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. | — | 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 | Jan 20 2025 | Mar 6 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Kimi K1.5vsClaude Haiku 5.5QwQ-32BvsClaude Haiku 5.5Kimi K1.5vsGPT-6.1 TerraQwQ-32BvsGPT-6.1 TerraKimi K1.5vsGemini 4 CarbonQwQ-32BvsGemini 4 CarbonKimi K1.5vsMuse Spark OpenQwQ-32BvsMuse Spark OpenKimi K1.5vsGrok 4.7QwQ-32BvsGrok 4.7Kimi K1.5vsDeepSeek-V4.1-Flash-1017QwQ-32BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Kimi K1.5 and QwQ-32B don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Kimi K1.5 shipped 45 days before QwQ-32B, so benchmark comparisons should account for the intervening progress.
Context windows are 128k (Kimi K1.5) vs 128k (QwQ-32B). Kimi K1.5 is closed, while QwQ-32B is open weight.
Direct benchmark comparisons are unavailable — Kimi K1.5 and QwQ-32B don't publish scores on any of the same benchmarks.