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 |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | 1M | — |
| 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. | $1.25Meta | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $4.25Meta | — |
| $463Meta | — | |
| Benchmarks | ||
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. | 82.9% | 50.98% |
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. | 50% | — |
ProgramBenchProgram reconstruction — The AI receives a working program and its documentation, then builds a replacement from scratch without the original source code, internet access or decompilation. The score is the percentage of 200 programs that pass every behavioral test. We record each model's best published mini-SWE-agent result, including higher reasoning efforts where available. Partial test-pass rates and almost-solved programs do not count toward this score. Equal scores share a rank here; the official board also uses partial progress to break ties. Higher is better. | 0% | — |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | 59.3% | — |
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 | Meta | NVIDIA |
| Release date | Aug 5 2026 | Oct 15 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Muse Spark 1.2vsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5Muse Spark 1.2vsGPT-6.1 SolNemotron 3.5 Bolt 74BvsGPT-6.1 SolMuse Spark 1.2vsGemini 4 ArgonNemotron 3.5 Bolt 74BvsGemini 4 ArgonMuse Spark 1.2vsGrok 4.7Nemotron 3.5 Bolt 74BvsGrok 4.7Muse Spark 1.2vsDeepSeek-V4.1-FlashNemotron 3.5 Bolt 74BvsDeepSeek-V4.1-FlashMuse Spark 1.2vsMistral Large 4Nemotron 3.5 Bolt 74BvsMistral Large 4Frequently asked questions
Muse Spark 1.2 leads Nemotron 3.5 Bolt 74B on 1 of the 1 benchmark they both report (Terminal-Bench 2.1). Muse Spark 1.2 shipped 71 days before Nemotron 3.5 Bolt 74B, so benchmark comparisons should account for the intervening progress.
Muse Spark 1.2 is closed, while Nemotron 3.5 Bolt 74B is open weight.
On Terminal-Bench 2.1, Muse Spark 1.2 leads at 82.9% vs Nemotron 3.5 Bolt 74B at 50.98%.