OpenAI's GPT Image 2.5 family on PixBulk

GPT Image 2.5 Flare Image Generator

Move from a written brief to usable visuals quickly with OpenAI's speed-focused GPT Image 2.5 model. Generate new images, direct edits with references, and produce multiple variations without leaving your PixBulk workspace.

A colorful set of mid-century poster concepts created with GPT Image 2.5 Flare
Official GPT Image 2.5 family showcase imagery. Results vary by prompt, references, and settings.

GPT Image 2.5 Flare で作成

GPT Image 2.5 Flare が初期選択されています。ここでプロンプトと任意の参照画像から1枚を生成できます。Variations、CSV、Products が必要な場合は一括生成画面を開いてください。

画像生成ツールを読み込み中…

Model overview

What is GPT Image 2.5 Flare?

Flare is the fast, high-quality model in OpenAI's GPT Image 2.5 family, designed for everyday image generation and responsive creative iteration.

OpenAI positions Flare as the default choice for most applications. It improves image quality over GPT Image 2 while cutting latency by 50%, so it fits workflows where a team needs to explore, compare, and revise ideas without waiting on a premium final-render path.

That balance makes Flare useful for social graphics, ecommerce concepts, rapid product visualization, visual search experiences, and high-volume content production. In PixBulk, the same model works for one-off generations and batch jobs, while quality, canvas shape, background, output format, and reference images remain explicit controls.

開発元
OpenAI
モデル ID
gpt-image-2.5-flare
出力解像度
1K / 2K / 4K
参照画像
対応、最大 16 枚
1 枚あたりのクレジット
1–246
アスペクト比
1:1, 2:3, 3:2, 3:4, 4:3, 9:16, 16:9, 5:4, 4:5, 2:1, 1:2, 21:9, 9:21, 3:1, 1:3

Built for fast visual iteration

Flare is most valuable when speed must still leave room for art direction, readable text, repeatable subjects, and practical delivery formats.

Draft polished campaign concepts sooner

Translate a detailed creative brief into presentation-ready directions without beginning from a blank canvas. Specify the layout, palette, typography, objects, and visual era, then generate alternatives while the idea is still fresh.

Presentation slides with branded visual concepts generated by GPT Image 2.5 Flare

Create assets with embedded text

Ask for labels, headlines, signs, packaging copy, or sticker lettering as part of the composition. Clear wording, hierarchy, placement, and style instructions give the model a stronger target and make review easier.

A collection of illustrated stickers with text created by GPT Image 2.5 Flare

Explore distinct art directions

Move between editorial illustration, photography, painterly scenes, graphic posters, and stylized product visuals. Describe materials, light, lens or medium, color relationships, and composition instead of relying on a short style label alone.

An impressionist city scene generated by GPT Image 2.5 Flare

Scale one brief into many deliverables

Use Variations when one prompt needs several options, CSV when every row has its own prompt, or Products when reference-led product imagery is the job. The selected quality and image count are priced before submission.

A retrofuturist visual created with GPT Image 2.5 Flare

Direct edits with natural-language instructions

Upload one or more reference images and explain what should change and what must remain stable. Flare can support quick background, styling, composition, and object-level iterations while preserving the overall intent of the source.

Original portrait before a GPT Image 2.5 Flare edit
Before
Portrait after a background and lighting edit with GPT Image 2.5 Flare
After
Example edit instruction

Replace the simple background with a refined studio set, keep the subject identity and pose, preserve the wardrobe colors, and add soft editorial lighting with realistic shadows.

Where Flare fits best

Choose Flare when creative throughput and quick feedback matter more than spending the longest time on a single premium render.

A cyberpunk creator visual made with GPT Image 2.5 Flare

Social and creator content

Develop post concepts, thumbnails, promotional cards, event graphics, and visual series in the aspect ratio required by the publishing channel.

A vintage stamp product concept made with GPT Image 2.5 Flare

Product and ecommerce drafts

Test settings, styling, backgrounds, seasonal directions, and listing compositions before committing time to final production or a photo shoot.

A retro editorial portrait made with GPT Image 2.5 Flare

Editorial and brand exploration

Turn a campaign theme into several visual territories that stakeholders can compare, annotate, and refine into a consistent direction.

How to prompt GPT Image 2.5 Flare

A structured brief produces more controllable variations and makes each revision easier to diagnose.

Prompt formula

Subject + action + setting + composition + lighting + materials + color system + exact text + delivery format

Example prompt

Design a square launch poster for a compact wireless speaker on a warm terracotta plinth, three-quarter product view, soft late-afternoon window light, tactile paper texture, navy and coral palette, headline ‘SMALL SIZE. FULL SOUND.’ at the top, clean editorial spacing, premium but playful art direction.

1

Name the non-negotiables

State the subject, required text, product details, dominant colors, and placement constraints before adding stylistic language.

2

Describe relationships

Explain what sits in front, behind, beside, or inside another element so the model can reason about the composition.

3

Revise one variable at a time

Keep the stable parts of a successful prompt and change only the light, framing, copy, or material that needs improvement.

4

Match quality to the decision

Use a lower tier for exploration and raise quality when the direction is approved and detail becomes more important than iteration speed.

GPT Image 2.5 Flare 画像生成ツールの使い方

同じ 3 ステップで単体制作と一括制作に対応できます。

1

このモデルから始める

GPT Image 2.5 Flare は最初から選択済みです。プロンプトを入力し、必要に応じて対応する参照画像を追加できます。大きな作業には一括生成画面を利用してください。

2

指示と出力を設定

被写体、状況、制約を記述し、GPT Image 2.5 Flare が対応する比率と解像度を選びます。対応時のみ参照画像を追加します。

3

生成して確認

GPT Image 2.5 Flare の結果を比較し、必要な指示だけを調整します。完了した作業は履歴に保存されます。

GPT Image 2.5 controls in PixBulk

These are the settings currently exposed by the PixBulk integration. The generator remains the source of truth if provider capabilities change.

SettingAvailable choices
QualityLow, Medium, High, XHigh, Max
Canvas shapeSquare (1024 × 1024), portrait (1024 × 1536), landscape (1536 × 1024)
BackgroundAuto, opaque, transparent
Output formatPNG, JPEG, WebP
Images per request1–4 images
Reference imagesMultiple images, subject to the current model input limit

Flare vs. Sunburst: which model should you use?

Flare and Sunburst share the GPT Image 2.5 control surface, but they optimize different moments in a production workflow. Start with the decision you need to make, not a universal model ranking.

ModelBest forPrimary tradeoff
GPT Image 2.5 FlareRapid concepts, social assets, product drafts, high-volume creative iterationOptimized for speed rather than the family's highest precision
GPT Image 2.5 SunburstPrecise edits, premium product imagery, campaigns, demanding final assetsMore generation time for greater capability and control

Limits and production checklist

Use the model as part of a reviewed creative workflow. Quality settings improve rendering effort, but they do not replace a clear brief or human approval.

Text and small visual details still require review. Check every word, number, label, logo, hand, and repeated object before publishing.

Reference images guide the result but do not guarantee pixel-identical identity, layout, product geometry, or brand consistency across every run.

Generation is probabilistic. Save the prompt and settings that matter, then compare several outputs when consistency is important.

PixBulk exposes Low, Medium, High, XHigh, and Max. OpenAI also documents an Auto option, but it is intentionally not part of the current PixBulk selector.

GPT Image 2.5 Flare: よくある質問

利用方法、クレジット、入力、出力、一括生成、確認について説明します。

モデル情報の出典

機能情報は公式ベンダー資料と PixBulk 連携で使う API 文書に基づきます。

GPT Image 2.5 Flare で制作を始める

GPT Image 2.5 Flare が選択された生成ツールを開き、指示と形式を設定して、同じ画面で確認可能な結果を作成します。

このモデルで生成