DeepSeek-R1 API

DeepSeek R1 excels in reasoning tasks with advanced features like chain-of-thought processing and efficient parameter activation

Modalities: TextText
In / Out price: $0.3/$1.2per 1M
Context window: 125K tokens
Released: Aug 28, 2025

DeepSeek-R1 overview

DeepSeek-R1 by DeepSeek

Discover DeepSeek R1, an advanced reasoning model that outperforms competitors with high efficiency and open-source accessibility for developers.

DeepSeek-R1 specs

Model ID: deepseek/deepseek-reasoner
Developer: DeepSeek
Context window: 128,000 tokens
Max output: 127,000 tokens
Input: Text
Output: Text
API: OpenAI-compatible, base URL https://api.aimlapi.com/v1

Basic Information

Developer/Creator: DeepSeek AI
Release Date: January 21, 2025
Model Type: Large Language Model (LLM) focused on reasoning

Overview:

DeepSeek R1 is a cutting-edge reasoning model developed by DeepSeek AI, designed to excel in complex problem-solving, mathematical reasoning, and programming assistance. Leveraging a Mixture-of-Experts (MoE) architecture, the model activates only a subset of its parameters for each token processed, allowing for efficient computation while maintaining high performance across various tasks.

Key Features:

Mixture-of-Experts Architecture: Activates 37 billion out of 671 billion parameters per token, optimizing resource usage.
Chain-of-Thought Reasoning: Capable of breaking down complex problems into smaller, manageable steps for enhanced clarity and accuracy.
High Performance on Benchmarks: Achieves remarkable scores on various benchmarks such as 91.6% on the MATH benchmark and competitive ratings on coding challenges.
Reinforcement Learning Training: Utilizes pure reinforcement learning for training without extensive supervised fine-tuning, enhancing its reasoning capabilities.
Open Source Licensing: Available under the MIT license, allowing for unrestricted use and modification.

Intended Use:

DeepSeek R1 is intended for software developers, data scientists, and researchers who require advanced reasoning capabilities in their applications. It is particularly useful for tasks involving mathematical computations, coding challenges, and logical problem-solving.

Language Support:

The model primarily supports English but can accommodate multiple languages depending on user requirements.

Architecture:

DeepSeek R1 employs a Mixture-of-Experts (MoE) architecture that allows it to activate only a portion of its parameters during each forward pass. This design choice significantly reduces computational costs while maintaining high performance levels.

Training Data:

The model was trained on a large dataset consisting of diverse programming languages and mathematical problems.

Data Source and Size: The training dataset includes 14.8 trillion tokens sourced from various publicly available code repositories and mathematical texts.
Diversity and Bias: The training data was curated to minimize biases while maximizing diversity in topics and styles, ensuring robust performance across different scenarios.

Code Samples:

The model is available on the AI/ML API platform as "DeepSeek R1 .

API Documentation:

Detailed API Documentation is available here.

Ethical Guidelines

DeepSeek AI emphasizes ethical considerations in AI development by promoting transparency regarding the model's capabilities and limitations. The organization encourages responsible usage to prevent misuse or harmful applications of generated content.

Licensing

DeepSeek R1 is available under an open-source MIT license that allows both research and commercial usage rights while ensuring compliance with ethical standards regarding creator rights

Use cases

Advanced Mathematics: AIME, MATH, IMO-level problem solving
Code Generation & Reasoning: Handles complex code logic with precision
Logical Analysis: Chain-of-thought tasks and formal reasoning
Self-hosted Deployment: Users can run locally without API dependence
Cost-Sensitive Processing: Way cheaper than proprietary LLMs

Capabilities

Function calling
Streaming
Structured outputs
Reasoning

DeepSeek-R1 benchmarks by Artificial Analysis

See how DeepSeek-R1 compares in generation speed and latency. Data retrieved Nov 3, 2026.

Speed#39 / 219180Output tokens / second Latency#66 / 2160.99Seconds to first token Humanity's Last Exam#240 / 4588.5%Score GPQA Diamond#213 / 43970.8%Score

Speed

Output tokens / second

Benchmark results

Compare 7 models available on AI/ML API across 5 independent benchmarks.

Humanity's Last Exam

Expert-written questions across academic subjects.

DeepSeek-R1 API pricing

DeepSeek-R1 pricing in US dollars
Usage	Price	Unit
Input	$0.3	1M tokens
Output	$1.2	1M tokens
Cached input	$0.006	1M tokens

Prices from the AI/ML API price list, checked on Nov 4, 2026.

DeepSeek-R1 vs other models: token prices

AI/ML API input and output rates for 5 models, per million tokens.

AI/ML API model prices in USD per million tokens
Model	Input	Output
DeepSeek-R1	$0.3	$1.2
DeepSeek Chat (V4.1 Flash)	$0.3	$1.2
DeepSeek V4.1 Flash	$0.3	$1.2
DeepSeek V4 Flash Vision Exp	$0.44	$1.32
DeepSeek V4.1 Flash 1017	$0.39	$1.56

How to use the DeepSeek-R1 API

View docs

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "deepseek/deepseek-reasoner",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "deepseek/deepseek-reasoner",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "deepseek/deepseek-reasoner",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

All posts

DeepSeek-R1 FAQ

What is the context window of DeepSeek-R1?

DeepSeek-R1 has a 128,000 tokens context window and can return up to 127,000 tokens.

What inputs and outputs does DeepSeek-R1 support?

DeepSeek-R1 takes text as input and returns text.

What is the model ID for DeepSeek-R1?

Use deepseek/deepseek-reasoner as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.

When was DeepSeek-R1 released?

DeepSeek-R1 became available on August 28, 2025.

How much does DeepSeek-R1 cost?

DeepSeek-R1 costs $0.3 input and $1.2 output per 1M tokens on AI/ML API, billed as you go with no subscription.

Does DeepSeek-R1 support streaming?

Yes, DeepSeek-R1 can stream responses as they are generated.

Who created DeepSeek-R1?

DeepSeek-R1 was built by DeepSeek.

Start building with DeepSeek-R1

Get API key

DeepSeek-R1 API

deepseek/deepseek-reasoner

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "deepseek/deepseek-reasoner",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "deepseek/deepseek-reasoner",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "deepseek/deepseek-reasoner",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

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DeepSeek-R1 API

DeepSeek-R1 overview

DeepSeek-R1 by DeepSeek

DeepSeek-R1 specs

Basic Information

Overview:

Key Features:

Intended Use:

Language Support:

Architecture:

Training Data:

Code Samples:

API Documentation:

Ethical Guidelines

Licensing

Use cases

Capabilities

DeepSeek-R1 benchmarks by Artificial Analysis

Speed

Benchmark results

Humanity's Last Exam

GPQA Diamond

Terminal-Bench 4.0

AA-LCR

SciCode

DeepSeek-R1 API pricing

DeepSeek-R1 vs other models: token prices

How to use the DeepSeek-R1 API

Build with your agent

1Get your API key

2Make your first request

Related articles

DeepSeek-R1 FAQ

Start building with DeepSeek-R1

DeepSeek-R1 API

Build with your agent

1Get your API key

2Make your first request

DeepSeek-R1 API

DeepSeek-R1 overview

DeepSeek-R1 by DeepSeek

DeepSeek-R1 specs

Basic Information

Overview:

Key Features:

Intended Use:

Language Support:

Architecture:

Training Data:

Code Samples:

API Documentation:

Ethical Guidelines

Licensing

Use cases

Capabilities

Speed

Benchmark results

Humanity's Last Exam

GPQA Diamond

Terminal-Bench 4.0

AA-LCR

SciCode

DeepSeek-R1 API pricing

DeepSeek-R1 vs other models: token prices

How to use the DeepSeek-R1 API

Build with your agent

1Get your API key

2Make your first request

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