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. | 16B/236B | 2.8T |
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 |
| Overview | ||
| Company | DeepSeek | Moonshot AI |
| Release date | Jun 17 2024 | Oct 23 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
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DeepSeek Coder V2 and Kimi K3.1 Pro Highspeed don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek Coder V2 shipped 858 days before Kimi K3.1 Pro Highspeed, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder V2 has 16B/236B parameters, while Kimi K3.1 Pro Highspeed has 2.8T. Context windows are 128k (DeepSeek Coder V2) vs 1M (Kimi K3.1 Pro Highspeed). DeepSeek Coder V2 is open weight, while Kimi K3.1 Pro Highspeed is closed.
Direct benchmark comparisons are unavailable — DeepSeek Coder V2 and Kimi K3.1 Pro Highspeed don't publish scores on any of the same benchmarks.