Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 30B | — |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 400k |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.025Darkbloom | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.12Darkbloom | — |
| $11.00Darkbloom · int4 | — | |
| These models have no shared benchmark scores. | ||
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. | — | 39% |
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 | Sep 15 2025 |
| Access | Open Source | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 LightningvsClaude Haiku 5.5GPT-5-CodexvsClaude Haiku 5.5Nemotron 3.5 LightningvsGemini 4 ArgonGPT-5-CodexvsGemini 4 ArgonNemotron 3.5 LightningvsMuse Spark OpenGPT-5-CodexvsMuse Spark OpenNemotron 3.5 LightningvsGrok 4.7GPT-5-CodexvsGrok 4.7Nemotron 3.5 LightningvsDeepSeek-V4.1-Flash-1017GPT-5-CodexvsDeepSeek-V4.1-Flash-1017Nemotron 3.5 LightningvsMistral Large 4GPT-5-CodexvsMistral Large 4Frequently asked questions
Nemotron 3.5 Lightning and GPT-5-Codex don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-5-Codex shipped 330 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Nemotron 3.5 Lightning) vs 400k (GPT-5-Codex). Nemotron 3.5 Lightning is open source, while GPT-5-Codex is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Lightning and GPT-5-Codex don't publish scores on any of the same benchmarks.