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. | $0.25DeepInfra | $0.00Nvidia |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.95DeepInfra | $0.00Nvidia |
| $97.50DeepInfra · fp4 | $11.00Darkbloom · int4 | |
| Benchmarks | ||
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. | 66% | 51.6% |
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. | 74.8% | — |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | 15.9% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 75.4% |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Aug 21 2025 | Aug 11 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1vsClaude Haiku 5.5Nemotron 3.5 LightningvsClaude Haiku 5.5DeepSeek V3.1vsGPT-6.1 TerraNemotron 3.5 LightningvsGPT-6.1 TerraDeepSeek V3.1vsGemini 4 CarbonNemotron 3.5 LightningvsGemini 4 CarbonDeepSeek V3.1vsMuse Spark OpenNemotron 3.5 LightningvsMuse Spark OpenDeepSeek V3.1vsGrok 4.7Nemotron 3.5 LightningvsGrok 4.7DeepSeek V3.1vsMistral Large 4Nemotron 3.5 LightningvsMistral Large 4Frequently asked questions
DeepSeek V3.1 leads Nemotron 3.5 Lightning on 1 of the 1 benchmark they both report (SWE-Bench Verified). DeepSeek V3.1 shipped 355 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 has 671B parameters, while Nemotron 3.5 Lightning has 30B. Context windows are 128k (DeepSeek V3.1) vs 1M (Nemotron 3.5 Lightning).
On SWE-Bench Verified, DeepSeek V3.1 leads at 66% vs Nemotron 3.5 Lightning at 51.6%.