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Upload a document and turn it into clean, token-efficient Markdown for ChatGPT, Claude, Gemini, Cursor and RAG workflows.
Which is more popular?
Website, pricing, platforms and company facts side by side.
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| Website | javascripting.com | tokenpig.co |
| Pricing | — | |
| Platforms | — | |
| Listed in |
In their own words, as submitted to SaaSHub.


No description of JavaScripting yet.
TokenPig converts documents into clean, structured Markdown built specifically for LLM and RAG workflows — ChatGPT, Claude, Gemini, and retrieval pipelines. The problem Raw PDF, Word, PowerPoint and Excel exports carry a lot of formatting noise — repeated headers, broken tables, inconsistent...
What each product offers, as listed by its team.


Possible disadvantages
How often each product is chosen within a category, 0–100% relative to the other.


As answered by people managing JavaScripting and TokenPig.
TokenPig's answer:
TokenPig started from a recurring frustration: pasting PDF or Word exports into an LLM and watching layout noise — repeated headers, broken tables, stray whitespace — burn through the context window before the actual content even got read. TokenPig was built to solve that specific problem: clean, structured Markdown output plus visibility into the tokens saved.
TokenPig's answer:
Two main groups: individuals who regularly feed documents into ChatGPT or Claude and want cleaner, cheaper context (researchers, consultants, students), and developers/teams building RAG pipelines who need reliable document-to-Markdown conversion via API.
TokenPig's answer:
TokenPig focuses specifically on token efficiency, not just format conversion. Alongside clean Markdown output, it shows an estimated token savings for every conversion, so users can see exactly how much context window they're recovering before pasting a document into ChatGPT, Claude or Gemini — something general-purpose converters don't surface.
TokenPig's answer:
TokenPig runs entirely in the browser — no Python setup, no libraries to install, no code to maintain. That makes it accessible to non-developers (consultants, researchers, students) while still offering batch processing and an API for teams that want to automate document ingestion at scale.
TokenPig's answer:
TokenPig's answer:
Built as a modern web application using Next.js and TypeScript, with a focus on fast, reliable document processing entirely server-side — no client installation required.
Share your experience with using JavaScripting and TokenPig. For example, how are they different and which one is better?
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