Chế độ Thinking: GPT Image 2 lập kế hoạch trước khi vẽ

Lập kế hoạch, tìm kiếm, tự kiểm, nhất quán nhiều ảnh.

5/12/2026 · GPTImage Prompts #Thinking#GPT Image 2#Lý luận

Chế độ Thinking = lập kế hoạch → (tuỳ chọn) tìm kiếm → tự kiểm → render (tối đa 8 khung nhất quán).

1. Phác layout
2. Kiểm tra copy — chuỗi trong dấu ngoặc
3. Khóa series — cùng trang phục

API: mức thinking. Cho poster & storyboard, không phải Instant nhanh.

OpenAI positions GPT Image 2 as evolving from a drawing tool into a visual thought partner. The key narrative is Thinking mode: before pixels land, the model understands, plans, optionally retrieves information, and self-checks output.

Traditional vs Thinking generation

Reported wins: multi-page comics, whole-home design series, data infographics, multilingual social sets — tasks that need structure + cross-image consistency + lots of type.

Internal model (conceptual)

OpenAI has not published a full technical report; community reconstructions describe three phases:

1. Parse & plan

Split prompt into subject, background, text blocks, style, aspect; verify object counts; choose composition grid and whitespace.

2. Augment

Web Search (Plus / Pro / Business) for timely facts — maps, scores, product silhouettes (verify copyright and facts). Analyze uploaded references — menu photos, wireframes, brand PDFs.

3. Generate & verify

Render 1–8 images; internal checks on spelling, alignment, character consistency; iterate if needed (chain not visible to users).

User experience: longer wait, higher first-pass hit rate, fewer half-wrong headlines.

AI Chat product usage

• Instant: broader tiers including limited free quota (per current policy).
• Thinking: AI Chat Plus / Pro / Business with reasoning-capable image model.

Tips: toggle Thinking; write structured briefs; for series specify 6 coherent storyboard frames, same protagonist, manga line art; use regional edit instead of full re-roll.

Latency is often 30–60 seconds — plan async workflows, not live twitch demos.

API thinking parameter

Via gpt-image-2 (see OpenAI Image guide):

Batch n up to 10 on API; AI Chat may cap coherent series at 8 — check docs.

Billing: Thinking adds text + image token usage — log latency and cost per prompt.

Prompt patterns

A — Layout first

[Layout] Top 20% headline; middle 60% product; bottom 20% three icon columns.
[Text] Title "Winter Collection"; columns "Warm" "Light" "Dry".
[Style] Nordic minimal, off-white, soft shadow.
[Mode] Thinking — 4 variants, background color only differs.

B — Storyboard

4-panel manga, same girl (black ponytail, school uniform), rainy umbrella story,
short dialogue under each panel, clean lines, consistent character.

C — Data infographic (human must verify numbers)

2025 global renewable share infographic, pie + 3 callouts, title "Energy Mix 2025",
verify public data before drawing if web search available.

When NOT to use Thinking

Background textures, abstract art, material swatches, atmosphere-only references, or real-time demos — use Instant.

Failure modes

vs multimodal AI multimodal chat

Inside AI Chat, images bind to conversation context — upload a competitor screenshot and ask for your palette on that layout. Thinking fuses text + vision inputs. Stateless /images/generations alone cannot replicate that collaborative loop.

Conclusion

Thinking mode productizes language-model reasoning chains for images. It trades time and compute for complex briefs done once. Master when to enable, how to structure prompts, and how humans proof text and data — then “think before draw” becomes a repeatable team SOP.

Further reading: Deep review, vs Midjourney, Prompt tutorial.