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 | 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. | 4k | 128k |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Nov 29 2023 | Jun 6 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-LLMvsClaude Haiku 5.5Qwen2vsClaude Haiku 5.5DeepSeek-LLMvsGPT-6.1 SolQwen2vsGPT-6.1 SolDeepSeek-LLMvsGemini 4 ArgonQwen2vsGemini 4 ArgonDeepSeek-LLMvsMuse Spark OpenQwen2vsMuse Spark OpenDeepSeek-LLMvsGrok 4.7Qwen2vsGrok 4.7DeepSeek-LLMvsMistral Large 4Qwen2vsMistral Large 4Frequently asked questions
DeepSeek-LLM and Qwen2 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-LLM shipped 190 days before Qwen2, so benchmark comparisons should account for the intervening progress.
DeepSeek-LLM has 7B/67B parameters, while Qwen2 has 0.5B/1.5B/7B/57B/72B. Context windows are 4k (DeepSeek-LLM) vs 128k (Qwen2).
Direct benchmark comparisons are unavailable — DeepSeek-LLM and Qwen2 don't publish scores on any of the same benchmarks.