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. | 1.3B/5.7B/6.7B/33B | — |
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. | 16k | 32k |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Nov 2 2023 | Jan 28 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek CodervsClaude Haiku 5.5Qwen2.5-MaxvsClaude Haiku 5.5DeepSeek CodervsGPT-6.1 SolQwen2.5-MaxvsGPT-6.1 SolDeepSeek CodervsGemini 4 ArgonQwen2.5-MaxvsGemini 4 ArgonDeepSeek CodervsMuse Spark OpenQwen2.5-MaxvsMuse Spark OpenDeepSeek CodervsGrok 4.7Qwen2.5-MaxvsGrok 4.7DeepSeek CodervsMistral Large 4Qwen2.5-MaxvsMistral Large 4Frequently asked questions
DeepSeek Coder and Qwen2.5-Max don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek Coder shipped 453 days before Qwen2.5-Max, so benchmark comparisons should account for the intervening progress.
Context windows are 16k (DeepSeek Coder) vs 32k (Qwen2.5-Max). DeepSeek Coder is open weight, while Qwen2.5-Max is closed.
Direct benchmark comparisons are unavailable — DeepSeek Coder and Qwen2.5-Max don't publish scores on any of the same benchmarks.