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. | $0.2088GMICloud | $0.00Nvidia |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.3096GMICloud | $0.00Nvidia |
| $57.24GMICloud · fp8 | $11.00Darkbloom · int4 | |
| 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. | 13% | — |
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. | — | 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. | — | 75.4% |
| Overview | ||
| Company | DeepSeek | NVIDIA |
| Release date | Dec 1 2025 | Aug 11 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-V3.2vsClaude Haiku 5.5Nemotron 3.5 LightningvsClaude Haiku 5.5DeepSeek-V3.2vsGPT-6.1 TerraNemotron 3.5 LightningvsGPT-6.1 TerraDeepSeek-V3.2vsGemini 4 CarbonNemotron 3.5 LightningvsGemini 4 CarbonDeepSeek-V3.2vsMuse Spark OpenNemotron 3.5 LightningvsMuse Spark OpenDeepSeek-V3.2vsGrok 4.7Nemotron 3.5 LightningvsGrok 4.7DeepSeek-V3.2vsMistral Large 4Nemotron 3.5 LightningvsMistral Large 4Frequently asked questions
DeepSeek-V3.2 and Nemotron 3.5 Lightning don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-V3.2 shipped 253 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
DeepSeek-V3.2 has 671B parameters, while Nemotron 3.5 Lightning has 30B. Context windows are 128k (DeepSeek-V3.2) vs 1M (Nemotron 3.5 Lightning).
Direct benchmark comparisons are unavailable — DeepSeek-V3.2 and Nemotron 3.5 Lightning don't publish scores on any of the same benchmarks.