How to choose an AI model for your task and media
Start with the required output, available media, and essential controls. Compare current modes, settings, and credit costs with a small controlled test.
“Which AI model is best?” is rarely a useful question on its own. A model may be strong at turning a product photo into a poster but unreliable at preserving small text. Another may make an attractive video clip without supporting the first-and-last-frame control or duration your brief requires. The useful question is narrower: for this task, these source files, and this acceptable cost, which models deserve a test?
This guide turns that decision into seven steps. It does not offer a context-free leaderboard, and you do not need to learn every technical term first. Define the result, inputs, hard constraints, and acceptance criteria; then use the Model Hub to reduce the shortlist and spend credits on comparisons that can teach you something.
1. Define the deliverable first
Write one sentence that can be accepted or rejected: “Create a square product hero image while preserving the packaging text,” “turn this vertical portrait into a five-second clip with subtle camera movement,” or “generate a Mandarin dialogue for two characters.” A concrete sentence reveals the output type, aspect ratio or duration, details that must survive, and the intended use.
If the sentence contains several deliverables, split it. Image, video, audio, and chat are different workflows. A video request that combines major action, a scene change, a costume change, and accurate text may also need several separately reviewable shots. Model selection begins with a task that can be completed, not a long wish list.
2. List your source media and usage rights
Do you have only a written brief, or do you already have images, video, or audio? The answer determines the mode. With no source media, you need a text-led workflow. With a product photo, image-to-image or image-to-video becomes relevant. With an existing clip that needs a controlled change, the candidate must expose video editing in the current site interface.
Check quality and rights at the same time. Is the subject cropped? Is important text legible? Are you allowed to use the person, brand, music, or recording? Does the media contain private information that should not be submitted? A more popular model does not repair a flawed comparison baseline.
3. Turn non-negotiable requirements into filters
Separate “must have” from “nice to have.” A must-have might be image-to-video, vertical output, first-and-last-frame control, two reference images, a minimum duration, or multi-speaker speech. Speed, a particular visual style, maximum resolution, or completing a whole sequence in one run may be negotiable.
Remove models that fail the must-haves before comparing the remaining controls and costs. This prevents a common mistake: choosing a model because its examples look impressive even though the current Kyeo AI workflow lacks the input mode your task needs. Popularity can help you discover a candidate; it cannot replace the brief.
4. Check current on-site limits on the model page
Open the relevant image, video, audio, or chat category and confirm whether each model is available, unavailable, or listed for reference. Then review its inputs, file requirements, duration or resolution, adjustable settings, credit information, limitations, and alternatives.
Model pages distinguish public background from what the current Kyeo AI interface actually exposes. Do not assume that every feature described by the model maker is present here. Reference count, editing mode, audio input, web access, and safety controls are especially important to confirm in the live form.
5. Count both credits and the cost of rework
A lower credit price per run does not guarantee a lower total cost. If a model repeatedly changes the subject or lacks a required control, it may cost more than a higher-priced option that fits the task. Record estimated credits, output specification, likely number of rounds, and whether a failed direction requires new source material.
The first test should validate direction, not deliver the maximum specification. A short duration, default resolution, or small sample can confirm subject stability, motion, and composition. Billing dimensions differ between models, so never carry the cost assumption from one model to another; read the current estimate before every submission.
6. Run a small comparison with the same input
Choose two or three candidates that meet the hard requirements. Hold the source media, prompt, aspect ratio, and main specifications constant. Change only the model in the first round. If the prompt needs revision, confirm that change within one model before starting another cross-model comparison.
Judge the output against the deliverable: subject consistency, text accuracy, completion of motion, camera control, voice naturalness, editability, and credits. A one-sentence observation per criterion is more honest than a precise-looking score without a repeatable benchmark. After a few rounds, you have evidence from your own media instead of somebody else’s leaderboard.
7. Record why you chose the model
Alongside the final output, save the model, mode, key settings, source version, estimated credits, and the reason for the decision. For example: “Candidate A produced a more natural person but did not meet the first-and-last-frame requirement. Candidate B was more restrained and exposed the required input mode, so we chose B.” That record can be reused.
Availability, settings, and credits change, so the decision is not permanent. The last-reviewed date tells you when a page was checked. Before repeating an important job, confirm that the model is still available, the fields have not changed, and a small sample still meets the acceptance criteria. An explainable, updateable decision is more durable than “this model is best.”
Questions about choosing a model
Is there one AI model that works for every image or video task?
No. Inputs, controls, specifications, speed, and credit costs vary by model and mode. Define the task first, then compare a small sample from models that satisfy the hard requirements.
What should I trust if a model page and the workbench differ?
At submission, use the live fields, media requirements, and estimated credits in the workbench, and report the difference through the contact page. A model page helps with shortlisting but does not replace the final check.
How many models should I test at once?
Two or three candidates that meet the hard requirements are usually enough. Keep the media, prompt, and main specifications fixed, and change only the model or one key variable per round.
Build a shortlist you can explain
Confirm which models are currently available, then compare inputs, controls, and credits on the same task. You do not need to test everything.