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 | 8k |
| 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. | — | $30.00 |
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. | — | $60.00 |
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 | $30.00Azure |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.13Wafer | $60.00Azure |
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% | — |
HumanEvalFunction synthesis — 164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. Higher is better. | — | 67% |
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% | — |
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. | — | 86.4% |
GSM8KGrade-school math — Grade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | — | 92% |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Aug 11 2026 | Mar 14 2023 |
| Access | Open Source | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 LightningvsClaude Haiku 5.5GPT-4vsClaude Haiku 5.5Nemotron 3.5 LightningvsGemini 4 ArgonGPT-4vsGemini 4 ArgonNemotron 3.5 LightningvsMuse Spark 1.3GPT-4vsMuse Spark 1.3Nemotron 3.5 LightningvsGrok 4.7GPT-4vsGrok 4.7Nemotron 3.5 LightningvsDeepSeek-V4.1-FlashGPT-4vsDeepSeek-V4.1-FlashNemotron 3.5 LightningvsMistral Large 4GPT-4vsMistral Large 4Frequently asked questions
Nemotron 3.5 Lightning and GPT-4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only GPT-4 has a verified first-party API price: $30.00 per million input tokens and $60.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Lightning. GPT-4 shipped 1246 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3.5 Lightning) vs 8k (GPT-4). Nemotron 3.5 Lightning is open source, while GPT-4 is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Lightning and GPT-4 don't publish scores on any of the same benchmarks.