DeepSeek-R1 is the latest AI model developed by DeepSeek, designed to provide advanced reasoning capabilities that can rival the best in the field. It builds upon the success of DeepSeek-R1-Zero and achieves performance on par with OpenAI o1 across a diverse range of tasks, including math, code, and reasoning. The model provides users with the ability to perform complex reasoning tasks at a fraction of the cost as compared to OpenAI’s GPT-4o as an open-source alternative. The model is now available on multiple platforms, including web, app, and API, making it accessible to a wide range of users and developers.
Table of Contents
Key Features of DeepSeek-R1
1. Advanced Reasoning Capabilities
The model uses Chain of Thought (CoT) reasoning to handle complex reasoning tasks. This feature allows the model to generate intermediate reasoning steps before arriving at a final answer.
2. Scalable and Flexible
The model supports a remarkable context length of 64K tokens, accommodating extensive input and enabling detailed responses. The model can generate a maximum of 32K tokens for reasoning content, followed by up to 8K tokens for the final output, making it ideal for large-scale tasks.
3. Cost-Effectiveness
This model offers GPT-4o-level performance at approximately 1/50th of the cost. This makes it a viable option for startups and small businesses.
4. Open Source
The model is entirely open source. This allows developers and researchers to modify and adapt the model for various applications. You can access the model at Hugging Face: DeepSeek-R1.
The Training Process of DeepSeek-R1
The training process comprises two pivotal phases: the integration of cold-start data and the application of reasoning-oriented reinforcement learning.
1. Cold-Start Data Integration
The cold-start phase plays a crucial role in the training process. By gathering thousands of long Chain-of-Thought (CoT) examples, DeepSeek-R1’s foundational model, DeepSeek-V3, undergoes fine-tuning, allowing it to establish a robust groundwork for addressing more intricate reasoning tasks. This dataset is carefully curated to ensure clarity and coherence, directly addressing the limitations observed in its predecessor. The intentional design of output formats enhances user engagement and satisfaction, leading to a more polished interactive experience.
2. Reinforcement Learning for Enhanced Reasoning
Following the cold-start phase, the model enters the reasoning-oriented reinforcement learning stage. This phase is pivotal in refining the model’s capabilities to solve complex problems across diverse domains, including mathematics, science, and coding. By employing Group Relative Policy Optimization (GRPO) as the RL framework, DeepSeek-R1 optimizes its performance through a structured reward system that emphasizes both accuracy and language consistency.
Benchmark Performance and Evaluation Results
The evaluation of this model reveals its superior performance across an array of benchmark tests. For instance, on the AIME 2024 benchmark, DeepSeek-R1 achieves a noteworthy 79.8% Pass@1 score, surpassing the performance metrics of OpenAI’s o1-1217. In mathematical reasoning tasks, it records an impressive 97.3% Pass@1 on the MATH-500 benchmark, outperforming OpenAI o1.
When placed alongside other models, it consistently demonstrates a competitive edge. Evaluations on knowledge benchmarks such as MMLU and GPQA Diamond reveal outstanding results, with scores of 90.8% and 71.5%, respectively. It is rivalling OpenAI’s o1-1217 and consistently outperforms other closed-source models, establishing itself as a leader in educational tasks.
In mathematical reasoning, it surpassed top-performing models like OpenAI’s o1-1217 and Claude 3.5 Sonnet by a significant margin. In coding tasks, such as LiveCodeBench and Codeforces, it exhibits a strong command with OpenAI o1. Additionally, in competitive coding environments, the model showcases exceptional capabilities, achieving a 2,029 Elo rating on Codeforces, placing it among the top percentile of human participants.
Moreover, DeepSeek-R1 excels in creative writing and general question answering, achieving an impressive length-controlled win rate of 87.6% on AlpacaEval 2.0. Its performance on long-context understanding tasks further solidifies its reputation as a versatile and robust reasoning model.
DeepSeek-R1 Distilled Models
The evaluation of distilled models derived from DeepSeek-R1 highlights their efficiency across reasoning-related benchmarks. For instance, the DeepSeek-R1-Distill-Qwen-7B model consistently outperforms models like GPT-4o-0513, reflecting the effectiveness of the distillation process. In comparisons, DeepSeek-R1-14B surpasses QwQ-32B-Preview across all metrics, while both DeepSeek-R1-32B and DeepSeek-R1-70B outperform OpenAI’s o1-mini and Claude 3.5 Sonnet. These results emphasize the potential of distillation to enhance reasoning capabilities further.
Below are its distilled models:
- DeepSeek-R1-Distill-Qwen-1.5B
- DeepSeek-R1-Distill-Qwen-7B
- DeepSeek-R1-Distill-Llama-8B
- DeepSeek-R1-Distill-Qwen-14B
- DeepSeek-R1-Distill-Qwen-32B
- DeepSeek-R1-Distill-Llama-70B
DeepSeek-R1 vs. OpenAI o1 Pricing
The model offers a significant advantage over OpenAI’s Model o1. While the o1 model is priced at $2.50 per 1M input tokens and $10.00 per 1M output tokens, DeepSeek-R1 is available at $0.14 per 1M input tokens (cache hit) and $2.19 per 1M output tokens. This pricing difference makes this model an incredibly accessible and cost-effective solution for users seeking advanced reasoning capabilities. This allows everyone to use this model without prohibitive financial constraints.
| Model | 1M Tokens Input Price | 1M Tokens Output Price |
| DeepSeek-R1 | $0.14 | $2.19 |
| DeepSeek-R1 | $0.14 | $2.19 |
How to Get Started With DeepSeek Reasoner
1. Chat Website
You can use the model on DeepSeek’s official website, chat.deepseek.com, and switch on the button “DeepThink.”
2. DeepSeek-R1 API
The model is easily accessible through its API. To use the model, users must ensure they have the latest OpenAI SDK installed. To interact with it, users need to formulate their queries in a structured manner. For example, a simple Python code snippet demonstrates how to use the API to access both the reasoning content and the final answer:
from openai import OpenAI
client = OpenAI(api_key="<DeepSeek API Key>", base_url="https://api.deepseek.com")
# Round 1
messages = [{"role": "user", "content": "9.11 and 9.8, which is greater?"}]
response = client.chat.completions.create(model="deepseek-reasoner", messages=messages)
reasoning_content = response.choices[0].message.reasoning_content
content = response.choices[0].message.content
# Round 2
messages.append({"role": "assistant", "content": content})
messages.append({"role": "user", "content": "How many Rs are there in the word 'strawberry'?"})
response = client.chat.completions.create(model="deepseek-reasoner", messages=messages)
This approach allows for iterative querying, where users can build upon previous interactions, making the conversation more dynamic and engaging.
Concluding Remarks
DeepSeek-R1 offers a significant competitive advantage over OpenAI o1 with its advanced reasoning capabilities, cost-effective pricing, and open-source accessibility. The model provides users with a powerful tool for achieving complex reasoning tasks. DeepSeek has plans for future enhancements to this model, including the introduction of new parameters that will further refine its reasoning capabilities. These updates aim to make the model even more powerful and versatile, allowing it to tackle increasingly complex tasks.
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