• chain-of-thought — Having a model write out intermediate reasoning steps before its answer, which markedly improves performance on hard problems.

QVQ extends chain-of-thought reasoning into the visual domain, letting a model reason step-by-step over images rather than just captioning them — useful if you're building agents that need to interpret diagrams, screenshots, or visual evidence.

• articleChinese CLIP: Contrastive Vision-Language Pretraining in Chinese
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• articleGeneralizing an LLM from 8k to 1M Context using Qwen-Agent

• articleQVQ-Max: Think with EvidenceQwen Team
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• articleQwQ: Reflect Deeply on the Boundaries of the UnknownQwen Team

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