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Chandra OCR 2 is Datalab's optical character recognition model that converts documents to markdown, HTML, or JSON while preserving layout. It scores 85.8% on the olmOCR benchmark and supports 90+ languages with strong performance on tables, math, handwriting, and complex layouts.
- Chandra 2 achieves 85.8% on olmOCR benchmark (state-of-art), a 12% improvement over Chandra 1's 77.8% multilingual score, with particular strength in math, tables, and complex layouts
- Available through multiple deployment methods: HuggingFace Transformers, vLLM, SGLang, and Docker, with quickstart CLI tools for local use
- Handles 90+ languages, extracts images with captions, reconstructs forms accurately including checkboxes, and supports handwriting recognition across diverse document types
Datalab’s 4 billion-parameter Chandra OCR 2 outperforms GPT-4o and Gemini across independent and multilingual benchmarks, handling complex layouts, math notation, flowcharts and 90 languages with state-of-the-art accuracy. It’s available under Apache 2.0 code with a modified OpenRAIL-M license for weights, runs locally via HuggingFace or vLLM, and doubles throughput over its predecessor.
- Chandra OCR 2 (4B params, open-weight) scored 85.9% on olmOCR vs GPT-4o's 69.9%, and beat Gemini/GPT-5 Mini on multilingual benchmarks, with huge gains on South Asian scripts (Kannada +42.6, Malayalam +46.2, Telugu +39.1).
- It processes full pages in one pass rather than splitting into blocks, giving it an edge on tables, nested headers, checkboxes, handwritten math, and flowcharts exported as Mermaid diagrams.
- Despite shrinking from 9B to 4B parameters, throughput doubled to ~2 pages/sec on an H100 while accuracy improved.
- Code is Apache 2.0 and installable via pip/Docker, but weights use a modified OpenRAIL-M license requiring a paid commercial license for larger companies.
Chandra OCR 2, a 4 billion-parameter model from Datalab, outperforms GPT-4o and Gemini on AllenAI’s olmOCR benchmark and a 90-language test while halving the model size. It preserves layout, reads complex tables and math notation, converts diagrams to Mermaid, and runs at two pages per second on an NVIDIA H100. The code is Apache 2.0 but the model weights use an OpenRAIL-M license with commercial restrictions.
- Chandra OCR 2 scores 85.9% on olmOCR vs GPT-4o's 69.9%, while cutting model size from 9B to 4B parameters and hitting ~2 pages/sec on an H100
- Multilingual performance beats Gemini 2.5 Flash and GPT-5 Mini, with 40-46 point gains on scripts like Kannada, Malayalam and Telugu over Chandra 1
- Weights carry an OpenRAIL-M license requiring a paid commercial license once a company exceeds $2M in funding or revenue, despite Apache 2.0 code
- Handwriting recognition remains weak, dropping to ~50.4% accuracy on complex forms despite strong printed-text and table/math handling