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. | — | 816B |
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 | ||
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 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. | $0.15Google | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.25Google | — |
| $185Google list price | — | |
| Benchmarks | ||
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% | 76.4% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1140 | 1401 |
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 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% | — |
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 60% |
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. | — | 89.2% |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 61.8% |
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% | — |
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% | — |
| Overview | ||
| Company | Meta | |
| Release date | Jul 21 2026 | Oct 15 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 3.5 Flash-LitevsClaude Haiku 5.5Muse Spark OpenvsClaude Haiku 5.5Gemini 3.5 Flash-LitevsGPT-6.1 SolMuse Spark OpenvsGPT-6.1 SolGemini 3.5 Flash-LitevsGrok 4.7Muse Spark OpenvsGrok 4.7Gemini 3.5 Flash-LitevsDeepSeek-V4.1-FlashMuse Spark OpenvsDeepSeek-V4.1-FlashGemini 3.5 Flash-LitevsMistral Large 4Muse Spark OpenvsMistral Large 4Gemini 3.5 Flash-LitevsKimi K3Muse Spark OpenvsKimi K3Frequently asked questions
Muse Spark Open leads Gemini 3.5 Flash-Lite on 2 of the 2 benchmarks they both report (Terminal-Bench 2.1, 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 Spark Open. Gemini 3.5 Flash-Lite shipped 86 days before Muse Spark Open, so benchmark comparisons should account for the intervening progress.
Gemini 3.5 Flash-Lite is closed, while Muse Spark Open is open weight.
On Terminal-Bench 2.1, Muse Spark Open leads at 76.4% vs Gemini 3.5 Flash-Lite at 54%. On GDPval-AA v2, Muse Spark Open leads at 1401 vs Gemini 3.5 Flash-Lite at 1140.