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. | 671B | 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 |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.27SiliconFlow | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.41SiliconFlow | — |
| $74.50SiliconFlow · fp8 | — | |
| 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 29 2025 | Nov 28 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.2 ExpvsClaude Haiku 5.5QwQ-32B-PreviewvsClaude Haiku 5.5DeepSeek V3.2 ExpvsGPT-6.1 SolQwQ-32B-PreviewvsGPT-6.1 SolDeepSeek V3.2 ExpvsGemini 4 ArgonQwQ-32B-PreviewvsGemini 4 ArgonDeepSeek V3.2 ExpvsMuse Spark OpenQwQ-32B-PreviewvsMuse Spark OpenDeepSeek V3.2 ExpvsGrok 4.7QwQ-32B-PreviewvsGrok 4.7DeepSeek V3.2 ExpvsMistral Large 4QwQ-32B-PreviewvsMistral Large 4Frequently asked questions
DeepSeek V3.2 Exp and QwQ-32B-Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. QwQ-32B-Preview shipped 305 days before DeepSeek V3.2 Exp, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.2 Exp has 671B parameters, while QwQ-32B-Preview has 32B. Context windows are 128k (DeepSeek V3.2 Exp) vs 32k (QwQ-32B-Preview).
Direct benchmark comparisons are unavailable — DeepSeek V3.2 Exp and QwQ-32B-Preview don't publish scores on any of the same benchmarks.