Unverified50% confidenceFactExact time
Multimodal vision models (such as gemma4 and qwen3-vl) have a vision encoder, and images are cut into patches and encoded into visual tokens that are fed into the language model together with text tokens; their token consumption is far higher than pure-text inference and is positively correlated with image resolution.
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50%
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Long-term
Relevance
10/17/2026
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UnverifiedImages are divided into fixed-size patches, converted into visual tokens by the visual encoder, then mapped to the same embedding space as text tokens through a projection layer, and fed together with text tokens into the language model for joint inference.77% similarUnverifiedThe SmolVLM model accepts both image and text inputs: a vision encoder first splits the image into several visual tokens, which are then fed together with text tokens into the language model to decode an answer.77% similarVerifiedCurrent mainstream multimodal LLMs convert images into token sequences via a visual encoder (typically CLIP or ViT series) and then align them with the text space through a linear projection layer or cross-attention mechanism.72% similarUnverifiedMainstream large image generation models such as SD3, FLUX, and Qwen-Image-3.0 have broadly shifted toward an architecture that fuses LLM text encoders with DiT image backbones.72% similarUnverifiedQwen VL的视觉编码器基于ViT（Vision Transformer）架构，将图像像素信息压缩编码为语言模型可理解的token序列71% similar
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