- Developers & Engineers: Build reasoning workflows, coding assistants, math solvers, or deploy via Amazon Bedrock or Hugging Face.
- Data Scientists & Analysts: Automate complex math, reasoning, or code-based analysis at scale.
- Researchers & Academics: Study reinforcement learning-first training and open-source MoE models.
- Enterprises: Use R1 in AWS Bedrock with enterprise-grade security, guardrails, and compliance.
- Open-Source Community: Download and fine-tune on Hugging Face under the MIT license; also use distilled smaller versions.
How to Use DeepSeek-R1?
- Access the Model: Use Hugging Face or its API; or deploy via Amazon Bedrock for fully managed use.
- Submit Prompts: Send tasks in math, code, reasoning—R1 includes chain-of-thought outputs by default.
- Choose Deployment Path: Pick self-hosted (open-source) or managed (Amazon Bedrock) depending on scale and compliance needs.
- Leverage Distilled Versions: Use lighter R1-Lite or distilled 1.5B–70B variants for on-device inference.
- Deploy Responsibly: Enable Bedrock Guardrails and monitor output as R1 can exhibit geopolitical bias or safety gaps.
- MoE Architecture: 671B total params but only ~37B activated per task—combining power with efficiency.
- Reinforcement-Learning-First Reasoning: R1-Zero self-learns chains of thought; R1 adds supervised fine-tuning for clarity.
- Benchmark Leader: Scores 79.8% on AIME, 97.3% MATH-500, Codeforces Elo 2029, GPQA 71.5%—matching or surpassing o1.
- Open-Source with Commercial License: MIT-licensed weight availability enables self-hosting or managed deployment.
- Cost-Effective: Inference runs at 90–95% lower cost than o1; in AWS, provides managed deployment with guardrails.
- Frontier-level reasoning on par with closed-source systems
- Chain-of-thought outputs for transparency
- Open-source nature fosters collaboration and experimentation
- Lightweight distilled variants for diverse hardware
- Managed deployment with enterprise-grade security
- Bias issues: pro-CCP content found within reasoning outputs
- Safety vulnerabilities: full safety evaluations still ongoing
- Distilled quality vs full model: trade-offs in reasoning may occur at smaller sizes
Free
Free
Cached
$0.14/$2.19 per 1M tokens
- InputL $0.14 & Ouput: $2.19
- Offpeak/Discount: $0.035 (75% off, 16:30–00:30 UTC) - Off-peak/discount pricing applies daily between 16:30 and 00:30 UTC
Uncached
$0.55/$2.19
- InputL $0.55 & Ouput: $2.19
- Offpeak/Discount: $0.135 (75% off, 16:30–00:30 UTC) - Off-peak/discount pricing applies daily between 16:30 and 00:30 UTC
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TrainKore
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TrainKore
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LLM Gateway
LLM Gateway is a unified API gateway designed to simplify working with large language models (LLMs) from multiple providers by offering a single, OpenAI-compatible endpoint. Whether using OpenAI, Anthropic, Google Vertex AI, or others, developers can route, monitor, and manage requests—all without altering existing code. Available as an open-source self-hosted option (MIT-licensed) or hosted service, it combines powerful features for analytics, cost optimization, and performance management—all under one roof.
LLM Gateway
LLM Gateway is a unified API gateway designed to simplify working with large language models (LLMs) from multiple providers by offering a single, OpenAI-compatible endpoint. Whether using OpenAI, Anthropic, Google Vertex AI, or others, developers can route, monitor, and manage requests—all without altering existing code. Available as an open-source self-hosted option (MIT-licensed) or hosted service, it combines powerful features for analytics, cost optimization, and performance management—all under one roof.
Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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