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. | 671B | — |
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 · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | — | $0.10 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | — | $0.50 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | — | $0.004 |
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. | $0.27SiliconFlow | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.41SiliconFlow | — |
| $74.50SiliconFlow · fp8 | $45.00Meta list price | |
| These models have no shared benchmark scores. | ||
AA Coding Agent IndexAgentic coding — Artificial Analysis' overall score for coding agents, combining three coding benchmarks with what each run costs and how many tokens it burns. It rates a model paired with a particular agent harness rather than the model alone, so the same model scores differently in different tools. Higher is better. | — | 46 |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | — | 40.9% |
AutomationBench (Artificial Analysis)Business workflows — The same business-workflow test, run by Artificial Analysis rather than by the lab, and graded strict pass@1: a workflow counts only if the whole of it is right on the first attempt. Read it as a separate board from the lab-run AutomationBench scores. Higher is better. | — | 37.8% |
AA Intelligence Index v5.0Overall intelligence — Artificial Analysis composite intelligence index, version 5.0 with tools; separate from earlier index editions | — | 36 |
GDP.PDF (Artificial Analysis)Document comprehension — The same professional-PDF test, run by Artificial Analysis rather than by the lab. Read it as a separate board from the lab-run GDP.PDF scores. Higher is better. | — | 22.8% |
| Overview | ||
| Company | DeepSeek | Meta |
| Release date | Sep 29 2025 | Nov 9 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
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DeepSeek V3.2 Exp and Muse Ember don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Muse Ember has a verified first-party price for an available API: $0.10 per million input tokens and $0.50 per million output tokens. No currently available pay-as-you-go API rate is tracked for DeepSeek V3.2 Exp. DeepSeek V3.2 Exp shipped 406 days before Muse Ember, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (DeepSeek V3.2 Exp) vs 1M (Muse Ember). DeepSeek V3.2 Exp is open weight, while Muse Ember is closed.
Direct benchmark comparisons are unavailable — DeepSeek V3.2 Exp and Muse Ember don't publish scores on any of the same benchmarks.