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. | 74B | — |
| 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. | — | $1.25 |
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. | — | $10.00 |
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. | — | $0.125 |
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. | — | $0.625OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $5.00OpenAI |
| — | $750OpenAI list price | |
| Benchmarks | ||
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 86.43% | 88.1% |
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. | — | 25% |
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. | — | 76.3% |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 50.98% | — |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | 52.2% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 66.75% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | — | 76% |
| Overview | ||
| Company | NVIDIA | OpenAI |
| Release date | Oct 15 2026 | Nov 12 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5GPT-5.1vsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsGemini 4 ArgonGPT-5.1vsGemini 4 ArgonNemotron 3.5 Bolt 74BvsMuse Spark OpenGPT-5.1vsMuse Spark OpenNemotron 3.5 Bolt 74BvsGrok 4.7GPT-5.1vsGrok 4.7Nemotron 3.5 Bolt 74BvsDeepSeek-V4.1-FlashGPT-5.1vsDeepSeek-V4.1-FlashNemotron 3.5 Bolt 74BvsMistral Large 4GPT-5.1vsMistral Large 4Frequently asked questions
GPT-5.1 leads Nemotron 3.5 Bolt 74B on 1 of the 1 benchmark they both report (GPQA Diamond). Only GPT-5.1 has a verified first-party API price: $1.25 per million input tokens and $10.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Bolt 74B. GPT-5.1 shipped 337 days before Nemotron 3.5 Bolt 74B, so benchmark comparisons should account for the intervening progress.
Nemotron 3.5 Bolt 74B is open weight, while GPT-5.1 is closed.
On GPQA Diamond, GPT-5.1 leads at 88.1% vs Nemotron 3.5 Bolt 74B at 86.43%.