Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | — | 30B |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 128k |
| 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. | $0.10 | — |
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. | $0.40 | — |
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. | $0.01 | — |
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. | $0.05Google AI Studio | $0.30DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.20Google AI Studio | $1.20DeepInfra |
| $40.00Google list price | $120DeepInfra · bf16 | |
| These models have no shared benchmark scores. | ||
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% |
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% |
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% |
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 | |
| Release date | Jun 17 2025 | Aug 10 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 2.5 Flash-LitevsClaude Haiku 5.5Muse GlimmervsClaude Haiku 5.5Gemini 2.5 Flash-LitevsGPT-6.1 SolMuse GlimmervsGPT-6.1 SolGemini 2.5 Flash-LitevsGrok 4.7Muse GlimmervsGrok 4.7Gemini 2.5 Flash-LitevsDeepSeek-V4.1-Flash-1017Muse GlimmervsDeepSeek-V4.1-Flash-1017Gemini 2.5 Flash-LitevsMistral Large 4Muse GlimmervsMistral Large 4Gemini 2.5 Flash-LitevsKimi K3Muse GlimmervsKimi K3Frequently asked questions
Gemini 2.5 Flash-Lite and Muse Glimmer don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Gemini 2.5 Flash-Lite has a verified first-party API price: $0.10 per million input tokens and $0.40 per million output tokens. No pay-as-you-go API rate is tracked for Muse Glimmer. Gemini 2.5 Flash-Lite shipped 419 days before Muse Glimmer, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 2.5 Flash-Lite) vs 128k (Muse Glimmer). Gemini 2.5 Flash-Lite is closed, while Muse Glimmer is open weight.
Direct benchmark comparisons are unavailable — Gemini 2.5 Flash-Lite and Muse Glimmer don't publish scores on any of the same benchmarks.