HunyuanOCR API
Released November 2025 | 16K Tokens context | 1.0B params parameters
HunyuanOCR API enables Dense Document Digitization (PDF/Scanned), Key-Information Extraction (KIE) from Receipts, Multilingual Scene Text Spotting, Handwriting Recognition, LaTeX Formula Extraction, and End-to-End Image Translation. Released in late 2025, HunyuanOCR is an open-source contribution from Tencent that outperforms many larger proprietary models. It utilizes a "Global-to-Local" architecture with a SigLIP-v2 visual encoder to handle high-resolution inputs and extreme aspect ratios (like long receipts) without splitting images artificially. Standout strengths include Lightweight (1B parameters) with SOTA performance and Native support for high-resolution & extreme aspect ratios. It is designed for document-heavy pipelines that require dependable extraction quality across mixed layouts and multilingual content.
from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="tencent/HunyuanOCR", messages=[ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ], max_tokens=4096, temperature=0, stream=True, extra_body={ "language": "en", "ocr_mode": "general", } ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n")from openai import OpenAI # Initialize the OpenAI client with Qubrid base URL client = OpenAI( base_url="https://platform.qubrid.com/v1", api_key="QUBRID_API_KEY", ) stream = client.chat.completions.create( model="tencent/HunyuanOCR", messages=[ { "role": "user", "content": [ { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ], max_tokens=4096, temperature=0, stream=True, extra_body={ "language": "en", "ocr_mode": "general", } ) for chunk in stream: if chunk.choices and chunk.choices[0].delta.content: print(chunk.choices[0].delta.content, end="", flush=True) print("\n") Enterprise
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Document Intelligence Platform