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. | $0.256DeepInfra | $0.00Nvidia |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.712DeepInfra | $0.00Nvidia |
| $103StreamLake | $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. | 42% | 51.6% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 59.1% | 75.4% |
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. | 40.5% | — |
MMLUGeneral knowledge — A 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | 88.5% | — |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Dec 26 2024 | Aug 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
DeepSeek V3 ChatvsClaude Haiku 5.5Nemotron 3.5 LightningvsClaude Haiku 5.5DeepSeek V3 ChatvsGPT-6.1 SolNemotron 3.5 LightningvsGPT-6.1 SolDeepSeek V3 ChatvsGemini 4 ArgonNemotron 3.5 LightningvsGemini 4 ArgonDeepSeek V3 ChatvsMuse Spark OpenNemotron 3.5 LightningvsMuse Spark OpenDeepSeek V3 ChatvsGrok 4.7Nemotron 3.5 LightningvsGrok 4.7DeepSeek V3 ChatvsMistral Large 4Nemotron 3.5 LightningvsMistral Large 4Frequently asked questions
Nemotron 3.5 Lightning leads DeepSeek V3 Chat on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). DeepSeek V3 Chat shipped 593 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
DeepSeek V3 Chat has 671B parameters, while Nemotron 3.5 Lightning has 30B. Context windows are 128k (DeepSeek V3 Chat) vs 1M (Nemotron 3.5 Lightning). DeepSeek V3 Chat is open weight, while Nemotron 3.5 Lightning is open source.
On SWE-Bench Verified, Nemotron 3.5 Lightning leads at 51.6% vs DeepSeek V3 Chat at 42%. On GPQA Diamond, Nemotron 3.5 Lightning leads at 75.4% vs DeepSeek V3 Chat at 59.1%.