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. | 72B | 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 | 32k |
| These models have no shared benchmark scores. | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 60.4% | — |
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. | — | 50% |
| Overview | ||
| Company | Moonshot AI | Qwen |
| Release date | Jun 17 2025 | Nov 28 2024 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
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Kimi-Dev and QwQ-32B-Preview don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. QwQ-32B-Preview shipped 201 days before Kimi-Dev, so benchmark comparisons should account for the intervening progress.
Kimi-Dev has 72B parameters, while QwQ-32B-Preview has 32B. Context windows are 128k (Kimi-Dev) vs 32k (QwQ-32B-Preview).
Direct benchmark comparisons are unavailable — Kimi-Dev and QwQ-32B-Preview don't publish scores on any of the same benchmarks.