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 | ||
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 | — | |
| 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. | — | 39% |
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 | OpenAI |
| Release date | Sep 22 2025 | Sep 15 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1 TerminusvsClaude Haiku 5.5GPT-5-CodexvsClaude Haiku 5.5DeepSeek V3.1 TerminusvsGemini 4 ArgonGPT-5-CodexvsGemini 4 ArgonDeepSeek V3.1 TerminusvsMuse Spark OpenGPT-5-CodexvsMuse Spark OpenDeepSeek V3.1 TerminusvsGrok 4.7GPT-5-CodexvsGrok 4.7DeepSeek V3.1 TerminusvsMistral Large 4GPT-5-CodexvsMistral Large 4DeepSeek V3.1 TerminusvsKimi K3GPT-5-CodexvsKimi K3Frequently asked questions
DeepSeek V3.1 Terminus and GPT-5-Codex don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-5-Codex shipped 7 days before DeepSeek V3.1 Terminus, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (DeepSeek V3.1 Terminus) vs 400k (GPT-5-Codex). DeepSeek V3.1 Terminus is open weight, while GPT-5-Codex is closed.
Direct benchmark comparisons are unavailable — DeepSeek V3.1 Terminus and GPT-5-Codex don't publish scores on any of the same benchmarks.