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 | 816B |
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 | ||
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 | $1.25BaseTen |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.41SiliconFlow | $4.25BaseTen |
| $74.50SiliconFlow · fp8 | $463Modal | |
| 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. | — | 60% |
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. | — | 76.4% |
MCP AtlasMulti-step tool use — Can the AI chain together many tools and steps to complete one bigger task, rather than doing just a single thing? Higher is better. | — | 89.2% |
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. | — | 61.8% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1401 |
| Overview | ||
| Company | DeepSeek | Meta |
| Release date | Sep 29 2025 | Oct 15 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.2 ExpvsClaude Haiku 5.5Muse Spark OpenvsClaude Haiku 5.5DeepSeek V3.2 ExpvsGPT-6.1 SolMuse Spark OpenvsGPT-6.1 SolDeepSeek V3.2 ExpvsGemini 4 ArgonMuse Spark OpenvsGemini 4 ArgonDeepSeek V3.2 ExpvsGrok 4.7Muse Spark OpenvsGrok 4.7DeepSeek V3.2 ExpvsMistral Large 4Muse Spark OpenvsMistral Large 4DeepSeek V3.2 ExpvsKimi K3Muse Spark OpenvsKimi K3Frequently asked questions
DeepSeek V3.2 Exp and Muse Spark Open don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek V3.2 Exp shipped 381 days before Muse Spark Open, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.2 Exp has 671B parameters, while Muse Spark Open has 816B. Context windows are 128k (DeepSeek V3.2 Exp) vs 1M (Muse Spark Open).
Direct benchmark comparisons are unavailable — DeepSeek V3.2 Exp and Muse Spark Open don't publish scores on any of the same benchmarks.