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 | 400k |
| 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. | — | $12.50 |
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. | — | $75.00 |
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. | $0.27SiliconFlow | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00SiliconFlow | — |
| $104SiliconFlow · fp8 | $6,250OpenAI 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% | — |
Advanced Cybersecurity Completion Rate (Internal)Agentic tasks — share of advanced cyber requests the model responds to; measures willingness to answer, not exploit success | — | 95% |
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 | OpenAI |
| Release date | Sep 22 2025 | Aug 10 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1 TerminusvsClaude Fable 5.2GPT-5.6 CybervsClaude Fable 5.2DeepSeek V3.1 TerminusvsGemini 4 CarbonGPT-5.6 CybervsGemini 4 CarbonDeepSeek V3.1 TerminusvsMuse EmberGPT-5.6 CybervsMuse EmberDeepSeek V3.1 TerminusvsGrok 4.7GPT-5.6 CybervsGrok 4.7DeepSeek V3.1 TerminusvsMistral Large 4GPT-5.6 CybervsMistral Large 4DeepSeek V3.1 TerminusvsKimi K3.1 ProGPT-5.6 CybervsKimi K3.1 ProFrequently asked questions
DeepSeek V3.1 Terminus and GPT-5.6 Cyber don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5.6 Cyber has a verified first-party price for an available API: $12.50 per million input tokens and $75.00 per million output tokens. No currently available pay-as-you-go API rate is tracked for DeepSeek V3.1 Terminus. DeepSeek V3.1 Terminus shipped 322 days before GPT-5.6 Cyber, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (DeepSeek V3.1 Terminus) vs 400k (GPT-5.6 Cyber). DeepSeek V3.1 Terminus is open weight, while GPT-5.6 Cyber is closed.
Direct benchmark comparisons are unavailable — DeepSeek V3.1 Terminus and GPT-5.6 Cyber don't publish scores on any of the same benchmarks.