Openai Gpt Image 2.5 Flare Text To Image API Documentation

Openai Gpt Image 2.5 Flare Text To Image API Documentation

Playground

OpenAI’s GPT Image 2.5 Flare Text-to-Image generates high-quality images from natural-language prompts, with five quality tiers up to 4K. Flare is the fast, balanced GPT Image 2.5 tier for everyday generation at low latency. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

OpenAI GPT Image 2.5 Flare Text-to-Image turns natural-language prompts into high-quality images. Flare is the fast, balanced GPT Image 2.5 tier for everyday generation at low latency.

• Need to edit an existing image instead? Try OpenAI GPT Image 2.5 Flare Edit.
• Need extra fidelity on intricate detail? Try OpenAI GPT Image 2.5 Sunburst Text-to-Image.

Why Choose This?

• Strong prompt fidelity Generate images that closely follow detailed natural-language instructions, including scene layout, visual style, and composition.

• Accurate text rendering Create images with clearer, more usable in-image text for posters, ads, packaging, and interface mockups.

• Five quality tiers Choose from low drafts to max fidelity and pay only for the detail you need.

• Up to 4K output and flexible aspect ratios Square, portrait, landscape, and panoramic outputs at 1k, 2k, or 4k.

• Production-ready API Access the model through a ready-to-use REST inference API for fast integration into applications and workflows.

Parameters

Quality Tiers

GPT Image 2.5 exposes five quality tiers. Pick the lowest tier that meets your need; every step up adds detail, latency, and cost.

How to Use

1. Write your prompt — describe the image in detail, including subject, style, lighting, composition, and any text you want to appear.
2. Pick quality and resolution (optional) — medium / 1k is the default; raise quality for more detail or resolution for larger output.
3. Choose aspect ratio (optional) — 1:1 for square, 2:3 or 9:16 for portrait, 3:2 or 16:9 for landscape, up to 3:1 / 1:3 panoramas.
4. Submit — run the model and download your generated image.

Example Prompt

A cinematic product photo of a luxury perfume bottle on a marble surface, soft golden-hour lighting, shallow depth of field, elegant reflections, premium editorial photography style

Pricing

Pricing varies by quality and resolution.

Best Use Cases

• Marketing creatives — Hero images, ad visuals, banners, and campaign assets from text prompts.
• E-commerce content — Product shots, lifestyle scenes, and promotional visuals without a studio workflow.
• Landing pages & web design — Branded illustrations, hero sections, and visual mockups.
• Social media content — Eye-catching visuals tailored to different content formats.
• Typography-driven visuals — Posters, ads, and packaging concepts that need readable in-image text.

Pro Tips

• Be specific in your prompt — include subject, environment, camera angle, lighting, mood, and style.
• For text inside the image, put the exact wording in quotes.
• Start at medium quality and 1k; move up a tier only if a specific detail is missing.
• Test multiple aspect ratios when creating assets for different placements.

Notes

• prompt is the only required field.
• Requests containing sexual content are rejected before generation and are not charged.

Related Models

• OpenAI GPT Image 2.5 Flare Edit — Edit one or more reference images using natural-language instructions.
• OpenAI GPT Image 2.5 Sunburst Text-to-Image — The Sunburst tier of the same model: extra fidelity on intricate detail, at longer generation times.
• OpenAI GPT Image 2 Text-to-Image — The previous generation.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "prompt": "A cinematic ocean wave at sunrise, highly detailed",
  "aspect_ratio": "1:1",
  "resolution": "1k",
  "quality": "medium",
  "output_format": "png"
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/openai/gpt-image-2.5-flare/text-to-image" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}")
  RESULT=$(printf '%s' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout|deleted) printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    *) sleep 2 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

Response Parameters

Result Request Parameters

Result Response Parameters