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 | 256k |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $1.00 |
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. | — | $2.00 |
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. | — | $0.20 |
| — | $300SpaceXSI 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. | — | 70.8% |
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 | SpaceXSI |
| Release date | Oct 15 2026 | Aug 28 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5Grok Code Fast 1vsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsGPT-6.1 SolGrok Code Fast 1vsGPT-6.1 SolNemotron 3.5 Bolt 74BvsGemini 4 ArgonGrok Code Fast 1vsGemini 4 ArgonNemotron 3.5 Bolt 74BvsMuse Spark OpenGrok Code Fast 1vsMuse Spark OpenNemotron 3.5 Bolt 74BvsDeepSeek-V4.1-Flash-1017Grok Code Fast 1vsDeepSeek-V4.1-Flash-1017Nemotron 3.5 Bolt 74BvsMistral Large 4Grok Code Fast 1vsMistral Large 4Frequently asked questions
Nemotron 3.5 Bolt 74B and Grok Code Fast 1 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Grok Code Fast 1 has a verified first-party API price: $1.00 per million input tokens and $2.00 per million output tokens. No pay-as-you-go API rate is tracked for Nemotron 3.5 Bolt 74B. Grok Code Fast 1 shipped 413 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 256k (Grok Code Fast 1). Nemotron 3.5 Bolt 74B is open weight, while Grok Code Fast 1 is closed.
Direct benchmark comparisons are unavailable — Nemotron 3.5 Bolt 74B and Grok Code Fast 1 don't publish scores on any of the same benchmarks.