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 | 48B |
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 30 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek Coder V2vsClaude Haiku 5.5Kimi LinearvsClaude Haiku 5.5DeepSeek Coder V2vsGPT-6.1 SolKimi LinearvsGPT-6.1 SolDeepSeek Coder V2vsGemini 4 ArgonKimi LinearvsGemini 4 ArgonDeepSeek Coder V2vsMuse Spark OpenKimi LinearvsMuse Spark OpenDeepSeek Coder V2vsGrok 4.7Kimi LinearvsGrok 4.7DeepSeek Coder V2vsMistral Large 4Kimi LinearvsMistral Large 4Frequently asked questions
DeepSeek Coder V2 and Kimi Linear don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek Coder V2 shipped 500 days before Kimi Linear, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder V2 has 16B/236B parameters, while Kimi Linear has 48B. Context windows are 128k (DeepSeek Coder V2) vs 1M (Kimi Linear).
Direct benchmark comparisons are unavailable — DeepSeek Coder V2 and Kimi Linear don't publish scores on any of the same benchmarks.