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 | 32B |
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 |
| These models have no shared benchmark scores. | ||
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | — | 63.4% |
| Overview | ||
| Company | Moonshot AI | Qwen |
| Release date | Apr 10 2025 | Mar 6 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Kimi-VLvsClaude Haiku 5.5QwQ-32BvsClaude Haiku 5.5Kimi-VLvsGPT-6.1 TerraQwQ-32BvsGPT-6.1 TerraKimi-VLvsGemini 4 CarbonQwQ-32BvsGemini 4 CarbonKimi-VLvsMuse Spark OpenQwQ-32BvsMuse Spark OpenKimi-VLvsGrok 4.7QwQ-32BvsGrok 4.7Kimi-VLvsDeepSeek-V4.1-Flash-1017QwQ-32BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Kimi-VL and QwQ-32B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. QwQ-32B shipped 35 days before Kimi-VL, so benchmark comparisons should account for the intervening progress.
Kimi-VL has 16B parameters, while QwQ-32B has 32B. Context windows are 128k (Kimi-VL) vs 128k (QwQ-32B).
Direct benchmark comparisons are unavailable — Kimi-VL and QwQ-32B don't publish scores on any of the same benchmarks.