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openai/gpt-image-2.6-sunburstRuns on your balance at the prices above. New accounts start with free usage.
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GPT Image 2.6 Sunburst is an image generation and editing model from OpenAI. It is built for detail, with a focus on dense compositions, text-heavy layouts, and edits that keep details consistent from one edit to the next. It sits in the image category and is reached through the same OpenAI-compatible interface as the rest of the catalogue on api14.
The model accepts text and image inputs and returns images. Its context window is 400,000 tokens, with a maximum output of 360,000. Generation is controlled through a set of image parameters: aspect ratio (1:1, 3:2, 2:3, 4:3, 3:4, 16:9, 9:16, 21:9, or auto), output format (png, jpeg, or webp), quality (auto, low, medium, high, xhigh, or max), and background (auto, transparent, or opaque). You can request between 1 and 10 images per call, supply up to 16 input references, and set output compression anywhere from 0 to 100.
Those capabilities point to a few practical patterns. Dense, text-heavy layouts suit generated posters, UI mockups, slide artwork, and marketing assets where lettering has to stay legible. Because the model accepts image inputs alongside text, it also works for editing: pass a source image plus instructions and get a revised image back, with the emphasis on keeping details consistent across successive edits. Multiple input references help when a result needs to draw on several existing images, and the aspect ratio and background options make it straightforward to produce assets that drop into a specific layout or need a transparent backdrop.
You call it through api14's OpenAI-compatible API using the model id "openai/gpt-image-2.6-sunburst". The supported request parameters are max_tokens, response_format, seed, and structured_outputs, alongside the image parameters described above. The seed parameter is useful when you want reproducible output across runs, and structured_outputs lets you constrain the shape of what comes back. Because the endpoint follows the OpenAI format, existing OpenAI client libraries and code paths generally work with only the base URL and model id changed.
The main thing to weigh is that this is an image model: it takes text and images in and produces images out, so tasks that need text answers belong with a text model instead. Within image work, the quality and image count settings give you a direct trade between fidelity and volume, and the 16 reference limit is worth planning around if your workflow chains many source images together.
%3B%0A%0Aconst%20result%20%3D%20await%20client.images.generate(%7B%0A%20%20%22model%22%3A%20%22openai%2Fgpt-image-2.6-sunburst%22%2C%0A%20%20%22prompt%22%3A%20%22A%20minimalist%20product%20photo%20of%20a%20ceramic%20mug%20on%20a%20wooden%20table%22%2C%0A%20%20%22n%22%3A%201%2C%0A%20%20%22aspect_ratio%22%3A%20%221%3A1%22%0A%7D)%3B%0Aimport%20%7B%20writeFile%20%7D%20from%20%22node%3Afs%2Fpromises%22%3B%0Aawait%20writeFile(%22image.png%22%2C%20Buffer.from(result.data%5B0%5D.b64_json%2C%20%22base64%22))%3B%0A){kind=link}
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": "openai/gpt-image-2.6-sunburst",
"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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