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. | 1.3B/5.7B/6.7B/33B | 16B |
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. | 16k | 128k |
| Overview | ||
| Company | DeepSeek | Moonshot AI |
| Release date | Nov 2 2023 | Apr 10 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek CodervsClaude Haiku 5.5Kimi-VLvsClaude Haiku 5.5DeepSeek CodervsGPT-6.1 SolKimi-VLvsGPT-6.1 SolDeepSeek CodervsGemini 4 ArgonKimi-VLvsGemini 4 ArgonDeepSeek CodervsMuse Spark OpenKimi-VLvsMuse Spark OpenDeepSeek CodervsGrok 4.7Kimi-VLvsGrok 4.7DeepSeek CodervsMistral Large 4Kimi-VLvsMistral Large 4Frequently asked questions
DeepSeek Coder and Kimi-VL don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. DeepSeek Coder shipped 525 days before Kimi-VL, so benchmark comparisons should account for the intervening progress.
DeepSeek Coder has 1.3B/5.7B/6.7B/33B parameters, while Kimi-VL has 16B. Context windows are 16k (DeepSeek Coder) vs 128k (Kimi-VL).
Direct benchmark comparisons are unavailable — DeepSeek Coder and Kimi-VL don't publish scores on any of the same benchmarks.