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. | 8B/70B/405B | — |
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. | 128k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
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 | — | |
| 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. | 14% | — |
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. | — | 79% |
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. | — | 57.5% |
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. | — | 75.3% |
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. | — | 45.4% |
Finance Agent v2Agentic financial analysis — Tests the AI on real financial-analysis work, like digging through reports and making sound decisions. Higher is better. | — | 59.1% |
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. | — | 62.3% |
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. | — | 33.2% |
| 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 Wonder FullvsClaude Haiku 5.5LLaMA 3.1vsGPT-6.1 SolMuse Wonder FullvsGPT-6.1 SolLLaMA 3.1vsGemini 4 ArgonMuse Wonder FullvsGemini 4 ArgonLLaMA 3.1vsGrok 4.7Muse Wonder FullvsGrok 4.7LLaMA 3.1vsDeepSeek-V4.1-Flash-1017Muse Wonder FullvsDeepSeek-V4.1-Flash-1017LLaMA 3.1vsMistral Large 4Muse Wonder FullvsMistral Large 4Frequently asked questions
LLaMA 3.1 and Muse Wonder Full don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. LLaMA 3.1 shipped 811 days before Muse Wonder Full, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (LLaMA 3.1) vs 1M (Muse Wonder Full). LLaMA 3.1 is open weight, while Muse Wonder Full is closed.
Direct benchmark comparisons are unavailable — LLaMA 3.1 and Muse Wonder Full don't publish scores on any of the same benchmarks.