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This article details the implementation of a unified API interface for multi-provider image generation, addressing fragmentation in endpoints and billing models within an application workflow. By standardizing request formats and response handling across various image models, it simplifies the integration of generative visual capabilities into software products. While the content involves coding tutorials, the core subject matter is the infrastructure and productization of AI image generation services.

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OpenRouter Image Generation: A Code-First API Tutorial

OpenRouterAugust 17, 20266 min read

Supporting more than one image provider means handling different endpoints, data formats, and billing models. Our dedicated Image API gives you one request format and one key across supported models. Here's the full prompt-to-local-file workflow in Python and JavaScript.

Adding image generation to an app gets harder when you need to support more than one provider. Dozens of image models across many providers use different endpoints, data formats, controls, and billing models, with charges calculated per image, megapixel, or token. We address this integration problem with a dedicated Image generation API that uses one request format and one key across supported models. One API and one key reach supported image models through POST /api/v1/images. Buffered responses place the generated image in data[0].b64_json, which you decode and save locally. Compatible models accept an optional reference image through input_references. In this guide, you’ll build runnable Python and JavaScript flows that send a prompt, decode and…

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