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 | 30B |
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 | ||
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 | $0.05Novita |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00SiliconFlow | $0.20Novita |
| $104SiliconFlow · fp8 | $20.00Novita · fp4 | |
| Benchmarks | ||
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% | 68.3% |
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% | 73% |
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. | — | 28% |
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% | — |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Sep 22 2025 | Dec 15 2025 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1 TerminusvsClaude Haiku 5.5Nemotron 3 NanovsClaude Haiku 5.5DeepSeek V3.1 TerminusvsGPT-6.1 SolNemotron 3 NanovsGPT-6.1 SolDeepSeek V3.1 TerminusvsGemini 4 ArgonNemotron 3 NanovsGemini 4 ArgonDeepSeek V3.1 TerminusvsMuse Spark OpenNemotron 3 NanovsMuse Spark OpenDeepSeek V3.1 TerminusvsGrok 4.7Nemotron 3 NanovsGrok 4.7DeepSeek V3.1 TerminusvsMistral Large 4Nemotron 3 NanovsMistral Large 4Frequently asked questions
DeepSeek V3.1 Terminus leads Nemotron 3 Nano on 2 of the 2 benchmarks they both report (LiveCodeBench, GPQA Diamond). DeepSeek V3.1 Terminus shipped 84 days before Nemotron 3 Nano, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 Terminus has 671B parameters, while Nemotron 3 Nano has 30B. Context windows are 128k (DeepSeek V3.1 Terminus) vs 1M (Nemotron 3 Nano). DeepSeek V3.1 Terminus is open weight, while Nemotron 3 Nano is open source.
On LiveCodeBench, DeepSeek V3.1 Terminus leads at 74.9% vs Nemotron 3 Nano at 68.3%. On GPQA Diamond, DeepSeek V3.1 Terminus leads at 80.7% vs Nemotron 3 Nano at 73%.