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 | 30B |
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 third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.30DeepInfra | $0.045Wafer |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $1.10Phala | $0.13Wafer |
| Benchmarks | ||
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% | 51.6% |
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% | 75.4% |
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% | — |
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% | — |
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% | — |
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 | Aug 11 2026 |
| Access | Open Weight | Open Source |
| Model details | View model | View model |
Other comparisons
Muse GlimmervsClaude Haiku 5.5Nemotron 3.5 LightningvsClaude Haiku 5.5Muse GlimmervsGPT-6.1 SolNemotron 3.5 LightningvsGPT-6.1 SolMuse GlimmervsGemini 4 ArgonNemotron 3.5 LightningvsGemini 4 ArgonMuse GlimmervsGrok 4.7Nemotron 3.5 LightningvsGrok 4.7Muse GlimmervsDeepSeek-V4.1-FlashNemotron 3.5 LightningvsDeepSeek-V4.1-FlashMuse GlimmervsMistral Large 4Nemotron 3.5 LightningvsMistral Large 4Frequently asked questions
Muse Glimmer leads Nemotron 3.5 Lightning on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Muse Glimmer shipped 1 days before Nemotron 3.5 Lightning, so benchmark comparisons should account for the intervening progress.
Muse Glimmer has 30B parameters, while Nemotron 3.5 Lightning has 30B. Muse Glimmer is open weight, while Nemotron 3.5 Lightning is open source.
On SWE-Bench Verified, Muse Glimmer leads at 76% vs Nemotron 3.5 Lightning at 51.6%. On GPQA Diamond, Muse Glimmer leads at 83.5% vs Nemotron 3.5 Lightning at 75.4%.