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 | 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. | — | $15.00 |
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. | — | $60.00 |
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. | — | $7.50 |
These models have no shared benchmark scores. | ||
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% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 73.3% |
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 | OpenAI |
| Release date | Oct 12 2026 | Sep 12 2024 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Muse WondervsClaude Haiku 5.5o1-previewvsClaude Haiku 5.5Muse WondervsGemini 4 Argono1-previewvsGemini 4 ArgonMuse WondervsGrok 4.7o1-previewvsGrok 4.7Muse WondervsDeepSeek-V4.1-Flasho1-previewvsDeepSeek-V4.1-FlashMuse WondervsMistral Large 4o1-previewvsMistral Large 4Muse WondervsKimi K3o1-previewvsKimi K3Frequently asked questions
Muse Wonder and o1-preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only o1-preview has a verified first-party API price: $15.00 per million input tokens and $60.00 per million output tokens. No pay-as-you-go API rate is tracked for Muse Wonder. o1-preview shipped 760 days before Muse Wonder, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Muse Wonder) vs 128k (o1-preview).
Direct benchmark comparisons are unavailable — Muse Wonder and o1-preview don't publish scores on any of the same benchmarks.