SpaceXAI: Grok Imagine Image Quality

SpaceXAI: Grok Imagine Image Quality is an image generation and editing model from xAI. It sits in the image category and is built for fast, high-fidelity output, with a focus on photorealistic results. If you are already working with xAI models or evaluating image models for a product, this is the one to consider when output quality and turnaround both matter.

The model accepts text and image inputs and produces image output. That means you can drive it with a text prompt alone, or combine a prompt with reference images to guide the result. It supports up to three input references, so editing and image-to-image style workflows are possible without switching models. The context window is 65,536 tokens, and the maximum output is 58,982 tokens. Generated images can be requested at 1K or 2K resolution, and you can pick from a wide set of aspect ratios including 1:1, 3:4, 4:3, 9:16, 16:9, 2:3, 3:2, 9:19.5, 19.5:9, 9:20, 20:9, 1:2, 2:1, and auto. The n parameter is fixed at 1, so each request returns a single image.

Practical uses follow directly from those capabilities. Photorealistic generation at 1K or 2K suits product imagery, concept art, marketing visuals, and other assets where fidelity matters. Because the model takes reference images, you can use it for editing tasks such as restyling an existing photo, adjusting a composition, or producing variations that stay close to a supplied reference. The aspect ratio options cover both square and vertical formats for social and mobile placements, as well as wide formats for banners and headers. The auto option is useful when you would rather let the model choose a sensible framing from the prompt.

You call the model through api14's OpenAI-compatible API using the model id "x-ai/grok-imagine-image-quality". Because the interface follows the OpenAI schema, you can point an existing OpenAI client at api14 and change the model id, with no other structural changes. The request parameters it supports are logprobs, max_tokens, response_format, seed, temperature, top_logprobs, and top_p. The seed parameter is worth noting for image work, since it lets you reproduce or vary a generation. Image-specific options such as resolution, aspect ratio, and input references are passed alongside these parameters.

A few constraints are worth keeping in mind. Only one image is returned per request, so batches require multiple calls. Input references are capped at three, which is fine for most editing flows but limiting if you need to blend many sources. If your task is text generation or another modality rather than image output, a different model will fit better.