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 | — |
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 | 1M |
| 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 | — | |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Sep 29 2025 | Nov 11 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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DeepSeek V3.2 Exp and Qwen4-Max Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V3.2 Exp shipped 408 days before Qwen4-Max Preview, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (DeepSeek V3.2 Exp) vs 1M (Qwen4-Max Preview). DeepSeek V3.2 Exp is open weight, while Qwen4-Max Preview is closed.
Direct benchmark comparisons are unavailable — DeepSeek V3.2 Exp and Qwen4-Max Preview don't publish scores on any of the same benchmarks.