Krea: Krea 2 Large vs Microsoft AI: MAI-Image-2.6
A side-by-side comparison of Krea: Krea 2 Large ($0.21 / 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: Krea 2 Large | Microsoft AI: MAI-Image-2.6 | |
|---|---|---|
| Provider | Krea | Microsoft |
| Image output | $0.21 / image | $133.00 / 1M tokens |
| Input | - | $17.50 / 1M tokens |
| Image input | - | $133.00 / 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 | Jul 20, 2026 | Sep 4, 2026 |
Key differences
- Krea: Krea 2 Large reads 16.0× more context (65,536 tokens).
In depth
Krea: Krea 2 Large is an image model from Krea, and Microsoft AI: MAI-Image-2.6 is an image model from Microsoft. Both are listed on api14 under the image category, and both take text and image input while returning image output. The Krea model was released on 2026-07-20, and the Microsoft model followed on 2026-09-04.
The clearest difference between them is context length. Krea 2 Large accepts up to 65536 tokens, while MAI-Image-2.6 accepts up to 4096 tokens. That makes the Krea model the one with the bigger context, which matters when your prompt, reference material or conversation history is long. On modalities the two are the same: text and image in, image out. They also differ in parameter support. MAI-Image-2.6 exposes max_completion_tokens, max_tokens and temperature, so you can tune generation length and sampling behavior on the request. Krea 2 Large lists no supported parameters, so you send the model id and your input and take the defaults. The qualitative comparison shows no meaningful difference in input or output price, so cost is not a deciding factor between them here; check the live price table on the page for the current numbers.
For most work, pick based on how much input you need to send. If you are feeding long prompts, several reference images described in text, or a running thread of prior turns, Krea 2 Large gives you far more room before you have to trim anything. If your requests are short and self contained, and you want control over temperature or a token cap, MAI-Image-2.6 is the more adjustable of the two. A typical short prompt with one reference image fits comfortably in either model, so the parameter controls are the tiebreaker there. When in doubt and your input is small, either will do the job.
Calling either one through api14 works the same way, because the API is OpenAI compatible. You point your existing client at the api14 base URL, send your request to the chat completions endpoint, and set the model field. To use the Krea model, set model to "krea/krea-2-large". To use the Microsoft model, set model to "microsoft/mai-image-2.6". Nothing else in the request shape has to change, so switching between them is a one line edit in your config or code. If you rely on temperature or a token limit, keep in mind that those fields belong to the Microsoft model and are not listed as supported on the Krea side.
There is a case where MAI-Image-2.6 is still the better pick even though Krea 2 Large has the larger context. If your prompts are short but you need to vary temperature across attempts, or you want to cap output tokens explicitly, the Microsoft model is the one that supports those parameters. The Krea model remains the choice when input length is the constraint.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "krea/krea-2-large" 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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