Thanks for the examples and the link to the Kimi docs, that’s a good, specific request. I also see the screenshot where kimi.ai reads the image correctly.
You’re right: the base Kimi K3 model is multimodal, and the official Moonshot/kimi.ai endpoint serves that version. There’s no contradiction here. The difference isn’t the model architecture, it’s how Kimi K3 is currently available in Cursor. The serving deployment we have right now is text-only. So it’s the same model, but the specific deployment Cursor connects to can’t accept images natively.
Because of that, when you attach an image it goes through a separate helper model that writes a text description, and only that description gets sent to Kimi, not the actual pixels. That’s why you can get confident but inaccurate answers, especially for screenshots with small text.
This isn’t an intentional downgrade, it’s just what’s available to us right now. As soon as we have a natively multimodal Kimi K3 option, we’ll hook it up.
Your point that the UI and docs don’t say this clearly (and that the model presents guesses as real observations) is fair. I’ve shared this with the team so we can make it clearer.
Practical tip for now: for image tasks (reading text from screenshots, UI, logos, error dialogs), use a natively multimodal model like Grok 4.5, Claude, GPT, or Gemini. They receive the actual image, not a description.