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 | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $0.10 |
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. | — | $0.40 |
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. | — | $0.01 |
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 | $0.05Google AI Studio |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00SiliconFlow | $0.20Google AI Studio |
| $104SiliconFlow · fp8 | $40.00Google list price | |
| These models have no shared benchmark scores. | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 68.4% | — |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | 57.8% | — |
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. | 74.9% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 80.7% | — |
| Overview | ||
| Company | DeepSeek | |
| Release date | Sep 22 2025 | Jun 17 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1 TerminusvsClaude Haiku 5.5Gemini 2.5 Flash-LitevsClaude Haiku 5.5DeepSeek V3.1 TerminusvsGPT-6.1 SolGemini 2.5 Flash-LitevsGPT-6.1 SolDeepSeek V3.1 TerminusvsMuse Spark OpenGemini 2.5 Flash-LitevsMuse Spark OpenDeepSeek V3.1 TerminusvsGrok 4.7Gemini 2.5 Flash-LitevsGrok 4.7DeepSeek V3.1 TerminusvsMistral Large 4Gemini 2.5 Flash-LitevsMistral Large 4DeepSeek V3.1 TerminusvsKimi K3Gemini 2.5 Flash-LitevsKimi K3Frequently asked questions
DeepSeek V3.1 Terminus and Gemini 2.5 Flash-Lite don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Gemini 2.5 Flash-Lite has a verified first-party API price: $0.10 per million input tokens and $0.40 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek V3.1 Terminus. Gemini 2.5 Flash-Lite shipped 97 days before DeepSeek V3.1 Terminus, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (DeepSeek V3.1 Terminus) vs 1M (Gemini 2.5 Flash-Lite). DeepSeek V3.1 Terminus is open weight, while Gemini 2.5 Flash-Lite is closed.
Direct benchmark comparisons are unavailable — DeepSeek V3.1 Terminus and Gemini 2.5 Flash-Lite don't publish scores on any of the same benchmarks.