GPT Image 1 — OpenAIのAI画像生成ツール

GPT Image 1は精度の高いテキストレンダリングでフォトリアルな画像を生成。編集、スタイル転写も対応。透かしなし。

GPT Image 1とは何ですか?

GPT Image 1はOpenAIのGPT-4oマルチモーダルアーキテクチャ上に構築されており、GPT-4がテキストを考えるのと同じ方法で画像を「考え」ます。これは言語モデルに取り付けられた独立した画像モデルではなく、同一のニューラルネットワーク内で視覚的な理解と生成が行われる統合システムです。このアーキテクチャ上の決定が、GPT Image 1が競合する画像モデルよりも複雑な指示に忠実に従える理由です。

Corporate headshot — professional portrait with natural skin texture
Corporate headshot — professional portrait with natural skin texture

テキストレンダリングの突破口こそ、GPT Image 1の存在意義となる機能です。これまでのモデル — DALL-E 3、Midjourney、Stable Diffusion、Flux — はすべて画像内に読めるテキストを配置することに苦労していました。GPT Image 1は正確なスペルと適切なフォーマットのテキストを安定して生成でき、これまでAI画像生成では不可能だったユースケースを切り開きます。

Interior design — modern luxury living room visualization
Interior design — modern luxury living room visualization

GPT Image 1 — テキストレンダリング、編集、DALL-Eにできなかったこと

解像度
最大 1024×1024
テキストレンダリング
クラス最高
画像編集
あり(アップロード+編集)
スタイル転写
あり
出力フォーマット
PNG, JPEG, WebP
生成速度
約10〜20秒

OpenAI画像生成の料金詳細

20 credits per image

At 20 credits per image (~$0.20), GPT Image 1 is the mid-range option among image models. It's 4x the cost of Seedream (5 credits) and 2x Flux (10 credits), but the text rendering and instruction-following capabilities justify the premium for marketing and design work. Compared to a ChatGPT Plus subscription ($20/month with limited image generations), pay-per-image is more cost-effective for most users.

AI画像における文字組み — GPT Imageが突出する理由

When it shines

GPT Image 1 is the undisputed best choice when your image needs readable text — posters, marketing banners, memes, infographics, social media quote cards, product packaging mockups. No other AI image model renders text this accurately and consistently. It's also the strongest at following complex, multi-part instructions ('put X in the top-left, Y in the center, with Z as background'). For image editing workflows — changing backgrounds, removing objects, adding elements — GPT Image handles natural language editing commands better than alternatives.

When to pick a different model

If you need speed above all, Flux Pro (~5 seconds) is 2–4x faster. If you need resolution above 1024px for print or large displays, Flux supports up to 2048px. For character consistency across a series of images (same person in different scenes), Flux Kontext is purpose-built for that. For portraits and Asian aesthetic content at the lowest cost, Seedream at 5 credits/image is 4x cheaper. And GPT Image's artistic aesthetic, while good, doesn't match the distinctive visual quality that Flux is known for.

Limitations worth knowing

  • Slower than Flux (10–20 seconds). GPT Image takes 10–20 seconds per image, while Flux Pro generates in ~5 seconds. For rapid-fire ideation where speed matters most, Flux is the faster choice.
  • 1024px maximum resolution. Output caps at 1024x1024 pixels — fine for social media and web use, but not ideal for print or large-format displays. Flux supports up to 2048px if you need higher resolution.
  • No character consistency. GPT Image doesn't maintain the same character appearance across multiple generations. For creating consistent brand mascots or character series, Flux Kontext's character consistency feature is a better fit.

GPT Image vs Flux vs Seedream — 徹底比較

指標gpt-imagefluxseedream
Text RenderingMulti-line, styledSingle-lineUnreliable
Image EditingYes (upload + instruct)Yes (Kontext)No
Photorealism1024×1024Up to 2048px1024×1024
Speed10-20s5-10s5-15s
Cost per image5 credits3-5 credits3 credits
Style TransferYes (upload ref)Yes (Kontext ref)No
Max Resolution1024×10242048×20481024×1024

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いつでも読みやすいテキスト — GPT Imageプロンプトガイド

1

Include Text Directly

When you want text in the image, write it exactly as you want it to appear. GPT Image renders text literally — use quotes for emphasis.

A minimalist poster with large bold text saying 'THINK DIFFERENT' in white on a black background, Apple-style typography
2

Be Specific About Layout

GPT Image understands spatial instructions. Describe where elements should be: 'text at the top', 'product centered', 'logo in bottom right corner'.

3

Use Image Editing for Refinement

Generate a base image first, then upload it back and describe specific changes. This iterative approach gives much better results than trying to get everything perfect in one prompt.

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