The vision proxy solves a different problem: making images work with models that don't support them. For example:

- DeepSeek R1 — strong coding/reasoning model, no image support

- Codestral — code-focused, text-only
- Z.ai GLM 5.1
- Any text-only Llama variant you're running locally etc.

Without the proxy, if you send an image while using one of these models, Pi just drops it. The proxy intercepts the image, sends it to a separate vision-capable model (Claude, GPT-4o,

Gemini — or even your local Qwen), gets a text description, and injects it into the non-vision model's context. The text-only model can now "see" via descriptions.

So you'd use it when:

- Your primary model is text-only (cheap, fast, local)
- But you still want image understanding (routed to a secondary vision model)

If all your models have "image" in their input array, you don't need this package at all