Compare AI models
| Specifications | ||
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. | — | $4.00 |
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. | — | $20.00 |
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. | — | $0.20 |
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. | $0.02DeepInfra | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.04DeepInfra | — |
| $6.00DeepInfra · fp8 | $1,800Meta list price | |
| Benchmarks | ||
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. | 14% | 57% |
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. | — | 78.8% |
SWE-Marathon v1.1Multi-hour coding — Twenty multi-hour programming jobs — rebuilding a library, cloning a product, training a model, making something faster. A run counts only when every check passes, hidden tests and interface review included. Higher is better. | — | 53.1% |
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. | — | 70.5% |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | — | 39.1% |
Finance Agent v2Agentic financial analysis — Tests the AI on real financial-analysis work, like digging through reports and making sound decisions. Higher is better. | — | 56.6% |
HealthBench ProfessionalHealth — Realistic health conversations graded against detailed rubrics written by physicians — can the AI respond the way a careful medical professional would? Higher is better. | — | 60.5% |
GDP.PDFDocument comprehension — Real professional PDFs — filings, reports, technical documents — with questions an expert in that field would ask. Tests whether the AI reads the page as a document, layout and figures included, rather than as loose text. Higher is better. | — | 30.8% |
| Overview | ||
| Company | Meta | Meta |
| Release date | Jul 23 2024 | Oct 12 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
LLaMA 3.1vsClaude Haiku 5.5Muse WondervsClaude Haiku 5.5LLaMA 3.1vsGPT-6.1 SolMuse WondervsGPT-6.1 SolLLaMA 3.1vsGemini 4 ArgonMuse WondervsGemini 4 ArgonLLaMA 3.1vsGrok 4.7Muse WondervsGrok 4.7LLaMA 3.1vsDeepSeek-V4.1-FlashMuse WondervsDeepSeek-V4.1-FlashLLaMA 3.1vsMistral Large 4Muse WondervsMistral Large 4Frequently asked questions
Muse Wonder leads LLaMA 3.1 on 1 of the 1 benchmark they both report (BullshitBench v2). Only Muse Wonder has a verified first-party API price: $4.00 per million input tokens and $20.00 per million output tokens. No pay-as-you-go API rate is tracked for LLaMA 3.1. LLaMA 3.1 shipped 811 days before Muse Wonder, so benchmark comparisons should account for the intervening progress.
LLaMA 3.1 is open weight, while Muse Wonder is closed.
On BullshitBench v2, Muse Wonder leads at 57% vs LLaMA 3.1 at 14%.