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. | 7B | — |
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. | 4k | 1M |
| 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. | — | 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 | DeepSeek | Meta |
| Release date | Feb 5 2024 | Oct 12 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek-MathvsClaude Haiku 5.5Muse Wonder FullvsClaude Haiku 5.5DeepSeek-MathvsGPT-6.1 SolMuse Wonder FullvsGPT-6.1 SolDeepSeek-MathvsGemini 4 ArgonMuse Wonder FullvsGemini 4 ArgonDeepSeek-MathvsGrok 4.7Muse Wonder FullvsGrok 4.7DeepSeek-MathvsMistral Large 4Muse Wonder FullvsMistral Large 4DeepSeek-MathvsKimi K3Muse Wonder FullvsKimi K3Frequently asked questions
DeepSeek-Math and Muse Wonder Full don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-Math shipped 980 days before Muse Wonder Full, so benchmark comparisons should account for the intervening progress.
Context windows are 4k (DeepSeek-Math) vs 1M (Muse Wonder Full). DeepSeek-Math is open weight, while Muse Wonder Full is closed.
Direct benchmark comparisons are unavailable — DeepSeek-Math and Muse Wonder Full don't publish scores on any of the same benchmarks.