Vision-based chatbots move beyond text by interpreting images inside a conversational loop. Whether you are building a support agent that reads screenshots or a coding assistant that critiques UI mockups, the architecture follows a consistent pattern. A vision-language model (VLM) consumes image inputs alongside text prompts across multiple turns. The gap between a prototype and a production-ready system usually comes down to how you manage context, select models, and handle pricing.
Architecture of a Vision-Based Chatbot
A typical pipeline has three layers. The client captures or uploads an image. A preprocessing service resizes the image and encodes it to base64 or uploads it to a temporary URL. The backend then assembles a message payload and forwards it to a chat completions endpoint that supports vision. Because Oxlo.ai exposes a fully OpenAI-compatible API at https://api.oxlo.ai/v1, you can reuse existing OpenAI SDK logic without refactoring your request builders.
Vision Model Selection
Not every LLM accepts image inputs. You need a VLM. Oxlo.ai lists several options across its catalog. For general vision tasks, Gemma 3 27B offers strong image understanding. For workflows that mix visual reasoning with code generation, Kimi K2.6 supports vision, advanced reasoning, and agentic coding inside a 131K context window. There is also Kimi VL A3B, a dedicated vision-language model. When you choose, verify that the model supports the features you plan to use, such as function calling or JSON mode, because not all vision endpoints expose the same toolset.
Preparing Images for the API
Most vision endpoints accept either a public URL or
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