Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | 30B | — |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 1M | 128k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $0.15 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $0.60 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.075 |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.045Wafer | $0.15Azure |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.13Wafer | $0.60Azure |
| Benchmarks | ||
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% | 40.2% |
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. | — | 2% |
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% | — |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Aug 11 2026 | Jul 18 2024 |
| Access | Open Source | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 LightningvsClaude Haiku 5.5GPT-4o minivsClaude Haiku 5.5Nemotron 3.5 LightningvsGemini 4 ArgonGPT-4o minivsGemini 4 ArgonNemotron 3.5 LightningvsMuse Spark 1.3GPT-4o minivsMuse Spark 1.3Nemotron 3.5 LightningvsGrok 4.7GPT-4o minivsGrok 4.7Nemotron 3.5 LightningvsDeepSeek-V4.1-FlashGPT-4o minivsDeepSeek-V4.1-FlashNemotron 3.5 LightningvsMistral Large 4GPT-4o minivsMistral Large 4Frequently asked questions
Nemotron 3.5 Lightning leads GPT-4o mini on 1 of the 1 benchmark they both report (GPQA Diamond). Only GPT-4o mini has a verified first-party API price: $0.15 per million input tokens and $0.60 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Lightning. GPT-4o mini shipped 754 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3.5 Lightning) vs 128k (GPT-4o mini). Nemotron 3.5 Lightning is open source, while GPT-4o mini is closed.
On GPQA Diamond, Nemotron 3.5 Lightning leads at 75.4% vs GPT-4o mini at 40.2%.