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. | 1.6T | — |
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. | 1M | 128k |
| 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. | — | $2.50 |
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. | — | $10.00 |
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. | — | $1.25 |
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. | $0.90Relace | $2.50Azure |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.50Relace | $10.00Azure |
| $255Relace · fp4 | $1,000OpenAI list price | |
| 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. | 14% | 12% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 90.1% | 49.9% |
CheatBenchCheating rate — Measures how often AI agents try to cheat on difficult assignments, such as reading hidden answers, copying work or manipulating grading. The overall score gives equal weight to ten categories; the sycophancy category measures how far an agent shifts its beliefs toward a user's stated views. Scores describe each model in its tested agent setup, and task success is measured separately. Lower is better. | 75.1% | — |
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. | 93.5% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 83.4% | — |
| Overview | ||
| Company | DeepSeek | OpenAI |
| Release date | Apr 24 2026 | May 13 2024 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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DeepSeek-V4-Pro leads GPT-4o on 2 of the 2 benchmarks they both report (BullshitBench v2, GPQA Diamond). Only GPT-4o has a verified first-party API price: $2.50 per million input tokens and $10.00 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek-V4-Pro. GPT-4o shipped 711 days before DeepSeek-V4-Pro, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (DeepSeek-V4-Pro) vs 128k (GPT-4o). DeepSeek-V4-Pro is open weight, while GPT-4o is closed.
On BullshitBench v2, DeepSeek-V4-Pro leads at 14% vs GPT-4o at 12%. On GPQA Diamond, DeepSeek-V4-Pro leads at 90.1% vs GPT-4o at 49.9%.