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. | 30B | 74B |
| 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. | $0.30DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.20DeepInfra | — |
| $120DeepInfra · bf16 | — | |
| 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. | 51.7% | 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. | 75.5% | 52.2% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 83.5% | 86.43% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | 51.2% | — |
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% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 66.75% |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 22% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 65.9% | — |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 953 | — |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | 78.8% | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 74% | — |
| Overview | ||
| Company | Meta | NVIDIA |
| Release date | Aug 10 2026 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Muse GlimmervsClaude Haiku 5.5Nemotron 3.5 Bolt 74BvsClaude Haiku 5.5Muse GlimmervsGPT-6.1 SolNemotron 3.5 Bolt 74BvsGPT-6.1 SolMuse GlimmervsGemini 4 ArgonNemotron 3.5 Bolt 74BvsGemini 4 ArgonMuse GlimmervsGrok 4.7Nemotron 3.5 Bolt 74BvsGrok 4.7Muse GlimmervsDeepSeek-V4.1-FlashNemotron 3.5 Bolt 74BvsDeepSeek-V4.1-FlashMuse GlimmervsMistral Large 4Nemotron 3.5 Bolt 74BvsMistral Large 4Frequently asked questions
Muse Glimmer leads Nemotron 3.5 Bolt 74B on 2 of the 3 benchmarks they both report (Terminal-Bench 2.1, MCP Atlas, GPQA Diamond). Muse Glimmer shipped 66 days before Nemotron 3.5 Bolt 74B, so benchmark comparisons should account for the intervening progress.
Muse Glimmer has 30B parameters, while Nemotron 3.5 Bolt 74B has 74B.
On Terminal-Bench 2.1, Muse Glimmer leads at 51.7% vs Nemotron 3.5 Bolt 74B at 50.98%. On MCP Atlas, Muse Glimmer leads at 75.5% vs Nemotron 3.5 Bolt 74B at 52.2%. On GPQA Diamond, Nemotron 3.5 Bolt 74B leads at 86.43% vs Muse Glimmer at 83.5%.