aiqbench analysis
The article focuses on benchmarking image generation models across pricing structures, editing capabilities, and quality metrics, making AI the central subject. It provides a practical comparison of costs and performance for developers selecting specific generative AI tools.
Image Generation Models Compared: Cost, Edit, Quality

Image models are priced per token, per megapixel, or per image, so their listed rates do not compare. We sent the same prompt to 20 of them through the Image API, read usage.cost off each response, and tested text rendering, reference-image editing, seeds, and text-plus-image replies.
We route more than 50 image generation models through one API. We ran the same prompt through 20 of them and recorded what each call billed, what it returned, and what it accepts. Cost is the hard part, because these models are not sold in the same unit. FLUX.2 is priced per megapixel. Gemini, OpenAI, and Microsoft MAI are priced per token. Grok, Recraft, Riverflow, Qwen, and Seedream are priced per image, at a rate that changes with the quality and resolution you ask for. Each model page is correct in its own unit, so you cannot compare two of them by reading their prices side by side. We put them on the same footing. The 20 models we tested all got the same prompt at the same size, and we read the usage.cost field from each response, which is the amount we charged. Every price on this page came from a real generation, measured on 11 September 2026. Prices and models change often, so treat any figure more than a month old as a hint rather than a budget line. You can re-pull the current state with GET /api/v1/images/models for accepted parameters and GET /api/v1/images/models/{model}/endpoints for pricing with the…
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