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 | 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. | 128k | 128k |
| 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. | $0.2088GMICloud | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.3096GMICloud | — |
| $57.24GMICloud · fp8 | — | |
| These models have no shared benchmark scores. | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 13% | — |
| Overview | ||
| Company | DeepSeek | Moonshot AI |
| Release date | Dec 1 2025 | Apr 10 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek-V3.2vsClaude Haiku 5.5Kimi-VLvsClaude Haiku 5.5DeepSeek-V3.2vsGPT-6.1 SolKimi-VLvsGPT-6.1 SolDeepSeek-V3.2vsGemini 4 ArgonKimi-VLvsGemini 4 ArgonDeepSeek-V3.2vsMuse Spark OpenKimi-VLvsMuse Spark OpenDeepSeek-V3.2vsGrok 4.7Kimi-VLvsGrok 4.7DeepSeek-V3.2vsMistral Large 4Kimi-VLvsMistral Large 4Frequently asked questions
DeepSeek-V3.2 and Kimi-VL don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi-VL shipped 235 days before DeepSeek-V3.2, so benchmark comparisons should account for the intervening progress.
DeepSeek-V3.2 has 671B parameters, while Kimi-VL has 16B. Context windows are 128k (DeepSeek-V3.2) vs 128k (Kimi-VL).
Direct benchmark comparisons are unavailable — DeepSeek-V3.2 and Kimi-VL don't publish scores on any of the same benchmarks.