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 | 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 | 128k |
| 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. | $0.25SiliconFlow | $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 | $1.00SiliconFlow |
| $100SiliconFlow · fp8 | $104SiliconFlow · fp8 | |
| 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 | DeepSeek |
| Release date | Mar 24 2025 | Sep 22 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3-0324vsClaude Haiku 5.5DeepSeek V3.1 TerminusvsClaude Haiku 5.5DeepSeek V3-0324vsGPT-6.1 SolDeepSeek V3.1 TerminusvsGPT-6.1 SolDeepSeek V3-0324vsGemini 4 ArgonDeepSeek V3.1 TerminusvsGemini 4 ArgonDeepSeek V3-0324vsMuse Spark OpenDeepSeek V3.1 TerminusvsMuse Spark OpenDeepSeek V3-0324vsGrok 4.7DeepSeek V3.1 TerminusvsGrok 4.7DeepSeek V3-0324vsMistral Large 4DeepSeek V3.1 TerminusvsMistral Large 4Frequently asked questions
DeepSeek V3-0324 and DeepSeek V3.1 Terminus don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V3-0324 shipped 182 days before DeepSeek V3.1 Terminus, so benchmark comparisons should account for the intervening progress.
DeepSeek V3-0324 has 671B parameters, while DeepSeek V3.1 Terminus has 671B. Context windows are 128k (DeepSeek V3-0324) vs 128k (DeepSeek V3.1 Terminus).
Direct benchmark comparisons are unavailable — DeepSeek V3-0324 and DeepSeek V3.1 Terminus don't publish scores on any of the same benchmarks.