ReasoningOpen SourceDeepSeek

DeepSeek R1

DeepSeek R1 is DeepSeek's reasoning-focused model designed for mathematical and logical problem-solving tasks, with a 64K token context window.

Context 64K
Tier Reasoning
Knowledge Nov 2024
License Open Source
Input from
$0.320 / 1M tokens
across 6 providers

API Pricing

Cheapest on OpenRouter 81% below avg
ProviderInput / 1MOutput / 1MUpdated
$0.320$0.8904/14/2026
$0.700$0.8004/4/2026
$1.05$1.054/13/2026
$1.35$5.404/14/2026
$3.00$7.004/14/2026
$3.75$0.0103/31/2026

Prices updated daily. Last check: 4/14/2026

Model Details

General

Creator
DeepSeek
Family
DeepSeek
Tier
Reasoning
Context Window
64K
Knowledge Cutoff
Nov 2024
Modalities
Text

Capabilities

Tool Calling
No
Open Source
Yes
Subtypes
Chat Completion

Strengths & Limitations

  • Open-source model with publicly available weights
  • Specialized reasoning architecture optimized for mathematical and logical tasks
  • 64,000 token context window supports extended problem-solving sequences
  • November 2024 knowledge cutoff provides relatively current information
  • Part of established DeepSeek model family with research backing
  • Text-based chat completion interface for straightforward integration
  • No tool calling or function execution capabilities
  • Text-only modality without image or multimodal support
  • Smaller context window compared to some frontier models
  • Reasoning specialization may limit general conversational performance
  • No structured output modes beyond standard text generation

Key Features

64K token context window
Text-based chat completion
Open-source model weights
Reasoning-optimized architecture
Mathematical problem-solving capabilities
Step-by-step logical analysis
November 2024 knowledge cutoff

About DeepSeek R1

DeepSeek R1 is a reasoning-specialized language model developed by DeepSeek, part of their open-source model family. This model is positioned as DeepSeek's dedicated reasoning tier offering, focusing specifically on mathematical computation, logical analysis, and multi-step problem-solving tasks rather than general-purpose chat applications. The model features a 64,000 token context window and supports text-only interactions through chat completion. DeepSeek R1 has a knowledge cutoff of November 2024, providing relatively current information. As an open-source release, the model weights and architecture details are publicly available for research and deployment, distinguishing it from proprietary reasoning models in the market. DeepSeek R1 is designed for applications requiring systematic reasoning and mathematical analysis. Unlike general-purpose models that handle broad conversational tasks, R1 specializes in scenarios where step-by-step logical processing is essential, making it suitable for educational tools, research applications, and technical problem-solving workflows.

Common Use Cases

DeepSeek R1 is suited for applications requiring systematic reasoning and mathematical analysis. Its reasoning specialization makes it appropriate for educational platforms teaching mathematics and logic, research tools for academic problem-solving, and technical applications where step-by-step logical processing is essential. The model's open-source nature enables custom deployments for organizations needing on-premises reasoning capabilities, while its 64K context window supports extended mathematical proofs and multi-step analytical workflows that require maintaining context across lengthy problem-solving sequences.

Frequently Asked Questions

How much does DeepSeek R1 cost per million tokens?

DeepSeek R1 pricing varies by provider and deployment method. As an open-source model, you can also deploy it yourself. Check the pricing table above for current rates across all API providers.

What is DeepSeek R1 best used for?

DeepSeek R1 is optimized for mathematical reasoning, logical problem-solving, and multi-step analytical tasks. Its reasoning-focused architecture makes it particularly effective for educational applications, research problems, and technical scenarios requiring systematic logical processing rather than general conversation.

How does DeepSeek R1 compare to general-purpose models for reasoning tasks?

DeepSeek R1 is specifically architected for reasoning tasks, unlike general-purpose models that balance reasoning with conversational abilities. This specialization may provide advantages for mathematical and logical problems, though general-purpose models offer broader capabilities including tool calling and multimodal support that R1 lacks.