Kimi K3 for Vision: Images, Video and Charts

K3's vision is native rather than bolted on: MoonViT-V2, a 401M-parameter encoder trained as part of the model rather than adapted onto it afterwards. It handles images and video, and it posts 81.6/83.4 on MMMU-Pro — a benchmark specifically designed to resist the shortcut of answering from text alone.

This page is written for teams building multimodal features or analysing visual material at scale. If you want the model overview first, start with the complete Kimi K3 guide.

Best effort level	high (the API default is max)
Context available	1M tokens — enough for real working material, not just snippets
Cost	$3.00 per million input tokens, $15.00 per million output, $0.30 cached input

Why Kimi K3 suits image and video understanding

• MoonViT-V2 (401M) is trained into the model, not attached to it, which generally shows up as better integration between what it sees and what it reasons about.
• Video input is supported natively — a genuine differentiator, since most models that claim multimodality mean still images.
• 81.6/83.4 on MMMU-Pro, a benchmark built to defeat models that guess from the question text without really reading the image.
• The 1M tokens window means many images in a single conversation, not one at a time.
• Charts, tables, and scanned documents work as direct input, which removes a whole OCR stage from most document pipelines.

A workflow that works

1. Encode as base64 or upload first. The API rejects public image URLs. Either inline base64 or upload the file and reference ms://<file-id>.
2. Ask what is in the image before asking about it. A one-line description request is a cheap check that the model is reading what you think you sent — especially with multi-page scans.
3. Downsample when detail is not load-bearing. Images consume real tokens. A full-resolution screenshot to answer "is this button blue" is waste.
4. Send video for temporal questions only. If the answer is in a single frame, send the frame. Video costs far more and adds nothing on static questions.
5. Pair charts with their captions. Reading a value off an axis is the kind of task where the surrounding text resolves ambiguity the pixels cannot.

Settings to start from

Set reasoning_effort to high. Description and extraction are not deep-reasoning tasks — high is plenty and cheaper than the max default. Raise to max when the visual is the evidence rather than the subject: reading a trend off a badly-labelled chart, or reconciling a diagram against a written spec.

Remember that reasoning cannot be turned off on this model, and that the API default is max — the most expensive of the three levels. Setting it explicitly is the single highest-value line of configuration you will write. See reasoning effort explained if you want to tune it properly.

What to watch out for

• No public image URLs. The most common integration failure, and the one that costs people an afternoon. Base64 or ms://<file-id>.
• Images are not cheap. High-resolution input consumes substantial context. At scale this dominates your bill before the text does.
• Precise numeric readout from charts remains a weak spot for all current models. Ask for the underlying data if you have it, rather than asking the model to eyeball a value you will then rely on.
• It does not generate images. K3 analyses visual input; it does not produce it. Pair it with a dedicated image model if you need both.

The verdict

One of the more capable open-weight vision stacks available, and native video support genuinely sets it apart. Budget for image tokens, get the base64 detail right on day one, and use high effort unless the image is the evidence rather than the subject.

If cost is the constraint, read how to cut Kimi K3 costs before downgrading — caching and effort levels usually beat switching models.

More on Kimi K3

Start with the complete Kimi K3 guide for the overview, or go deeper:

• Kimi K3 vs ChatGPT: A Model Against a Product
• K3 Max vs K3 Swarm Max: Which Variant Do You Need?
• Kimi K3 for Long Documents: Using the Full 1M-Token Window