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 | 21B |
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 | ||
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 | $0.018Darkbloom |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $0.13Wafer | $0.09Darkbloom |
| Benchmarks | ||
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% | 60.7% |
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% | 71.5% |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | — | 10.9% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 17.3% |
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. | — | 85.3% |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Aug 11 2026 | Aug 5 2025 |
| Access | Open Source | Open Weight |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 LightningvsClaude Haiku 5.5gpt-oss-20bvsClaude Haiku 5.5Nemotron 3.5 LightningvsGemini 4 Argongpt-oss-20bvsGemini 4 ArgonNemotron 3.5 LightningvsMuse Spark 1.3gpt-oss-20bvsMuse Spark 1.3Nemotron 3.5 LightningvsGrok 4.7gpt-oss-20bvsGrok 4.7Nemotron 3.5 LightningvsDeepSeek-V4.1-Flashgpt-oss-20bvsDeepSeek-V4.1-FlashNemotron 3.5 LightningvsMistral Large 4gpt-oss-20bvsMistral Large 4Frequently asked questions
Nemotron 3.5 Lightning and gpt-oss-20b are evenly matched across the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). gpt-oss-20b shipped 371 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Nemotron 3.5 Lightning has 30B parameters, while gpt-oss-20b has 21B. Context windows are 1M (Nemotron 3.5 Lightning) vs 128k (gpt-oss-20b). Nemotron 3.5 Lightning is open source, while gpt-oss-20b is open weight.
On SWE-Bench Verified, gpt-oss-20b leads at 60.7% vs Nemotron 3.5 Lightning at 51.6%. On GPQA Diamond, Nemotron 3.5 Lightning leads at 75.4% vs gpt-oss-20b at 71.5%.