/higgsfield-gpt-image-2

name: higgsfield-gpt-image-2
description: "Use when the user mentions GPT Image 2.0 or GPT Image 2.5, gpt-image-2, gpt-image-2.5, gpt_image_2_5, GPT-Image-2 prompts, the Flare / Sunburst variants, a transparent-background image, or wants to generate an image with GPT Image 2.0 or 2.5. Covers when to prefer GPT Image 2.5 over 2.0 (transparent background, xhigh/max quality, extra aspect ratios — from platform and OpenAI facts), the CLI-vs-MCP disagreement on gpt_image_2, the three-format prompt taxonomy (Format A structured JSON for UI mockups and layout-dense images; Format B dense cinematic prose for single-subject scenes; Format C auto-derive meta-prompt for theme-only concepts), per-format craft patterns, output conventions, the 6-item pre-delivery checklist, and cross-surface workflow context (companion static-ads-workflow.md for ad recreation; higgsfield-marketing-studio cross-surface-workflow.md §3 for ms_image / DTC Ads Higgsfield-native alternative)."
user-invocable: true
metadata:
  tags: [higgsfield, gpt-image-2, gpt-image-2.5, flare, sunburst, transparent-background, prompt-director, image, json, prose, meta-prompt, layout, mockup, infographic, character-sheet, ui-mockup, landing-page, static-ads, cross-surface]
  version: 1.3.1
  updated: 2026-09-26
  parent: higgsfield

Higgsfield GPT Image 2.0

<!-- CONTENTS — generated by scripts/build_index.py; do not hand-edit --> **Contents**

• [GPT Image 2.5](#gpt-image-25)
• [Surface](#surface)
• [When to prefer 2.5 vs 2.0](#when-to-prefer-25-vs-20)
• [CLI ↔ MCP disagreement on `gpt_image_2`](#cli--mcp-disagreement-on-gpt_image_2)
• [Prompting status on 2.5](#prompting-status-on-25)
• [1. What GPT Image 2.0 is](#1-what-gpt-image-20-is)
• [2. Three prompt formats](#2-three-prompt-formats)
• [Tie-break](#tie-break)
• [3. Format A — Structured JSON](#3-format-a--structured-json)
• [Core fields to reach for](#core-fields-to-reach-for)
• [Key patterns that make JSON prompts work](#key-patterns-that-make-json-prompts-work)
• [Worked example — minimal shape](#worked-example--minimal-shape)
• [4. Format B — Dense cinematic prose](#4-format-b--dense-cinematic-prose)
• [What makes prose prompts work](#what-makes-prose-prompts-work)
• [Worked example](#worked-example)
• [5. Format C — Auto-derive meta-prompt](#5-format-c--auto-derive-meta-prompt)
• [Structure](#structure)
• [When to use](#when-to-use)
• [6. Routing decision](#6-routing-decision)
• [7. Output format conventions](#7-output-format-conventions)
• [8. Pre-delivery checklist](#8-pre-delivery-checklist)
• [9. Example routings](#9-example-routings)
• [10. Cross-surface workflow context](#10-cross-surface-workflow-context)
• [11. Source acknowledgment](#11-source-acknowledgment)
• [Per-claim translation calibration](#per-claim-translation-calibration)
• [Verification trail](#verification-trail)

<!-- /CONTENTS -->

A prompt director for GPT Image 2.0. Converts plain-text concepts into production-ready prompts that route by output type: structured JSON for layout-dense images (UI mockups, infographics, character sheets, multi-panel posters), dense cinematic prose for single-subject scenes (portraits, photographs, landscapes), or auto-derive meta-prompts for theme-only concepts where the model self-generates the composition.

Translated from Adil Aliyev's `gpt-image-2-director` source corpus per the v3.7.13 / v3.7.15 translation precedent. Two companion satellites extend this sub-skill: `static-ads-workflow.md` covers the ad-recreation workflow that uses GPT Image 2.0 as its generation engine, and `reference-sheet-workflow.md` covers the Automatic Product Reference Sheet + Automatic Prompt Creator workflow (one product image → a multi-view identity-locked reference sheet for high-consistency generation).

Sections 1–11 were written for **GPT Image 2.0**. GPT Image 2.5 — its surface, when to prefer it, and what is and is not documented about prompting it — is the next section.

---

GPT Image 2.5

New on Higgsfield in the 2026-09-26 catalog as `gpt_image_2_5` — **not yet field-rated**. Facts below are `[OFFICIAL — platform, snapshot 2026-09-26]` (MCP and CLI agree on all of them) or `[OFFICIAL — OpenAI docs]` from the [image generation guide](https://platform.openai.com/docs/guides/image-generation) and the [Sunburst](https://platform.openai.com/docs/models/gpt-image-2.5-sunburst) / [Flare](https://platform.openai.com/docs/models/gpt-image-2.5-flare) model pages (fetched 2026-09-26).

Surface

| Param | Values (default first) | |---|---| | `variant` | `flare` · `sunburst` | | `quality` | `low` · `medium` · `high` · **`xhigh`** · **`max`** | | `resolution` | `1k` · `2k` · `4k` | | `background` | omit (model default) · `auto` · `opaque` · **`transparent`** | | aspect ratio | auto, 1:1, 3:2, 2:3, 4:3, 3:4, 16:9, 9:16, 21:9, 27:16, 16:27, 9:8, 8:9, 4:5, 5:4 | | media | `image_references` (generation and editing) |

A bare call is `flare` / `low` / `1k` — draft settings. OpenAI: "Use `quality: "low"` for quick drafts. For final assets, compare higher quality settings to find the right balance of detail, latency, and cost." State `quality` and `resolution` explicitly in every delivered settings header.

When to prefer 2.5 vs 2.0

Only platform and vendor facts — neither model has been field-rated against the other here, so no quality ranking is claimed.

**Prefer GPT Image 2.5 when:**

• **The asset needs a transparent background** — `background: transparent`. It is the first

transparent-background route in the MCP catalog, and the only one both Higgsfield surfaces agree on. OpenAI's API needs PNG or WebP output for transparency; Higgsfield exposes no output-format parameter, so confirm the downloaded file actually carries alpha before building on it.

• **A quality tier above `high` is wanted** — `xhigh` / `max`. OpenAI: earlier GPT Image models

"support quality settings up to `high`".

• **The frame needs 27:16, 16:27, 9:8, or 8:9** — 2.5