laya vs unlimited-ocr
Two of the top projects in Open models, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
laya
Single-pass multilingual classifier for typed choice, score and yes/no decisions
unlimited-ocr
OCR model that parses long documents and multi-page PDFs in one pass
Verdict
laya ranks higher, 76 to 55. Of the 3 score parts we measure for both, laya leads on 2, unlimited-ocr on none and 1 is even.
- Adoptionlaya leads by 25 points, 53 to 28: a wide gap.
- FreshnessEven at 100.
- MaintenanceWe do not score unlimited-ocr on this; laya has 91.
- Agent-readylaya leads by 30 points, 30 to 0: a wide gap.
Pick laya if you are
Engineers needing fast multilingual triage, routing and moderation classifiers
- One forward pass: 33 ms per question, 7.2 ms/question batched, measured on a T4
- No generation, so no output parsing; answers are typed choice, score or probability
Pick unlimited-ocr if you are
Engineers self-hosting OCR for scanned documents and PDFs on NVIDIA GPUs
- Multi-page parsing in one request, with 32768 max context length in the examples
- Three inference paths: Transformers, vLLM (Docker images), and SGLang with OpenAI-compatible API
| What we compare | laya | unlimited-ocr |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 53, popular | 28, niche |
| Freshness | 100, active | 100, active |
| Maintenance | 91, healthy | Not scored for this kind |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 30, minimal | 0, none |
| Facts from GitHub and the README | ||
| Stars | 32.9k | 27.3k |
| License | Apache-2.0 (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | None published |
| Language | Python | Python |
| Docker | Yes | No |
| GPU | Optional | Required |
| arm64 or Apple Silicon | Mentioned | Not stated |
laya
Laya answers typed questions (choice, score, noul) about a text, email, ticket or JSON state in one forward pass of an encoder model, with no text generation. A Router picks between three checkpoints (ModernBERT-large English, mmBERT-base multilingual for 100+ languages, and a typed-decisions fine-tune). It ships as a Python package with a CLI, optional HTTP and MCP servers, and LangChain, LlamaIndex and CrewAI integrations.
Who it is for: Engineers needing fast multilingual triage, routing and moderation classifiers
Strengths
- One forward pass: 33 ms per question, 7.2 ms/question batched, measured on a T4
- No generation, so no output parsing; answers are typed choice, score or probability
- Router selects English or multilingual checkpoint per request; 100+ languages covered
- Fine-tuning notebooks provided; typed-decisions checkpoint scores 0.766 vs 0.362 for base English
Weaknesses
- Zero-shot accuracy is low on domain tasks; fine-tuning is needed for good results
- Multilingual checkpoint ships with a 1,024-token limit; beyond about 4,000 tokens accuracy varies
- Only classifies fixed question types; cannot generate or explain text
- Checkpoints download from Hugging Face on first use, so offline setups need preparation
- GPU optional
- Docker + Compose
- Needs Hugging Face Hub (checkpoint download), PyTorch, transformers
- Models: ModernBERT-large (421M), mmBERT-base (322M), ONNX Runtime
unlimited-ocr
Unlimited-OCR is a Baidu document-parsing vision-language model, building on DeepSeek-OCR, with weights on Hugging Face. The repo ships inference code for Transformers, vLLM and SGLang, the last exposing an OpenAI-compatible chat API. It handles single images and multi-page input, with PDFs converted to images first.
Who it is for: Engineers self-hosting OCR for scanned documents and PDFs on NVIDIA GPUs
Strengths
- Multi-page parsing in one request, with 32768 max context length in the examples
- Three inference paths: Transformers, vLLM (Docker images), and SGLang with OpenAI-compatible API
- infer.py batch script launches SGLang and sends concurrent requests for image dirs or PDFs
- MIT license; training supported through ms-swift
Weaknesses
- Requires an NVIDIA GPU; examples tested only on CUDA 12.9 with Python 3.12
- PDFs must be rasterized to images with PyMuPDF before parsing
- SGLang setup needs a bundled dev wheel and custom logit processor flags
- VRAM requirement is not stated in the README
- GPU required
- Needs PyTorch 2.10.0, transformers 4.57.1, PyMuPDF, vLLM or SGLang (optional)
- Models: baidu/Unlimited-OCR (safetensors)
- port 10000
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