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. | — | 550B |
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. | 1M | 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. | — | $0.40DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.76DeepInfra |
| — | $210DeepInfra · fp4 | |
| 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. | — | 49% |
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | — | 71.9% |
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% | — |
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. | — | 56.4% |
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% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | — | 44.4% |
Humanity's Last Exam · with toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. | — | 26.7% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | — | 86.7% |
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% | — |
AA Intelligence IndexOverall intelligence — Artificial Analysis composite intelligence index across evals | — | 48 |
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 | NVIDIA |
| Release date | Oct 12 2026 | Jun 4 2026 |
| Access | Closed | Open Source |
| Model details | View model | View model |
Other comparisons
Muse Wonder FullvsClaude Haiku 5.5Nemotron 3 UltravsClaude Haiku 5.5Muse Wonder FullvsGPT-6.1 SolNemotron 3 UltravsGPT-6.1 SolMuse Wonder FullvsGemini 4 ArgonNemotron 3 UltravsGemini 4 ArgonMuse Wonder FullvsGrok 4.7Nemotron 3 UltravsGrok 4.7Muse Wonder FullvsDeepSeek-V4.1-Flash-1017Nemotron 3 UltravsDeepSeek-V4.1-Flash-1017Muse Wonder FullvsMistral Large 4Nemotron 3 UltravsMistral Large 4Frequently asked questions
Muse Wonder Full and Nemotron 3 Ultra don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Nemotron 3 Ultra shipped 130 days before Muse Wonder Full, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Muse Wonder Full) vs 1M (Nemotron 3 Ultra). Muse Wonder Full is closed, while Nemotron 3 Ultra is open source.
Direct benchmark comparisons are unavailable — Muse Wonder Full and Nemotron 3 Ultra don't publish scores on any of the same benchmarks.