Compare AI models
| Specifications | ||
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. | 2M | 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. | — | $4.00Meta |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $20.00Meta |
| — | $1,800Meta list price | |
| These models have no shared benchmark scores. | ||
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. | — | 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 | |
| Release date | Sep 24 2024 | Oct 12 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Gemini 1.5 Pro-002vsClaude Haiku 5.5Muse WondervsClaude Haiku 5.5Gemini 1.5 Pro-002vsGPT-6.1 SolMuse WondervsGPT-6.1 SolGemini 1.5 Pro-002vsGrok 4.7Muse WondervsGrok 4.7Gemini 1.5 Pro-002vsDeepSeek-V4.1-Flash-1017Muse WondervsDeepSeek-V4.1-Flash-1017Gemini 1.5 Pro-002vsMistral Large 4Muse WondervsMistral Large 4Gemini 1.5 Pro-002vsKimi K3Muse WondervsKimi K3Frequently asked questions
Gemini 1.5 Pro-002 and Muse Wonder don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. 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 Gemini 1.5 Pro-002. Gemini 1.5 Pro-002 shipped 748 days before Muse Wonder, so benchmark comparisons should account for the intervening progress.
Context windows are 2M (Gemini 1.5 Pro-002) vs 1M (Muse Wonder).
Direct benchmark comparisons are unavailable — Gemini 1.5 Pro-002 and Muse Wonder don't publish scores on any of the same benchmarks.