DeepSeek: DeepSeek OCR
deepseek/deepseek-ocr
Access DeepSeek OCR from DeepSeek using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "deepseek/deepseek-ocr"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "deepseek/deepseek-ocr"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="deepseek/deepseek-ocr",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "deepseek/deepseek-ocr",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
DeepSeek OCR is a vision model from DeepSeek, released in October 2025, built for optical character recognition and document parsing. It takes images as input through a chat-style API and returns text, including documents converted to markdown.
Its core idea is optical context compression, representing text as a small number of vision tokens and decoding them back to text. In DeepSeek's paper it reaches 97% decoding precision at compression ratios under 10x, and about 60% at 20x.
On OmniDocBench it outperforms GOT-OCR 2.0 while using only 100 vision tokens per page (versus 256), and beats MinerU 2.0 with fewer than 800 vision tokens (versus 6,000+).
It fits document-heavy workloads: parsing PDFs and scans, extracting text and tables, and generating training data for other models.
Context Window 8K
tokens
Max Output 8K
tokens
Input Cost $0.04
per million tokens
Output Cost $0.08
per million tokens
Input text, image
modalities
Release Date Oct 20, 2025
Model Playground
Try DeepSeek OCR instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From DeepSeek
DeepSeek V4 Flash
DeepSeek V4 Flash is a lightweight, efficiency-focused Mixture-of-Experts model from DeepSeek, with 284B total parameters and 13B activated per token. It supports a 1M-token context window and configurable reasoning modes (standard, high, and max thinking effort). Designed as the fast and economical option in the V4 family, Flash delivers reasoning capabilities that closely approach the larger V4 Pro, and performs on par with it on simpler agentic tasks. In its max reasoning mode, it achieves comparable reasoning scores to Pro when given a larger thinking budget. At $0.14/M input and $0.28/M output tokens, it's one of the cheapest frontier-tier models available — well suited for high-throughput workloads like coding assistants, chat systems, and agent pipelines where latency and cost matter most.
ChatDeepSeek V4 Pro
DeepSeek V4 Pro is a 1.6T-parameter Mixture-of-Experts model from DeepSeek with 49B parameters activated per token, supporting a 1M-token context window. It is positioned as the strongest open-weight model currently available. V4 Pro leads all open-source models in math, coding, and STEM reasoning. On LiveCodeBench it scores 93.5, ahead of Gemini 3.1 Pro (91.7) and Claude Opus 4.6 (88.8). Its Codeforces rating of 3206 also tops GPT-5.4 (3168). On agentic tool-use benchmarks like MCPAtlas, it reaches near-parity with Opus 4.6. DeepSeek acknowledges it trails GPT-5.4 and Gemini 3.1 Pro overall by roughly 3–6 months of frontier development. Priced at $1.74/M input and $3.48/M output — a fraction of comparable closed-source models — it's a strong pick for complex reasoning, agentic coding, and knowledge-intensive tasks.
ChatDeepSeek V3.2
DeepSeek V3.2 is the December 2025 flagship model featuring DeepSeek Sparse Attention for efficiency and massive reinforcement learning post-training, achieving GPT-5-level performance. It's the first DeepSeek model to integrate thinking directly into tool-use and excels at agentic AI tasks.
Frequently Asked Questions
You can access DeepSeek OCR by DeepSeek through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add DeepSeek OCR to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.04 |
| Output | $0.08 |
DeepSeek OCR was created by DeepSeek and released on Oct 20, 2025.
DeepSeek OCR supports a context window of 8K tokens. For reference, that is roughly equivalent to 16 pages of text.
DeepSeek OCR can generate up to 8K tokens in a single response.
DeepSeek OCR accepts the following input types: text, image. It produces: text.
Yes — the DeepSeek OCR API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add DeepSeek OCR to your app without worrying about API keys or setup.
Read the Docs View Tutorials