shipped 117 days before DeepSeek Coder V2, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder V2 has 16B/236B parameters, while Gemma 1 has 2B/7B. Context windows are 128k (DeepSeek Coder V2) vs 8k (Gemma 1).
Direct benchmark comparisons are unavailable — DeepSeek Coder V2 and Gemma 1 don't publish scores on any of the same benchmarks.