The BioRender Alternative That Generates Figures From Text

PaperFig uses AI to turn your research descriptions into publication-ready scientific figures. No icon hunting. No manual layout. Describe your mechanism and get a structured figure in seconds.

Try free — no credit card See pricing

Why researchers switch from BioRender

BioRender is a great tool for cell biology illustrations using its drag-and-drop icon library. But if your research spans materials science, environmental engineering, geoscience, or computational workflows — or if you simply need figures faster — its icon-dependent approach becomes a bottleneck. PaperFig takes a different approach: you describe the mechanism, pathway, or pipeline in natural language, and the AI generates a structured figure with labeled components, directional arrows, and logical spatial organization. No icons to hunt. No manual arrangement. Works in any language.

PaperFig vs BioRender: Side-by-Side

Feature	PaperFig	BioRender
Approach	Text-to-figure (AI generation)	Drag-and-drop icon library
Time to first draft	~30 seconds	15–30 minutes
Learning curve	Write a description	Learn icon library and canvas tools
Field coverage	All scientific fields	Primarily biology and life sciences
Label editing	Click to edit any label	Click to edit
Export formats	PNG, 4K PNG, SVG, PPTX	PNG, SVG (paid plans only)
Input language	Any language	English only
Starting price	Free (150 signup credits included)	$39/month (academic)

Where PaperFig fits better than BioRender

BioRender's icon library covers cell biology and immunology well. For everything else, PaperFig's AI-driven approach delivers results that icon libraries cannot:

Materials science — heterojunctions, solar cells, electrode stacks, biosensors
Environmental science — multi-scale processes from molecular to ecosystem level
CS & AI — deep learning architectures, computational pipelines, system workflows
Non-English research — describe mechanisms in Chinese, Spanish, or any language
Tight deadlines — generate five figures for a revision in under an hour

Frequently Asked Questions

Can I use PaperFig figures in journal submissions?: Yes. PaperFig exports SVG (vector, editable) and high-resolution PNG. Check your target journal's figure guidelines for specific format and DPI requirements. We also have journal-specific requirement pages for Nature, Science, Cell, and more.
Can I import my existing BioRender figures into PaperFig?: PaperFig generates figures from text descriptions rather than importing existing files. If you have a BioRender figure you want to recreate, describe the mechanism in your own words and PaperFig will generate a new version. You can also upload a photo or sketch as a reference.
Is PaperFig free?: PaperFig offers 150 signup credits — no credit card required. Each figure generation uses credits. For regular use, credit packages and subscription plans are available.
Can I edit the generated figures?: Yes. You can click to edit any label directly in PaperFig. For more advanced editing, export as SVG and open in Adobe Illustrator, Inkscape, or any vector editor — all text and shapes remain fully editable.
Which research fields does PaperFig support?: PaperFig works across all scientific fields: biomedicine, materials science, environmental science, energy, geoscience, computational biology, and CS/AI. The AI understands domain-specific terminology and figure conventions for each field.

Ready to try a different approach?

Describe your mechanism. Get a journal-ready figure. Edit every label.

Try free — no credit card

Why researchers switch from BioRender

PaperFig vs BioRender: Side-by-Side

Feature	PaperFig	BioRender
Approach	Text-to-figure (AI generation)	Drag-and-drop icon library
Time to first draft	~30 seconds	15–30 minutes
Learning curve	Write a description	Learn icon library and canvas tools
Field coverage	All scientific fields	Primarily biology and life sciences
Label editing	Click to edit any label	Click to edit
Export formats	PNG, 4K PNG, SVG, PPTX	PNG, SVG (paid plans only)
Input language	Any language	English only
Starting price	Free (150 signup credits included)	$39/month (academic)

Where PaperFig fits better than BioRender

BioRender's icon library covers cell biology and immunology well. For everything else, PaperFig's AI-driven approach delivers results that icon libraries cannot:

Materials science — heterojunctions, solar cells, electrode stacks, biosensors

Environmental science — multi-scale processes from molecular to ecosystem level

CS & AI — deep learning architectures, computational pipelines, system workflows

Non-English research — describe mechanisms in Chinese, Spanish, or any language

Tight deadlines — generate five figures for a revision in under an hour

Frequently Asked Questions

Can I use PaperFig figures in journal submissions?

Yes. PaperFig exports SVG (vector, editable) and high-resolution PNG. Check your target journal's figure guidelines for specific format and DPI requirements. We also have journal-specific requirement pages for Nature, Science, Cell, and more.

Can I import my existing BioRender figures into PaperFig?

PaperFig generates figures from text descriptions rather than importing existing files. If you have a BioRender figure you want to recreate, describe the mechanism in your own words and PaperFig will generate a new version. You can also upload a photo or sketch as a reference.

Is PaperFig free?

PaperFig offers 150 signup credits — no credit card required. Each figure generation uses credits. For regular use, credit packages and subscription plans are available.

Can I edit the generated figures?

Yes. You can click to edit any label directly in PaperFig. For more advanced editing, export as SVG and open in Adobe Illustrator, Inkscape, or any vector editor — all text and shapes remain fully editable.

Which research fields does PaperFig support?

PaperFig works across all scientific fields: biomedicine, materials science, environmental science, energy, geoscience, computational biology, and CS/AI. The AI understands domain-specific terminology and figure conventions for each field.