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. | 284B | — |
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 | 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. | — | $2.00 |
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. | — | $8.00 |
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. | — | $0.50 |
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. | $0.05Relace | $2.00OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.1652StreamLake | $8.00OpenAI |
| $24.78StreamLake · fp8 | $800OpenAI 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. | 18% | 26% |
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. | 91.6% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 88.1% | — |
| Overview | ||
| Company | DeepSeek | OpenAI |
| Release date | Apr 24 2026 | Apr 16 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek-V4-FlashvsClaude Haiku 5.5o3vsClaude Haiku 5.5DeepSeek-V4-FlashvsGemini 4 Argono3vsGemini 4 ArgonDeepSeek-V4-FlashvsMuse Spark Openo3vsMuse Spark OpenDeepSeek-V4-FlashvsGrok 4.7o3vsGrok 4.7DeepSeek-V4-FlashvsMistral Large 4o3vsMistral Large 4DeepSeek-V4-FlashvsKimi K3o3vsKimi K3Frequently asked questions
o3 leads DeepSeek-V4-Flash on 1 of the 1 benchmark they both report (BullshitBench v2). Only o3 has a verified first-party API price: $2.00 per million input tokens and $8.00 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek-V4-Flash. o3 shipped 373 days before DeepSeek-V4-Flash, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (DeepSeek-V4-Flash) vs 200k (o3). DeepSeek-V4-Flash is open weight, while o3 is closed.
On BullshitBench v2, o3 leads at 26% vs DeepSeek-V4-Flash at 18%.