GoKawiilDeveloper publishes practical guide and benchmarks for AI image generation APIs
An independent developer has published a guide to AI image generation based on hands-on experiments calling several image-generation providers from Python, including ChatGPT and API-based models. The guide covers how diffusion models work, pricing differences across providers, and practical advice on prompting and building image generation into code and agents.
GoKawiil's interpretation of the reporting above, not reported fact.
The guide suggests that choosing an image-generation provider involves trade-offs beyond headline price, since cost can vary more than a hundredfold without a consistent link to output quality. It also flags that image APIs change quickly, with the author noting that providers retire models with little notice or silently ignore parameters, which could affect anyone building production systems on top of them.
- Most image models denoise random noise guided by text prompts, and newer ones plan images similarly to how chat models plan sentences.
- Per-image pricing reportedly ranges from a fraction of a cent to 15 cents or more, with running models locally costing nothing.
- The author advises logging actual API outputs and keeping model choice in config, since providers can retire models or ignore parameters with little warning.
Source: danielball.com, 2026-10-20
Published there as: “A practical guide to AI image generation, from diffusion to agents”
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