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. | 200k | 1M |
| 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. | 62.3% | — |
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% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 68% | — |
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 | Anthropic | Meta |
| Release date | Feb 24 2025 | Oct 12 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Claude 3.7 SonnetvsGPT-6.1 SolMuse Wonder FullvsGPT-6.1 SolClaude 3.7 SonnetvsGemini 4 ArgonMuse Wonder FullvsGemini 4 ArgonClaude 3.7 SonnetvsGrok 4.7Muse Wonder FullvsGrok 4.7Claude 3.7 SonnetvsDeepSeek-V4.1-Flash-1017Muse Wonder FullvsDeepSeek-V4.1-Flash-1017Claude 3.7 SonnetvsMistral Large 4Muse Wonder FullvsMistral Large 4Claude 3.7 SonnetvsKimi K3Muse Wonder FullvsKimi K3Frequently asked questions
Claude 3.7 Sonnet and Muse Wonder Full don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Claude 3.7 Sonnet shipped 595 days before Muse Wonder Full, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3.7 Sonnet) vs 1M (Muse Wonder Full).
Direct benchmark comparisons are unavailable — Claude 3.7 Sonnet and Muse Wonder Full don't publish scores on any of the same benchmarks.