compare
Frenchie vs LlamaParse.
LlamaParse fits the LlamaIndex stack. Frenchie fits the MCP ecosystem.
LlamaParse is a commercial PDF parsing API built around LlamaIndex workflows — if you're already invested in that framework, it slots in tight. Frenchie is built around MCP — native in Claude Code, Cursor, Codex, and every other MCP client. Both are managed services with pay-as-you-go pricing. The choice comes down to where your agent lives.
side by side
The shape of each tool.
| Dimension | LlamaParse | Frenchie |
|---|---|---|
| Integration surface | REST API, native in LlamaIndex loaders | MCP tool — appears as a callable function inside any MCP client |
| Ecosystem fit | LlamaIndex pipelines and retrievers | MCP-compatible agents (Claude Code, Cursor, Codex, etc.) |
| Pricing model | Tiered per-page (accuracy tier affects price) | Flat $1 = 100 credits, 1 credit per page |
| Output | Markdown, JSON, with optional structured extraction prompts | Clean Markdown + figures, metadata response for stdio |
pick LlamaParse
When LlamaParse is the right call
- You're building on LlamaIndex and want parsing, indexing, and retrieval in one ecosystem.
- You need the tuneable accuracy tiers LlamaParse exposes for specific document types.
- Your extraction uses structured-output prompts that fit LlamaParse's pipeline idioms.
pick frenchie
When Frenchie is the right call
- Your agent runs in Claude Code, Cursor, or any MCP client and you want OCR as a native tool call.
- You prefer flat pricing you can reason about per job — no accuracy tier math.
- You want the extraction step separated from the framework — no LlamaIndex lock-in.
together
Can you use both?
If you're building a product with a complex RAG backend, LlamaParse can do the heavy document parsing for your warehouse while Frenchie handles the agent-facing tool calls. Different layers of the same stack — document pipeline for offline, agent tool for online.
questions
The ones that come up.
Is Frenchie cheaper than LlamaParse?
Frenchie is flat: $0.01 per OCR page. LlamaParse varies by accuracy tier. At base tier, pricing is in the same ballpark. At higher tiers, LlamaParse is more expensive per page but offers extraction features Frenchie doesn't.
Can I use Frenchie inside a LlamaIndex app?
Yes — call Frenchie via its MCP tool or HTTP endpoint, feed the Markdown into your existing LlamaIndex indexer. No custom loader required.
Does Frenchie do structured extraction like LlamaParse?
No. Frenchie returns clean Markdown. Structured extraction — pulling specific fields, applying schemas — is something your agent handles after reading the Markdown, using the LLM it already has.
Why not just use LlamaParse?
Use it if your stack is LlamaIndex and you want parsing + retrieval coupled. Use Frenchie if your stack is MCP and you want parsing as a standalone tool call.
See if Frenchie fits.
100 free credits on signup. No card. Try it against a real PDF or scan from your workflow — compare the Markdown side by side.