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 | — |
| 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. | — | $0.05 |
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. | — | $0.40 |
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. | — | $0.005 |
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. | $0.035Wafer | $0.025OpenAI |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.13Wafer | $0.20OpenAI |
These models have no shared benchmark scores. | ||
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 | NVIDIA | OpenAI |
| Release date | Aug 11 2026 | Aug 7 2025 |
| Access | Open Source | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 LightningvsClaude Haiku 5.5GPT-5 nanovsClaude Haiku 5.5Nemotron 3.5 LightningvsGemini 4 ArgonGPT-5 nanovsGemini 4 ArgonNemotron 3.5 LightningvsMuse Spark 1.3GPT-5 nanovsMuse Spark 1.3Nemotron 3.5 LightningvsGrok 4.7GPT-5 nanovsGrok 4.7Nemotron 3.5 LightningvsDeepSeek-V4.1-FlashGPT-5 nanovsDeepSeek-V4.1-FlashNemotron 3.5 LightningvsMistral Large 4GPT-5 nanovsMistral Large 4Frequently asked questions
Nemotron 3.5 Lightning and GPT-5 nano don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-5 nano has a verified first-party API price: $0.05 per million input tokens and $0.40 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Lightning. GPT-5 nano shipped 369 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Nemotron 3.5 Lightning is open source, while GPT-5 nano is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Lightning and GPT-5 nano don't publish scores on any of the same benchmarks.