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 | 480B |
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 | 256k |
| 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. | $0.2088GMICloud | $0.30DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.3096GMICloud | $1.00DeepInfra |
| $57.24GMICloud · fp8 | $110DeepInfra · turbo · fp4 | |
| Benchmarks | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 13% | 20% |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Dec 1 2025 | Jul 22 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
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Qwen3-Coder leads DeepSeek-V3.2 on 1 of the 1 benchmark they both report (BullshitBench v2). Qwen3-Coder shipped 132 days before DeepSeek-V3.2, so benchmark comparisons should account for the intervening progress.
DeepSeek-V3.2 has 671B parameters, while Qwen3-Coder has 480B. Context windows are 128k (DeepSeek-V3.2) vs 256k (Qwen3-Coder).
On BullshitBench v2, Qwen3-Coder leads at 20% vs DeepSeek-V3.2 at 13%.