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Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions.
Passed 16 of 20 test cases with the skill, and 4 of 20 without it.
and 20 more in the full report
14 fixed4 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
A newer attempt on gemini-3.8-flash on 10/19/2026 did not produce a publishable comparison (not comparable). The snapshot above is the previously measured run.
One of 14 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am generating a product call-to-action banner using tools/ideogram4.py with a JSON prompt saved at cta_prompt.json. We need to inject our company's registered brand hex colors using the built-in preset option for 'digital-samba'. What CLI flags should be passed?
| Checked | Without skill | With skill |
|---|---|---|
| The command invocation uses the --brand flag. | Missed | Passed |
| The command passes the brand preset value provided in the request. | Passed | Passed |
The instructions your model reads when the skill loads.
Text-to-image generation with Ideogram 4 (9.3B, open-weight, released June 2026). Its superpower is best-in-class in-image text rendering — it beats much larger models (FLUX.2 dev 32B, Qwen-Image 20B, Hunyuan 80B) at rendering legible signage, logos, captions, and multi-line text — plus exact color-palette and bounding-box control.
That advantage is locked behind a structured JSON caption format. A plain-text prompt gets you FLUX-level results and misses the entire point of using this model. This skill teaches Claude to act as the "magic prompt" expander — turning a user's casual request into the JSON caption Ideogram 4 was trained on.
Backend: The toolkit uses Ideogram's hosted v4 API (not self-hosted weights). The API accepts a structured
json_prompt, so everything this skill teaches applies directly — Claude builds the caption, the tool posts it asjson_prompt. Paid API plans include a commercial license, which the self-hostable weights (non-commercial) do not — that's why we use the API. Cost is ~$0.03/image (turbo) to ~$0.10/image (quality).
Reach for Ideogram 4 (over FLUX.2) when the image needs:
Use FLUX.2 instead when: the image has no critical text, you need commercial-licensed output,
or you just want a fast atmospheric background. FLUX takes plain natural-language prompts; Ideogram
wants JSON. See tools/flux2.py.
Always emit a structured JSON caption, not a plain sentence. The model is trained exclusively on JSON captions that name every element explicitly. Claude is a better expander than Ideogram's free hosted magic-prompt (their own docs note the shipped one "is not the same used in production"), so build the caption yourself using this skill rather than passing raw text.
Minimal valid caption:
test cases passed, vs 4 / 20 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.