jinaai / jina-ocr-v1

Jina-OCR-v1: Efficient Document Parsing with Speculative Decoding and Reward-Dense Post-Training

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jina-ocr-v1 is an end-to-end document parsing model designed for high-quality OCR at an efficient serving point . The model builds on DeepSeek-OCR and inherits two of its efficiency-oriented components: a DeepEncoder vision tower that represents a 1024×1024 global view with 256 visual tokens and augments it with dynamic local tiles, and a 3B-parameter mixture-of-experts decoder with approximately 570M active parameters per token. On top of this backbone, we add a FastMTP speculative decoding head that recursively shares one dense draft block across K=3 prediction steps. Greedy verification accepts the longest token-equality prefix, preserving the verifier’s greedy output sequence by construction .

This repository ships the model weights and the custom code needed to load them ( trust_remote_code=True ). There is no separate training or serving package to install. Transformers uses the modeling files in this snapshot; vLLM needs a one-time architecture registration from deepseek_ocr_mtp.py (see Using vLLM ). A single script covers both backends: example.py .

python example.py --backend transformers --image document.png
python example.py --backend vllm --image document.png

Performance

At the default dynamic-resolution setting, jina-ocr-v1 scores 91.14 on OmniDocBench v1.6 and 83.4 on olmOCR-Bench, and has the highest page throughput of the fourteen systems we measured: 2.57 pages/s . Post-training adds 7.4 points on olmOCR-Bench over the DeepSeek-OCR backbone. On an NVIDIA L4, FastMTP nearly doubles decoding speed over greedy autoregressive decoding. Scores below use the eval prompt .

Three axes that determine deployment cost. (a) Pixels per visual token, log scale. DeepEncoder maps a 1024×1024 view from 4,096 patches to 256 tokens (3,887 pixels per visual token vs 783–1,022 for 28–32 px patch encoders). (b) Page throughput on olmOCR-Bench, one A100, concurrency 32. (c) Benchmark overall vs active parameters (log scale); the solid line joins Pareto-optimal systems. jina-ocr-v1 sits on both frontiers at 570M active parameters.

The same fourteen systems on olmOCR-Bench (one A100, concurrency 32), ranked by (a) output tokens/s, (b) output tokens/page, and (c) pages/s. Surya OCR 2 leads on tokens/s (3,760) but emits 3,568 tokens/page and finishes 1.05 pages/s. jina-ocr-v1 combines 2,792 tokens/s with 1,085 tokens/page and reaches 2.57 pages/s — the shortest outputs of any system scoring above 83.

Per-category breakdowns on olmOCR-Bench and OmniDocBench v1.6, FastMTP measurements on L4, and the full comparison set are in the technical report and the blog .

Prompt

Recommended default:

Transcribe the provided document image into a clean Markdown format, preserving the natural reading order.

The OmniDocBench and OlmOCR Bench scores on this card were measured with a stricter instruction (LaTeX math, HTML tables, drop headers/footers and figures):

Just return the plain text representation of this document as if you were reading it naturally.
Turn equations and math symbols into a LaTeX representation, make sure to use $ and $ as a delimiter for inline math, and $$ and $$ for block math. Do NOT use ascii or unicode math symbols such as ∈ ∉ ⊂ ⊃ ⊆ ⊇ ∅ ∪ ∩ ∀ ∃ ¬, just use LaTeX syntax, ex $ \in $ $ \notin $ etc. If you were going to surround a math expression in \( \) or \[ \] delimiters, surround it with $ $ or $$ $$ instead.
Convert tables into HTML format. Keep the syntax simple, but use <th> for header rows, and use rowspan and colspans appropriately. Don't use <br> inside of table cells, just split that into new rows as needed. Do NOT use LaTeX or Markdown table syntax.
Ignore all graphical content in the image document. Do not attempt to describe or convert the images.
Remove the headers and footers, but keep references and footnotes.
Read any natural handwriting.
This is likely one page out of several in the document, so be sure to preserve any sentences that come from the previous page, or continue onto the next page, exactly as they are.
If there is no text at all that you think you should read, you can output null.

Jina Reader

The quickest way to run the model: point r.jina.ai at a URL. Reader fetches the page or PDF, renders it, runs jina-ocr-v1 , and returns Markdown. Nothing to deploy, no image plumbing, same API key as the rest of the platform.

curl "https://r.jina.ai/https://example.com/document.pdf" \
  -H "Authorization: Bearer $JINA_API_KEY" \
  -H "X-Respond-With: jina-ocr-v1"

Add X-Page (1-indexed) to transcribe one page of a multi-page upload:

curl "https://r.jina.ai/https://example.com/document.pdf" \
  -H "Authorization: Bearer $JINA_API_KEY" \
  -H "X-Respond-With: jina-ocr-v1" \
  -H "X-Page: 2"

Both headers are in the Reader API editor ; the toggle writes them for you. Key from jina.ai .

Hosted API

For a single image (URL or data:image/...;base64,... ), the OpenAI-compatible endpoint talks to the model directly. Same key from jina.ai . Add "stream": true to stream.

curl https://api.jina.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $JINA_API_KEY" \
  -d '{
    "model": "jina-ocr-v1",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Transcribe the provided document image into a clean Markdown format, preserving the natural reading order."},
        {"type": "image_url", "image_url": {"url": "https://example.com/document.png"}}
      ]
    }]
  }'

Local file:

"url": "data:image/jpeg;base64,'$(base64 -i document.png)'"

A cold start returns HTTP 503 — retry after 30–60s.

Playground

Upload a page and inspect the Markdown in the Document OCR playground . No local setup; same key from jina.ai .

Transformers

Install transformers , torch , torchvision , Pillow . One PIL image per call. eos / pad come from generation_config.json ; do not pass a fresh GenerationConfig .

import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoProcessor

MODEL_ID = 'jinaai/jina-ocr-v1'
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, dtype=torch.bfloat16, trust_remote_code=True,
).to(device)

image = Image.open('document.png').convert('RGB')
inputs = processor.prepare_ocr_inputs(image, device=device)
output = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
print(processor.decode_ocr(output, inputs['input_ids']))

prepare_ocr_inputs(image, prompt=..., device=...) overrides the default prompt.

generate() attaches SlidingWindowNoRepeatNgramProcessor with defaults no_repeat_ngram_size=35 , ngram_window=1024 , and whitelist {128821, 128822} ( <td> , </td> ). Override with those keyword arguments, or disable with no_repeat_ngram_size=0 .

No FastMTP here. generate() is the 3B MoE decoder only. MTP keys ( mtp_module.* , mtp_embed_tokens.* ) are ignored on load.

vLLM (FastMTP)

Requires vLLM ≥ 0.21. Call register() once per process before LLM(...) . The snapshot directory must be on sys.path so worker processes can import deepseek_ocr_mtp .

import sys
from huggingface_hub import snapshot_download
from PIL import Image
from vllm import LLM

sys.path.insert(0, snapshot_download('jinaai/jina-ocr-v1'))
# local checkout: sys.path.insert(0, '/path/to/jina-ocr-v1')

from deepseek_ocr_mtp import DEFAULT_OCR_PROMPT, log_spec_stats, register, vllm_llm_kwargs, vllm_sampling_params

register()

llm = LLM(
    **vllm_llm_kwargs(
        'jinaai/jina-ocr-v1',
        num_speculative_tokens=3,  # K; 0 disables MTP
        mtp_heads=1,
        mtp_recursive=True,
    )
)

image = Image.open('document.png').convert('RGB')
outputs = llm.chat(
    [{
        'role': 'user',
        'content': [
            {'type': 'image_pil', 'image_pil': image},
            {'type': 'text', 'text': DEFAULT_OCR_PROMPT},
        ],
    }],
    sampling_params=vllm_sampling_params(max_tokens=4096),
)
log_spec_stats(llm)
print(outputs[0].outputs[0].text)

vllm_llm_kwargs() sets trust_remote_code , the OCR architecture override, and speculative_config.method="eagle" . FastMTP was trained with recursive hidden-state feedback; vLLM's default method="mtp" re-grounds every draft step on the target and breaks that. register() also maps EagleDeepSeekMTPModel (vLLM prepends Eagle when method="eagle" ).

vllm_sampling_params() sets temperature=0.0 , repetition_penalty=1.05 , and repetition_detection ( max_pattern_size=35 , min_pattern_size=35 , min_count=10 ). That uses vLLM's built-in n-gram stop instead of a custom logits processor, so it works with FastMTP. Pass repetition_detection=None to disable.

log_spec_stats(llm) flushes SpecDecoding metrics after a short run — vLLM otherwise prints them on a ~10s interval.

Chat content types image , image_pil , and image_url all insert <image> . Do not pass use_fast=False ; this checkpoint is LlamaTokenizerFast .

Citation

@misc{garcía2026jinaocr,
    title={Jina-OCR-v1: Efficient Document Parsing with Speculative Decoding and Dense Verifiable Rewards},
    author={Alejandro Barón García and Feng Wang and Emilia Garcia Casademont and Han Xiao},
    year={2026},
    eprint={2609.03181},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2609.03181},
}

License

CC BY-NC 4.0. Commercial use: contact .