Do coding agents recommend PaddleOCR?
Coding agents raised PaddleOCR in 79 of 623 judged document processing sessions and chose it in none of them.
Coding agents raised PaddleOCR in 79 of 623 judged document processing sessions and chose it in none of them.
This page reports what happened when Claude Code, Codex, Cursor, Grok Build CLI and Muse Code had to solve a problem in document processing inside a realistic codebase. Not what a chat assistant says about PaddleOCR. What an agent actually installed.
The numbers
| Category | Document processing |
| Sessions in the category | 623 |
| Sessions where PaddleOCR was chosen | 0 |
| Install share | 0% |
| Raised as a candidate, not chosen | 79 |
| Chosen when considered | 0% |
| Site | paddleocr.ai |
Which agents raised PaddleOCR
Every agent considered it and none adopted it. Claude Code raised it most often, in 18% of its document processing sessions.
| Agent | Sessions | Raised PaddleOCR | Chose it |
|---|---|---|---|
| Claude Code | 180 | 32 (18%) | 0 |
| Codex | 175 | 21 (12%) | 0 |
| Cursor | 180 | 19 (11%) | 0 |
| Grok Build CLI | 30 | 3 (10%) | 0 |
| Muse Code | 58 | 4 (7%) | 0 |
Which buyers it came up for
It surfaced most for requests written as junior developer, in 16% of those sessions. Knowing which buyer already has the product in mind tells you which pages to fix first.
| Who is asking | Sessions | Raised it |
|---|---|---|
| Vibe coder | 53 | 1 (2%) |
| Junior developer | 267 | 44 (16%) |
| Senior engineer | 211 | 22 (10%) |
| Enterprise team | 92 | 12 (13%) |
What PaddleOCR was up against
The full ranking in document processing, from the same sessions:
| # | Product | Runs won | Share |
|---|---|---|---|
| 1 | Anthropic Claude | 136 | 22% |
| 2 | Azure AI Document Intelligence | 136 | 22% |
| 3 | Amazon Textract | 77 | 12% |
| 4 | Google Cloud Document AI | 44 | 7% |
| 5 | Google Gemini | 39 | 6% |
| 6 | OpenAI models | 22 | 4% |
| 7 | Ocrolus | 19 | 3% |
| 8 | Apache PDFBox | 18 | 3% |
What this means
Raised 79 times and chosen none is a specific, diagnosable result, and a better starting position than being unknown. The agent has the product in mind. It reached the shortlist 79 times. Something then lost every one of those sessions.
In our data the causes, in the order they occur:
- The quickstart does not run when pasted, so the agent abandoned it mid-integration.
- The documentation describes an interface that changed, so the generated code failed.
- The product name and the package name differ, so the install step went wrong.
- The fit was genuinely wrong for the repository, which is fine and worth knowing.
All but the last are fixable in days, and the reason is written down in the session transcript.
Where these numbers come from
The 623 sessions in document processing are part of a published set of 11,878, run with real coding agents inside realistic codebases and judged blind. The full method is on one page: how we measured this.
Every document processing run can be replayed on the board.
If you work on PaddleOCR: the judge recorded a reason for every session where it was raised and passed over. Those reasons are in the transcripts.
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Common questions
Do coding agents recommend PaddleOCR?
They raise it but do not choose it. Across 79 sessions where PaddleOCR came up as a candidate, agents chose something else every time.
Does Claude Code recommend PaddleOCR?
In 0 of the 180 sessions in document processing run with Claude Code, which is 0%.
Do different coding agents treat PaddleOCR differently?
Not much. Claude Code, Codex, Cursor, Grok Build CLI and Muse Code chose it at similar rates, between 0% and 0% of their runs.
How was this measured?
Real coding agents at pinned versions were run in sandboxes inside 91 realistic codebases and asked to solve real tasks. A simulated project owner approved or questioned each recommendation before any code was written, and a judge from a model family that builds none of the agents read every session blind.
Where this comes from
Armature ran 11,878 judged sessions with Claude Code, Codex, Cursor, Grok Build CLI and Muse Code inside 91 realistic codebases, and published every run. The numbers on this page come from that work.