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 | 32B |
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 | 32k |
| These models have no shared benchmark scores. | ||
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 50% |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Sep 5 2024 | Nov 28 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V2.5vsClaude Haiku 5.5QwQ-32B-PreviewvsClaude Haiku 5.5DeepSeek V2.5vsGPT-6.1 SolQwQ-32B-PreviewvsGPT-6.1 SolDeepSeek V2.5vsGemini 4 ArgonQwQ-32B-PreviewvsGemini 4 ArgonDeepSeek V2.5vsMuse Spark OpenQwQ-32B-PreviewvsMuse Spark OpenDeepSeek V2.5vsGrok 4.7QwQ-32B-PreviewvsGrok 4.7DeepSeek V2.5vsMistral Large 4QwQ-32B-PreviewvsMistral Large 4Frequently asked questions
DeepSeek V2.5 and QwQ-32B-Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V2.5 shipped 84 days before QwQ-32B-Preview, so benchmark comparisons should account for the intervening progress.
DeepSeek V2.5 has 236B parameters, while QwQ-32B-Preview has 32B. Context windows are 128k (DeepSeek V2.5) vs 32k (QwQ-32B-Preview).
Direct benchmark comparisons are unavailable — DeepSeek V2.5 and QwQ-32B-Preview don't publish scores on any of the same benchmarks.