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. | 48B | 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. | 1M | 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 | Oct 30 2025 | Mar 6 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
Kimi LinearvsClaude Haiku 5.5QwQ-32BvsClaude Haiku 5.5Kimi LinearvsGPT-6.1 TerraQwQ-32BvsGPT-6.1 TerraKimi LinearvsGemini 4 CarbonQwQ-32BvsGemini 4 CarbonKimi LinearvsMuse Spark OpenQwQ-32BvsMuse Spark OpenKimi LinearvsGrok 4.7QwQ-32BvsGrok 4.7Kimi LinearvsDeepSeek-V4.1-Flash-1017QwQ-32BvsDeepSeek-V4.1-Flash-1017Frequently asked questions
Kimi Linear 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 238 days before Kimi Linear, so benchmark comparisons should account for the intervening progress.
Kimi Linear has 48B parameters, while QwQ-32B has 32B. Context windows are 1M (Kimi Linear) vs 128k (QwQ-32B).
Direct benchmark comparisons are unavailable — Kimi Linear and QwQ-32B don't publish scores on any of the same benchmarks.