Nemotron 3.5 LightningvsGPT-6 Luna
Nemotron 3.5 Lightning | GPT-6 Luna | |
|---|---|---|
| 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 | 1.05M |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $0.10 |
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. | — | $0.50 |
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. | — | $0.01 |
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. | $0.065Darkbloom | — |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.18Darkbloom | — |
| BenchmarksPublished by one model only | ||
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% | — |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 66.6% |
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 | NVIDIA | OpenAI |
| Release date | Aug 11 2026 | Sep 22 2026 |
| Access | Open Source | Proprietary |
Other comparisons
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Frequently asked questions
Nemotron 3.5 Lightning and GPT-6 Luna don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-6 Luna has a verified first-party API price: $0.10 per million input tokens and $0.50 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Lightning. Nemotron 3.5 Lightning shipped 42 days before GPT-6 Luna, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3.5 Lightning) vs 1.05M (GPT-6 Luna). Nemotron 3.5 Lightning is open source, while GPT-6 Luna is proprietary.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Lightning and GPT-6 Luna don't publish scores on any of the same benchmarks.