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 | 0.5B/1.5B/7B/57B/72B |
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 | 128k |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Jun 17 2024 | Jun 6 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek Coder V2vsClaude Haiku 5.5Qwen2vsClaude Haiku 5.5DeepSeek Coder V2vsGPT-6.1 SolQwen2vsGPT-6.1 SolDeepSeek Coder V2vsGemini 4 ArgonQwen2vsGemini 4 ArgonDeepSeek Coder V2vsMuse Spark OpenQwen2vsMuse Spark OpenDeepSeek Coder V2vsGrok 4.7Qwen2vsGrok 4.7DeepSeek Coder V2vsMistral Large 4Qwen2vsMistral Large 4Frequently asked questions
DeepSeek Coder V2 and Qwen2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Qwen2 shipped 11 days before DeepSeek Coder V2, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder V2 has 16B/236B parameters, while Qwen2 has 0.5B/1.5B/7B/57B/72B. Context windows are 128k (DeepSeek Coder V2) vs 128k (Qwen2).
Direct benchmark comparisons are unavailable — DeepSeek Coder V2 and Qwen2 don't publish scores on any of the same benchmarks.