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 | 200k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $15.00 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $60.00 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $7.50 |
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. | $0.259SiliconFlow | $15.00OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.38AtlasCloud | $60.00OpenAI |
| $71.00DeepInfra · fp4 | $6,000OpenAI list price | |
| These models have no shared benchmark scores. | ||
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% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 75.7% |
| Overview | ||
| Company | DeepSeek | OpenAI |
| Release date | Dec 1 2025 | Dec 5 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Frequently asked questions
DeepSeek-V3.2 and o1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o1 has a verified first-party API price: $15.00 per million input tokens and $60.00 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek-V3.2. o1 shipped 361 days before DeepSeek-V3.2, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (DeepSeek-V3.2) vs 200k (o1). DeepSeek-V3.2 is open weight, while o1 is closed.
Direct benchmark comparisons are unavailable — DeepSeek-V3.2 and o1 don't publish scores on any of the same benchmarks.