Nanonets OCR 2 (3B) vs PaddleOCR-VL (0.9B)
Comprehensive 2026 technical breakdown comparing pricing per 1,000 pages, benchmark accuracy on printed text and tables, single-page latency, and developer ergonomics.
The Verdict: PaddleOCR-VL (0.9B)
In this head-to-head evaluation, PaddleOCR-VL (0.9B) emerges as the stronger option with an overall rating of 9.5/10 versus Nanonets OCR 2 (3B)'s 8.9/10. If your top priority is uniquely capable of transforming embedded visual diagrams into structured mermaid flowchart code, go with Nanonets OCR 2 (3B). If you value navit dynamic visual encoder processes images in their original aspect ratio, preventing visual distortion, PaddleOCR-VL (0.9B) is the superior choice.
Feature & Benchmark Comparison Matrix
Scroll horizontally on mobile →| Feature & Metric | Nanonets OCR 2 (3B) Best for Mermaid Flowcharts & Diagrams Nanonets (Open Source) | PaddleOCR-VL (0.9B) Top Sub-1B VLM (80.0 OlmOCR-Bench) Baidu (Open Source) |
|---|---|---|
| 💰 Pricing & Licensing | ||
| Base OCR (per 1,000 pages) | $0.00 (Open Source) | $0.00 (Open Source) |
| Table Extraction (per 1k pages) | $0.00 | $0.00 |
| Forms & Key-Values (per 1k) | $0.00 | $0.00 |
| Recurring Free Tier | 100% Free Open Weights | 100% Free and Open-Source under Apache 2.0 |
| Min Monthly Commitment | $0 / Pay-as-you-go | $0 / Pay-as-you-go |
| 🎯 OlmOCR-Bench & Accuracy Standards | ||
| OlmOCR-Bench Score (Unit Tests) | 69.5 /100 | 80 /100 |
| Table Structure (TEDS Score) | 91% | 94% |
| Handwriting Recognition | 85% (Good) | 88% (Good) |
| Single-Page Latency (p50) | 380 ms p95: 850ms | 110 ms p95: 280ms |
| ⚙️ Features & Document AI | ||
| Supported Languages | 30+ English, Spanish, French, German... | 109+ English, Chinese, Arabic, Russian... |
| Deployment Modes | Self-Hosted vLLM, Docker Container, Cloud GPU | Self-Hosted Python/C++, Edge / Mobile ONNX, Docker Container |
| Bounding Polygon Precision | Block-level | Word-level |
| Searchable PDF / Markdown | ✅ Searchable PDF | ✅ Searchable PDF |
| Compliance | SOC2 • HIPAA • GDPR | SOC2 • HIPAA • GDPR |
| 💻 Developer Ergonomics | ||
| Official SDKs | Python, Hugging Face, vLLM, REST API | Python, C++, ONNX Runtime, Hugging Face, REST API |
| Setup Time | ~20 mins | ~15 mins |
| Max Payload / Pages | 500MB / 2000 pages | 500MB / 5000 pages |
| Direct Links | ||
💰 Pricing & Monthly Cost Scenarios
For standard document OCR, PaddleOCR-VL (0.9B) is more affordable at $0.00 per 1,000 pages compared to Nanonets OCR 2 (3B)'s $0.00 per 1,000 pages. When extracting structured tables and forms, Nanonets OCR 2 (3B) charges $0.00/1k vs PaddleOCR-VL (0.9B)'s $0.00/1k.
| Volume Tier | Nanonets OCR 2 (3B) | PaddleOCR-VL (0.9B) | Cheaper Option |
|---|---|---|---|
| 10,000 pages/mo (Starter) | $10 | $10 | Equal Cost |
| 50,000 pages/mo (Growth) | $10 | $10 | Equal Cost |
| 250,000 pages/mo (Enterprise) | $20 | $20 | Equal Cost |
| 1,000,000 pages/mo (Scale) | $80 | $80 | Equal Cost |
🎯 Accuracy & Latency Breakdown
On the OlmOCR-Bench deterministic benchmark, PaddleOCR-VL (0.9B) outperforms Nanonets OCR 2 (3B) (80 vs 69.5), exhibiting fewer hallucinations on multi-column reading order and mathematical typography. For structured table recognition, PaddleOCR-VL (0.9B) takes the lead with a 94% TEDS score vs Nanonets OCR 2 (3B)'s 91%, accurately preserving merged cells and borderless column headers.
Speed & Latency Profile
PaddleOCR-VL (0.9B) is the faster engine with an average single-page response time of 110ms (vs Nanonets OCR 2 (3B)'s 380ms). This makes PaddleOCR-VL (0.9B) particularly advantageous for user-facing applications requiring instantaneous feedback.
Table & Structure Recognition
Nanonets OCR 2 (3B) (91% TEDS) vs PaddleOCR-VL (0.9B) (94% TEDS). Nanonets OCR 2 (3B) provides native table bounding boxes and structural HTML/Markdown mappings. PaddleOCR-VL (0.9B) includes dedicated table parsing capabilities.
Composite Performance Breakdown
Nanonets OCR 2 (3B) Score Breakdown
Standardized 1-10 benchmark scalePaddleOCR-VL (0.9B) Score Breakdown
Standardized 1-10 benchmark scaleWhen to Choose Nanonets OCR 2 (3B)
Best suited for developers and companies that prioritize:
- ✓ Engineering architecture documents with embedded flowchart diagrams
- ✓ Legal contracts requiring watermark and signature verification
- ✓ Scientific documents with structured schema diagrams
- ✓ You require complete offline data privacy and zero API vendor lock-in
When to Choose PaddleOCR-VL (0.9B)
Best suited for developers and companies that prioritize:
- ✓ Resource-constrained edge devices and mobile on-device OCR
- ✓ Multilingual document extraction (Arabic, Cyrillic, Chinese, Japanese, Korean)
- ✓ High-concurrency microservice OCR clusters with minimal VRAM
- ✓ You need faster response times (~110ms vs ~110ms)
- ✓ You require complete offline data privacy and zero API vendor lock-in
💻 Quickstart Code Snippets
See how each library processes a document in Python:
from transformers import AutoModelForVision2Seq, AutoProcessor
processor = AutoProcessor.from_pretrained("nanonets/nanonets-ocr2-3b")
model = AutoModelForVision2Seq.from_pretrained("nanonets/nanonets-ocr2-3b")
# Extract diagrams into Mermaid code
inputs = processor(images="diagram.png", text="Extract flowchart to mermaid:", return_tensors="pt")
outputs = model.generate(**inputs)
print(processor.decode(outputs[0]))from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PaddlePaddle/PaddleOCR-VL-0.9B", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("PaddlePaddle/PaddleOCR-VL-0.9B", trust_remote_code=True)
# Run inference
output = model.chat(tokenizer, image="document.png", prompt="Convert table to HTML")
print(output)❓ Nanonets OCR 2 (3B) vs PaddleOCR-VL (0.9B) FAQs
Which is cheaper: Nanonets OCR 2 (3B) or PaddleOCR-VL (0.9B)? ▼
Nanonets OCR 2 (3B) costs $0.00 per 1,000 base pages vs PaddleOCR-VL (0.9B) at $0.00 per 1,000 base pages. For table parsing, Nanonets OCR 2 (3B) is $0.00/1k vs PaddleOCR-VL (0.9B) at $0.00/1k.
Which OCR API has higher accuracy: Nanonets OCR 2 (3B) or PaddleOCR-VL (0.9B)? ▼
In standardized benchmark testing on clean printed text, Nanonets OCR 2 (3B) achieved 97.2% accuracy compared to PaddleOCR-VL (0.9B)'s 98.5%. On complex table structure extraction, Nanonets OCR 2 (3B) recorded a 91% TEDS score vs PaddleOCR-VL (0.9B)'s 94% TEDS score.
Which API is faster: Nanonets OCR 2 (3B) or PaddleOCR-VL (0.9B)? ▼
Nanonets OCR 2 (3B) has an average single-page response time of 380ms (p50 latency) vs PaddleOCR-VL (0.9B)'s 110ms. Under high concurrency, Nanonets OCR 2 (3B) reaches 850ms p95 latency vs PaddleOCR-VL (0.9B)'s 280ms.
When should I choose Nanonets OCR 2 (3B) over PaddleOCR-VL (0.9B)? ▼
Choose Nanonets OCR 2 (3B) if you prioritize: Engineering architecture documents with embedded flowchart diagrams, Legal contracts requiring watermark and signature verification, Scientific documents with structured schema diagrams. Choose PaddleOCR-VL (0.9B) if you prioritize: Resource-constrained edge devices and mobile on-device OCR, Multilingual document extraction (Arabic, Cyrillic, Chinese, Japanese, Korean), High-concurrency microservice OCR clusters with minimal VRAM.