DeepSeek

DeepSeek-OCR

deepseek/deepseek-ocr
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

DeepSeek-OCR 是 DeepSeek AI 发布的视觉语言模型,旨在探索视觉-文本压缩边界,专注于文档识别及图像转文本场景的解决方案 。该模型可将长文本渲染为高压缩比图像,在 10 倍无损压缩下能实现 97% 的 OCR 准确率,即使压缩到 20 倍也能保持约 60% 的准确率。

Input / output modalities
文本 · 图像 to 文本
Reference input / output price
Input¥0.0001Output¥0per 1M tokens
Context window
0
Added to catalog
Nov 28, 2025

Providers and pricing

ProviderInput /MOutput /MCached /MContextMax outputDetails
baidu¥0.3¥1.2¥08K8K
Gitee¥0.0001¥0¥000

baidu

Latency
0.52s
Throughput
477 tokens/s
Context
8K

Pricing

Input
¥0.3/M tokens
Output
¥1.2/M tokens
Cached
¥0/M tokens

Specifications

Context
8K
Max output
8K
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

Gitee

Latency
1.43s
Throughput
247 tokens/s
Context
0

Pricing

Input
¥0.0001/M tokens
Output
¥0/M tokens
Cached
¥0/M tokens

Specifications

Context
0
Max output
0
Supported APIs
/v1/chat/completions/v1/messages/v1/responses/v1beta/models/*/v1/models/*

DeepSeek-OCR code examples and API guide

Modelmesh normalizes requests and responses across service providers behind one consistent API.

Modelmesh provides an OpenAI-compatible Completion API for more than 300 models and service providers. Call it directly, through the OpenAI SDK, or with supported third-party SDKs.

Modelmesh-specific request headers in these examples are optional. When supplied, your application can appear on the Modelmesh rankings.

Supported endpointsSelect an endpoint to switch the example below.
/v1/chat/completions
from openai import OpenAI API_KEY = "$SSY_API_KEY" client = OpenAI( base_url="https://router.shengsuanyun.com/api/v1", api_key=API_KEY, ) try: completion = client.chat.completions.create( model="deepseek/deepseek-ocr", messages=[{"role": "user", "content": "Which number is larger, 9.11 or 9.8?"}], temperature=0.6, top_p=0.7, stream=True, ) response_text = "" for chunk in completion: if chunk.choices and chunk.choices[0].delta.content is not None: content = chunk.choices[0].delta.content print(content, end="", flush=True) response_text += content except Exception as error: print(f"Request failed: {error}")
                
              

Application data is temporarily unavailable. Model details remain usable.

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