Free GPT Image 2 for Scientific Figures

Text-aware by design. GPT Image 2 is the model to reach for when a scientific figure has to explain, not just impress.

• Explanation-First Figures: It is especially useful for graphical abstracts, annotated concept figures, pathway diagrams, and any visual where labels, captions, and structured callouts carry a large share of the meaning.
• Recommended Default for Journal Work: GPT Image 2 is the recommended default in SciFig for peer-reviewed journal submission and notation-heavy figures. It tends to follow the scientific brief more tightly — every label, formula, and structural cue from the prompt has a higher chance of landing in the figure.
• Still a Full Figure Model: This is not a text-only model. It still handles full scientific figure generation and editing across prompt-led and image-led workflows.

Yes. One model, two real generation modes. GPT Image 2 covers both prompt-first generation and image-conditioned regeneration inside SciFig.

• Text to Image: In `Text-to-Figure`, SciFig can use GPT Image 2 to generate a figure directly from your scientific prompt.
• Image to Image: In `Sketch-to-Figure`, `Reference-to-Figure`, `Photo-to-Figure`, and `Figure Enhancer`, the same model can work from an uploaded image plus your prompt to regenerate, restage, or refine the figure.
• PDF Is Different: `PDF-to-Figure` remains a SciFig workflow where the paper is first interpreted into a prompt. GPT Image 2 then participates in the follow-up figure generation step, rather than reading the PDF natively itself.

Choose GPT Image 2 when the figure's logic has to stay readable under pressure.

• Labels Lead the Task: If the figure depends on annotations, panel text, callouts, arrows, and information hierarchy, GPT Image 2 is often the cleaner choice.
• Edits Matter More Than Aura: When you expect to revise the figure several times, preserve structure from an uploaded input, or make controlled revisions instead of aesthetic leaps, GPT Image 2 is usually the safer route.
• Different Defaults, Different Outputs: Nano Banana Pro is the editorial-style specialist when the figure heads to a slide deck, conference poster, or social-media thumbnail. GPT Image 2 stays the recommended default when the figure heads to a peer-reviewed journal — chemistry, math, dense labels, scale bars.

Usually, yes. This is where GPT Image 2 becomes most distinctive inside SciFig.

• Graphical Abstract Friendly: Graphical abstracts often combine many small labels, directional flow, and compressed scientific storytelling. GPT Image 2 is a strong fit for that kind of visual density.
• Annotation-Heavy Figures: It is also well suited for explanatory concept figures, educational diagrams, and multi-step mechanism visuals where embedded text is part of the figure, not an afterthought.
• Multilingual Edge: Because text inside the image matters more here, GPT Image 2 is especially relevant when the figure may need multilingual labels or a mix of scientific notation and prose.

Yes. This is one of the clearest reasons to pick it.

• Controlled Regeneration: When you already have a sketch, a rough figure, a reference image, or a lab photo, GPT Image 2 is often useful because it can reinterpret the image while still respecting your new instructions.
• Better for Keep This, Change That Tasks: Users often do not want a complete reset. They want to keep the scientific structure, preserve the rough layout, or maintain the original figure idea while tightening execution. That is a strong fit here.
• Fits Revision Work: If your figure is already halfway there and the problem is refinement, not invention, GPT Image 2 becomes more compelling.

It fits image-guided workflows better than most researchers expect.

• Sketch-to-Figure: It is useful when the hand-drawn structure matters and you want the AI to respect the logic of your sketch instead of free-styling too far away from it.
• Reference-to-Figure: It is also a strong option when you want to borrow organization, composition, or explanatory rhythm from a reference image without just making a prettier version of the same thing.
• Why It Matters: These are the workflows where prompt-following and image-to-image stability matter more than dramatic visual polish on the first try.

This is one of its real edges, not just a marketing talking point.

• Text Inside the Image: GPT Image 2 is particularly relevant when the text is part of the visual composition itself: labels, captions, panel headers, arrows, legends, and callout boxes.
• Multilingual Use Cases: It is also useful when figures need to survive bilingual or multilingual contexts, such as teaching slides, global teams, or region-specific presentation decks.
• Every Label Stays Editable: If a protein name or compartment label needs a change, fix it in seconds with Text Edit or in SciFig Vector Canvas.

Not every figure problem is a notation problem.

• Slide and Poster Work: If the figure is heading to a slide deck, conference poster, or social-media thumbnail — where readability outweighs notation density — Nano Banana Pro is usually the better first pick.
• BioRender-Style Simplification: For BioRender-style mechanism cartoons, intro-level pathway figures, or layered architecture diagrams aimed at non-specialist audiences, Nano Banana Pro's editorial polish is the more natural fit.
• Use the Model for Its Edge: GPT Image 2 stays the recommended default for journal submission and notation-heavy work — chemistry, math, dense labels, scale bars. When that is not the task, the editorial-style specialist often wins.

Every element stays editable after generation.

• Fix Labels Fast: Use Text Edit to correct a name, unit or symbol in seconds, or ask for a conversational revision when the change is structural.
• Redraw One Region: Region Redraw regenerates just the area you select, so the rest of the figure stays as it is.
• Go Fully Vector: Vectorize the figure in SciFig Vector Canvas to click any label, arrow or shape and edit it directly, then export SVG, PPTX or high-resolution PNG for your paper, poster, or talk.