Muse GlimmervsGPT-4
Muse Glimmer | GPT-4 | |
|---|---|---|
| Specifications | ||
Parameters | 30B | — |
Context window | — | 8k |
| 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% | — |
Function synthesis HumanEval164 small Python problems: the AI is given a function's description and has to write the working function. This was the coding benchmark of the GPT-3.5 and GPT-4 era, before the field moved to fixing real bugs in real repositories. 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% | — |
Science GPQA DiamondGraduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 83.5% | — |
General knowledge MMLUA 57-subject multiple-choice exam — history, law, medicine, maths — that was the standard measure of how much a model knows from 2020 until roughly 2024, when frontier scores crowded into the high 80s and labs moved on to harder tests. The scores here were published years apart under different testing setups, so read them as a historical record rather than a like-for-like ranking. Higher is better. | — | 86.4% |
Grade-school math GSM8KGrade-school maths word problems that take a few steps of arithmetic to work through. It separated the models of 2022 and 2023 sharply, then saturated. One caveat on the historical numbers: OpenAI included part of the GSM8K training set in GPT-4's pre-training mix, so GPT-4's score is not a clean few-shot result. Higher is better. | — | 92% |
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% | — |
| Overview | ||
| Company | Meta | OpenAI |
| Release date | Aug 10 2026 | Mar 14 2023 |
| Access | Open Weight | Proprietary |
Which is better: Muse Glimmer or GPT-4?
Muse Glimmer and GPT-4 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. GPT-4 shipped 1245 days before Muse Glimmer, so benchmark comparisons should account for the intervening progress.
Muse Glimmer is open weight, while GPT-4 is proprietary.
Direct benchmark comparisons are unavailable — Muse Glimmer and GPT-4 don't publish scores on any of the same benchmarks.
Frequently asked questions
Muse Glimmer was released by Meta on Aug 10 2026.
GPT-4 was released by OpenAI on Mar 14 2023.
Muse Glimmer is an open weight model released by Meta. GPT-4 is a proprietary model released by OpenAI.