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. | 7B/67B | 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. | 4k | 1M |
| Overview | ||
| Company | DeepSeek | Moonshot AI |
| Release date | Nov 29 2023 | Oct 30 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-LLMvsClaude Haiku 5.5Kimi LinearvsClaude Haiku 5.5DeepSeek-LLMvsGPT-6.1 SolKimi LinearvsGPT-6.1 SolDeepSeek-LLMvsGemini 4 ArgonKimi LinearvsGemini 4 ArgonDeepSeek-LLMvsMuse Spark OpenKimi LinearvsMuse Spark OpenDeepSeek-LLMvsGrok 4.7Kimi LinearvsGrok 4.7DeepSeek-LLMvsMistral Large 4Kimi LinearvsMistral Large 4Frequently asked questions
DeepSeek-LLM and Kimi Linear don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek-LLM shipped 701 days before Kimi Linear, so benchmark comparisons should account for the intervening progress.
DeepSeek-LLM has 7B/67B parameters, while Kimi Linear has 48B. Context windows are 4k (DeepSeek-LLM) vs 1M (Kimi Linear).
Direct benchmark comparisons are unavailable — DeepSeek-LLM and Kimi Linear don't publish scores on any of the same benchmarks.