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. | 236B | — |
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 |
| 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 | May 6 2024 | Oct 12 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
DeepSeek V2vsClaude Haiku 5.5Muse Wonder FullvsClaude Haiku 5.5DeepSeek V2vsGPT-6.1 SolMuse Wonder FullvsGPT-6.1 SolDeepSeek V2vsGemini 4 ArgonMuse Wonder FullvsGemini 4 ArgonDeepSeek V2vsGrok 4.7Muse Wonder FullvsGrok 4.7DeepSeek V2vsMistral Large 4Muse Wonder FullvsMistral Large 4DeepSeek V2vsKimi K3Muse Wonder FullvsKimi K3Frequently asked questions
DeepSeek V2 and Muse Wonder Full don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V2 shipped 889 days before Muse Wonder Full, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (DeepSeek V2) vs 1M (Muse Wonder Full). DeepSeek V2 is open weight, while Muse Wonder Full is closed.
Direct benchmark comparisons are unavailable — DeepSeek V2 and Muse Wonder Full don't publish scores on any of the same benchmarks.