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. | 256k | 200k |
| 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. | — | $1.10 |
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. | — | $4.40 |
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. | — | $0.55 |
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. | — | $1.10OpenAI |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $4.40OpenAI |
| — | $440OpenAI list price | |
| 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. | — | 49.3% |
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% | — |
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 | OpenAI |
| Release date | Oct 15 2026 | Jan 31 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5o3-minivsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsGemini 4 Argono3-minivsGemini 4 ArgonNemotron 3.5 Bolt 74BvsMuse Spark Openo3-minivsMuse Spark OpenNemotron 3.5 Bolt 74BvsGrok 4.7o3-minivsGrok 4.7Nemotron 3.5 Bolt 74BvsDeepSeek-V4.1-Flash-1017o3-minivsDeepSeek-V4.1-Flash-1017Nemotron 3.5 Bolt 74BvsMistral Large 4o3-minivsMistral Large 4Frequently asked questions
Nemotron 3.5 Bolt 74B and o3-mini don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o3-mini has a verified first-party API price: $1.10 per million input tokens and $4.40 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Bolt 74B. o3-mini shipped 622 days before Nemotron 3.5 Bolt 74B, so benchmark comparisons should account for the intervening progress.
Context windows are 256k (Nemotron 3.5 Bolt 74B) vs 200k (o3-mini). Nemotron 3.5 Bolt 74B is open weight, while o3-mini is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Bolt 74B and o3-mini don't publish scores on any of the same benchmarks.