DeepSeek-OCR vs Nanonets OCR 2 (3B)
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: DeepSeek-OCR
In this head-to-head evaluation, DeepSeek-OCR takes the lead with an overall score of 9.3/10 compared to Nanonets OCR 2 (3B)'s 8.9/10. If your top priority is contextual optical compression utilizes 10x-20x fewer vision tokens with a 97% recovery rate, go with DeepSeek-OCR. If you value uniquely capable of transforming embedded visual diagrams into structured mermaid flowchart code, Nanonets OCR 2 (3B) is the superior choice.
Feature & Benchmark Comparison Matrix
Scroll horizontally on mobile →| Feature & Metric | DeepSeek-OCR Best Throughput & Optical Compression DeepSeek | Nanonets OCR 2 (3B) Best for Mermaid Flowcharts & Diagrams Nanonets (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 Source Weights (Apache 2.0 / Open Weights) | 100% Free Open Weights |
| Min Monthly Commitment | $0 / Pay-as-you-go | $0 / Pay-as-you-go |
| 🎯 OlmOCR-Bench & Accuracy Standards | ||
| OlmOCR-Bench Score (Unit Tests) | 75.7 /100 | 69.5 /100 |
| Table Structure (TEDS Score) | 93% | 91% |
| Handwriting Recognition | 86.5% (Good) | 85% (Good) |
| Single-Page Latency (p50) | 120 ms p95: 350ms | 380 ms p95: 850ms |
| ⚙️ Features & Document AI | ||
| Supported Languages | 80+ English, Chinese, Spanish, French... | 30+ English, Spanish, French, German... |
| Deployment Modes | Self-Hosted vLLM, Docker Container, Air-Gapped Private VPC | Self-Hosted vLLM, Docker Container, Cloud GPU |
| Bounding Polygon Precision | Block-level | Block-level |
| Searchable PDF / Markdown | ✅ Searchable PDF | ✅ Searchable PDF |
| Compliance | SOC2 • HIPAA • GDPR | SOC2 • HIPAA • GDPR |
| 💻 Developer Ergonomics | ||
| Official SDKs | Python, vLLM, Hugging Face Transformers, REST API via FastAPI | Python, Hugging Face, vLLM, REST API |
| Setup Time | ~25 mins | ~20 mins |
| Max Payload / Pages | 500MB / 5000 pages | 500MB / 2000 pages |
| Direct Links | ||
💰 Pricing & Monthly Cost Scenarios
For standard document OCR, Nanonets OCR 2 (3B) is more affordable at $0.00 per 1,000 pages compared to DeepSeek-OCR's $0.00 per 1,000 pages. When extracting structured tables and forms, DeepSeek-OCR charges $0.00/1k vs Nanonets OCR 2 (3B)'s $0.00/1k.
| Volume Tier | DeepSeek-OCR | Nanonets OCR 2 (3B) | 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 rigorous OlmOCR-Bench unit-test evaluation, DeepSeek-OCR leads with a score of 75.7 compared to Nanonets OCR 2 (3B)'s 69.5, demonstrating superior spatial neighbor relationship preservation and LaTeX equation rendering. Both solutions offer comparable table parsing quality (93% vs 91% TEDS score).
Speed & Latency Profile
DeepSeek-OCR delivers faster synchronous inference, averaging 120ms per single-page document (~260ms faster than Nanonets OCR 2 (3B)'s 380ms). Under heavy concurrency, DeepSeek-OCR's 95th percentile latency caps at 350ms compared to Nanonets OCR 2 (3B)'s 850ms.
Table & Structure Recognition
DeepSeek-OCR (93% TEDS) vs Nanonets OCR 2 (3B) (91% TEDS). DeepSeek-OCR provides native table bounding boxes and structural HTML/Markdown mappings. Nanonets OCR 2 (3B) includes dedicated table parsing capabilities.
Composite Performance Breakdown
DeepSeek-OCR Score Breakdown
Standardized 1-10 benchmark scaleNanonets OCR 2 (3B) Score Breakdown
Standardized 1-10 benchmark scaleWhen to Choose DeepSeek-OCR
Best suited for developers and companies that prioritize:
- ✓ Massive back-office document digitizing backlogs (millions of pages)
- ✓ High-throughput air-gapped defense and sovereign enterprise processing
- ✓ Low-cost LLM document indexing clusters
- ✓ You need faster response times (~120ms vs ~380ms)
- ✓ You require complete offline data privacy and zero API vendor lock-in
When 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
💻 Quickstart Code Snippets
See how each library processes a document in Python:
from vllm import LLM, SamplingParams
llm = LLM(model="deepseek-ai/deepseek-ocr-3b", trust_remote_code=True)
prompt = "<image>\nConvert this document page into structured Markdown."
outputs = llm.generate([{"prompt": prompt, "multi_modal_data": {"image": "page.jpg"}}])
print(outputs[0].outputs[0].text)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]))❓ DeepSeek-OCR vs Nanonets OCR 2 (3B) FAQs
Which is cheaper: DeepSeek-OCR or Nanonets OCR 2 (3B)? ▼
DeepSeek-OCR costs $0.00 per 1,000 base pages vs Nanonets OCR 2 (3B) at $0.00 per 1,000 base pages. For table parsing, DeepSeek-OCR is $0.00/1k vs Nanonets OCR 2 (3B) at $0.00/1k.
Which OCR API has higher accuracy: DeepSeek-OCR or Nanonets OCR 2 (3B)? ▼
In standardized benchmark testing on clean printed text, DeepSeek-OCR achieved 98.2% accuracy compared to Nanonets OCR 2 (3B)'s 97.2%. On complex table structure extraction, DeepSeek-OCR recorded a 93% TEDS score vs Nanonets OCR 2 (3B)'s 91% TEDS score.
Which API is faster: DeepSeek-OCR or Nanonets OCR 2 (3B)? ▼
DeepSeek-OCR has an average single-page response time of 120ms (p50 latency) vs Nanonets OCR 2 (3B)'s 380ms. Under high concurrency, DeepSeek-OCR reaches 350ms p95 latency vs Nanonets OCR 2 (3B)'s 850ms.
When should I choose DeepSeek-OCR over Nanonets OCR 2 (3B)? ▼
Choose DeepSeek-OCR if you prioritize: Massive back-office document digitizing backlogs (millions of pages), High-throughput air-gapped defense and sovereign enterprise processing, Low-cost LLM document indexing clusters. 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.