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 |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.30 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $2.50 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.03 | — |
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. | $0.15Google | $0.30DeepInfra |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $1.25Google | $1.10Phala |
| Benchmarks | ||
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. | 54.2% | 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. | 54% | 51.7% |
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. | 74% | 65.9% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1140 | 953 |
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. | 66% | — |
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% |
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% |
BU BenchBrowser agent — Can the AI drive a real web browser to finish tasks — clicking, filling forms, and navigating sites the way a person would? Run by Browser Use on their BU Bench task set. Higher is better. | 49% | — |
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% |
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% |
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 | |
| Release date | Jul 21 2026 | Aug 10 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 3.5 Flash-LitevsClaude Opus 5.5Muse GlimmervsClaude Opus 5.5Gemini 3.5 Flash-LitevsGPT-6 SolMuse GlimmervsGPT-6 SolGemini 3.5 Flash-LitevsGrok 4.7Muse GlimmervsGrok 4.7Gemini 3.5 Flash-LitevsDeepSeek-V4.1-FlashMuse GlimmervsDeepSeek-V4.1-FlashGemini 3.5 Flash-LitevsMistral Medium 3.5Muse GlimmervsMistral Medium 3.5Gemini 3.5 Flash-LitevsKimi K3Muse GlimmervsKimi K3Frequently asked questions
Gemini 3.5 Flash-Lite leads Muse Glimmer on 4 of the 4 benchmarks they both report (SWE-Bench Pro, Terminal-Bench 2.1, OSWorld-Verified, GDPval-AA v2). Only Gemini 3.5 Flash-Lite has a verified first-party API price: $0.30 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for Muse Glimmer. Gemini 3.5 Flash-Lite shipped 20 days before Muse Glimmer, so benchmark comparisons should account for the intervening progress.
Gemini 3.5 Flash-Lite is closed, while Muse Glimmer is open weight.
On SWE-Bench Pro, Gemini 3.5 Flash-Lite leads at 54.2% vs Muse Glimmer at 51.2%. On Terminal-Bench 2.1, Gemini 3.5 Flash-Lite leads at 54% vs Muse Glimmer at 51.7%. On OSWorld-Verified, Gemini 3.5 Flash-Lite leads at 74% vs Muse Glimmer at 65.9%. On GDPval-AA v2, Gemini 3.5 Flash-Lite leads at 1140 vs Muse Glimmer at 953.