Why can some DeepSeek text models not read images directly while vision models can?

Professional explanation

From an architectural standpoint, pure text models—such as certain variants in the DeepSeek-V2 series—rely solely on a Transformer decoder that processes sequences of text tokens produced by a tokenizer. The tokenizer breaks natural language into subword units, each mapped to a fixed-dimensional embedding vector. The model learns dependencies among tokens via self-attention, but the entire pipeline lacks any module to convert pixel information into semantic vectors. Consequently, when an image is supplied, the model cannot interpret the non-text binary data and treats it as invalid input or raises an error.

Vision models like DeepSeek-VL2 adopt a multimodal architecture that augments text processing with a vision encoder. Typically based on a Vision Transformer (ViT) or similar structure, the vision encoder divides an input image into fixed-size patches, which are then linearly projected and combined with position encodings to form a sequence of visual tokens. These visual tokens are aligned with text embeddings in a shared space, enabling the model to jointly attend to textual and visual features within the same Transformer decoder, thus achieving cross-modal understanding.

A critical technical challenge is modality alignment. Visual features from the encoder reside in a different semantic space than text embeddings, and naively concatenating them would confuse the model. DeepSeek-VL2 addresses this by training a projection layer or adapter that maps visual features to the same dimensionality as text embeddings. The model is then pretrained on large-scale image-text pairs, learning to associate visual concepts with corresponding textual descriptions. This process demands substantial computational resources and careful training strategies, making it infeasible to achieve through simple fine-tuning of a text-only model.

Regarding input processing, a text model receives an integer sequence from the tokenizer, whereas a vision model requires additional preprocessing: images are resized, cropped to a fixed resolution, normalized, and fed into the vision encoder. The resulting feature maps are pooled or flattened, then passed through a projection layer to produce vectors of the same length as text tokens. These vectors are inserted into the text token sequence at designated positions (often using special tokens like <image>), and the model learns interactions between image and text tokens via self-attention. A pure text model lacks this entire visual processing pipeline and thus cannot parse image data.

Training data and objectives also define capability boundaries. Pure text models are trained exclusively on text corpora with the objective of predicting the next text token, storing linguistic and world knowledge in their parameters. Vision models, in contrast, are trained on interleaved image-text data, often using combined objectives such as image-text contrastive learning (e.g., CLIP-style), image-text matching, and language modeling. For instance, DeepSeek-VL2 is pretrained on massive image-text pairs and interleaved documents, endowing it with visual grounding—the ability to understand image content and generate relevant text. Text-only models lack such experience.

Inference computation differs markedly. Text model inference involves only forward propagation of text tokens, resulting in a relatively simple computational graph. Vision models must first run the vision encoder to convert an image into visual tokens, which are then concatenated with text tokens before being fed into the decoder. This significantly increases computational overhead, especially for high-resolution images where the number of visual tokens can far exceed text tokens, leading to higher latency and memory consumption. Thus, even if a text model could theoretically be extended with an external vision module, practical deployment requires careful efficiency–cost trade-offs.

From an API design perspective, DeepSeek's text model APIs (e.g., deepseek-chat) accept only text input, as the underlying model architecture does not support image processing. Vision model APIs (e.g., DeepSeek-VL2 series) explicitly accept image inputs, with documentation specifying supported formats and size limits. This separation is not an arbitrary restriction but reflects the fundamental difference in model capabilities. Developers attempting to send image data to a text model API will receive a parameter error or unsupported input format response because the server cannot convert the image into a representation the model understands.

The trade-off between model scale and multimodal capability is also important. Pure text models can maximize language performance within a given parameter budget, while vision models must allocate some parameters to the vision encoder and cross-modal alignment modules, potentially sacrificing pure text performance. DeepSeek optimizes for different application scenarios: for primarily text-based tasks (e.g., dialogue, code generation), efficient text-only models are provided; for tasks requiring image understanding (e.g., visual question answering, chart analysis), dedicated vision models are offered. This product strategy allows users to choose the most suitable model without paying extra for unneeded visual capabilities.

Finally, the limitations of fine-tuning and transfer learning are noteworthy. While it is theoretically possible to add a vision encoder to a text model and perform multimodal fine-tuning, this requires redesigning the architecture, preparing large-scale image-text data, and conducting pretraining from scratch or expensive continual training. For deployed text models, such a change is equivalent to training a new model and cannot be achieved through simple parameter updates. Therefore, DeepSeek opts to release standalone vision models like DeepSeek-VL2 rather than patching existing text models, ensuring stability and performance.