Krea: K3 Edit vs Microsoft AI: MAI-Image-2.6
A side-by-side comparison of Krea: K3 Edit ($0.11 / image) and Microsoft AI: MAI-Image-2.6 ($17.50 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| Krea: K3 Edit | Microsoft AI: MAI-Image-2.6 | |
|---|---|---|
| Provider | Krea | Microsoft |
| Image output | $0.11 / image | $133.00 / 1M tokens |
| Image input | $0.04 / image | $133.00 / 1M tokens |
| Input | - | $17.50 / 1M tokens |
| Context window | 65,536 | 4,096 |
| Max output | 58,982 | 1,024 |
| Input | text, image | text, image |
| Output | image | image |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Oct 15, 2026 | Sep 4, 2026 |
Key differences
- Krea: K3 Edit reads 16.0× more context (65,536 tokens).
In depth
Krea: K3 Edit is an image model from Krea, and Microsoft AI: MAI-Image-2.6 is an image model from Microsoft. Both sit in the image category, both take text and images as input, and both return images as output. Neither one produces text, so you are not choosing between a chat model and an image model here. You are choosing between two image models that accept the same kinds of prompts and reference images, from two different providers.
The clearest difference is context length. Krea: K3 Edit supports 65536 tokens of context, while MAI-Image-2.6 supports 4096. That makes Krea: K3 Edit the larger context option by a wide margin, which matters when your prompt includes long instructions or several reference images that all need to be considered together. The two models also differ in supported parameters. MAI-Image-2.6 exposes max_completion_tokens, max_tokens and temperature, so you can tune sampling behaviour and cap output size on the request. Krea: K3 Edit lists no supported parameters, so you send your input and take what comes back. Input and output modalities are otherwise the same on both sides.
For practical work, the context gap should drive most decisions. If your task involves detailed edit instructions, multi image references, or anything where the surrounding prompt is long, Krea: K3 Edit gives you the room to express it. If your task is a short, direct image request and you want control over temperature or output token limits, MAI-Image-2.6 is the more adjustable of the two. The qualitative comparison marks Krea: K3 Edit as the bigger context model and leaves price differences unstated, so cost is not a reliable tiebreaker from the facts here. Pick on context and parameter control rather than on price.
Calling either model through api14 works the same way because the API is OpenAI compatible. You send a request to the same endpoint with the same request shape, and the only thing that changes is the model id. Use "krea/k3-edit" for Krea: K3 Edit and "microsoft/mai-image-2.6" for MAI-Image-2.6. Your messages, image inputs and response handling stay identical, so switching between them is a one line change and you can compare outputs on your own prompts without rewriting integration code.
There are still cases where MAI-Image-2.6 is the better pick. If you need to set temperature or cap completion tokens on the request, it is the only one of the two that supports those parameters, and its smaller context window is not a limitation for short prompts. Krea: K3 Edit remains the choice when context length is the constraint you care about.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "krea/k3-edit" to "microsoft/mai-image-2.6" and prompts, streaming and tools keep working.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api14.com/api/v1",
apiKey: process.env.API14_API_KEY,
});
const result = await client.images.generate({
"model": "microsoft/mai-image-2.6",
"prompt": "A minimalist product photo of a ceramic mug on a wooden table",
"n": 1,
"aspect_ratio": "1:1"
});
import { writeFile } from "node:fs/promises";
await writeFile("image.png", Buffer.from(result.data[0].b64_json, "base64"));
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