Gemini 3.0 ProvsMuse Glimmer
Gemini 3.0 Pro | Muse Glimmer | |
|---|---|---|
| Specifications | ||
Parameters | — | 30B |
| Benchmarks | ||
Agentic coding SWE-Bench ProCan 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% |
Coding SWE-Bench VerifiedReal 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. | 76.2%Best | 76% |
Next.js coding Next.js EvalsVercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 67% | — |
Agentic terminal coding Terminal-Bench 2.1Can 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% |
Multi-step tool use MCP AtlasCan 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% |
Multidisciplinary reasoning Humanity's Last Exam · no toolsHumanity'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% |
Abstract reasoning ARC-AGI-2Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 31.1% | — |
Science GPQA DiamondGraduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 91.9%Best | 83.5% |
Agentic computer use OSWorld-VerifiedCan the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | — | 65.9% |
Knowledge work GDPval-AA v2economically valuable knowledge work (v2, re-based Elo) | — | 953 |
Chart reasoning CharXiv ReasoningCan the AI read and reason about complex charts and figures, not just text? Higher is better. | — | 78.8% |
Multimodal reasoning MMMU-ProA tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 74% |
Multimodal MMMUTests the AI on understanding images and text together across many college subjects. Higher is better. | 81% | — |
Community preference Arena Elo (Text)Real people chat with two anonymous AIs side by side and vote for the answer they prefer. Votes become a chess-style Elo rating on arena.ai — it measures which AI people actually like, not test scores. Higher is better. | 1486 | — |
Community preference (code) Arena Elo (Code)Like the text arena, but people vote on which AI writes better code. The votes become a chess-style Elo rating on arena.ai. Higher is better. | 1439 | — |
| Overview | ||
| Company | Meta | |
| Release date | Nov 18 2025 | Aug 10 2026 |
| Access | Proprietary | Open Weight |
Which is better: Gemini 3.0 Pro or Muse Glimmer?
Gemini 3.0 Pro leads Muse Glimmer on 2 of the 2 benchmarks they both report (SWE-Bench Verified, GPQA Diamond). Gemini 3.0 Pro shipped 265 days before Muse Glimmer, so benchmark comparisons should account for the intervening progress.
Gemini 3.0 Pro is proprietary, while Muse Glimmer is open weight.
On SWE-Bench Verified, Gemini 3.0 Pro leads at 76.2% vs Muse Glimmer at 76%. On GPQA Diamond, Gemini 3.0 Pro leads at 91.9% vs Muse Glimmer at 83.5%.
Frequently asked questions
Gemini 3.0 Pro was released by Google on Nov 18 2025.
Muse Glimmer was released by Meta on Aug 10 2026.
Gemini 3.0 Pro leads on SWE-Bench Verified — Gemini 3.0 Pro 76.2% vs Muse Glimmer 76%.
Gemini 3.0 Pro leads on GPQA Diamond — Gemini 3.0 Pro 91.9% vs Muse Glimmer 83.5%.
Gemini 3.0 Pro is a proprietary model released by Google. Muse Glimmer is an open weight model released by Meta.