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