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. | — | 74B |
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 | 256k |
| 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. | $0.30 | — |
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. | $2.50 | — |
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. | $0.03 | — |
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. | $0.15Google AI Studio | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.25Google AI Studio | — |
| $185Google list price | — | |
| 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. | 19% | — |
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% |
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% |
| Overview | ||
| Company | NVIDIA | |
| Release date | Apr 17 2025 | Oct 15 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 2.5 FlashvsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5Gemini 2.5 FlashvsGPT-6.1 SolNemotron 3.5 Bolt 74BvsGPT-6.1 SolGemini 2.5 FlashvsMuse Spark OpenNemotron 3.5 Bolt 74BvsMuse Spark OpenGemini 2.5 FlashvsGrok 4.7Nemotron 3.5 Bolt 74BvsGrok 4.7Gemini 2.5 FlashvsDeepSeek-V4.1-Flash-1017Nemotron 3.5 Bolt 74BvsDeepSeek-V4.1-Flash-1017Gemini 2.5 FlashvsMistral Large 4Nemotron 3.5 Bolt 74BvsMistral Large 4Frequently asked questions
Gemini 2.5 Flash and Nemotron 3.5 Bolt 74B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Gemini 2.5 Flash has a verified first-party API price: $0.30 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Bolt 74B. Gemini 2.5 Flash shipped 546 days before Nemotron 3.5 Bolt 74B, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 2.5 Flash) vs 256k (Nemotron 3.5 Bolt 74B). Gemini 2.5 Flash is closed, while Nemotron 3.5 Bolt 74B is open weight.
Direct benchmark comparisons are unavailable — Gemini 2.5 Flash and Nemotron 3.5 Bolt 74B don't publish scores on any of the same benchmarks.