AI model that changes the game that challenges the industry giant

DeepSeek was established in Hangzhou, China, and was established in May 2023 by Liang Wenfeng, a graduate of Zhijiang University. The company is operated as an independent AI research lab under a co -established luxury hedge fund. Deepseek’s accurate funds and evaluation numbers remain unreleased, but they specialize in the development of open source LLM. The first model debuted in November 2023, but in January 2025 the R1 Reasoning model was greatly recognized.

The release of Deepseek R1 shows a transformation of AI and exceeds the expectations set by previous models, including Deepseek-V3-BASE variants. Deepseek R1, which directly competes with O1 of OPENAI, is not just another AI model. It is a game changer, with state -of -the -art performance, cost efficiency, and surprising flexibility. The fact that Deepseek R1 is an open source with MIT license, gives a great possibility to companies and developers that seek powerful and commercially executed AI solutions.

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Despite the low budget of $ 6 million, Deepseek has achieved hundreds of dollar high -tech companies. This creates an AI model that competes with Openai O1 in both performance and efficiency. This is how they did:

• Budget efficiency: Deepseek R1 has been built for only $ 5.58 million, far below the estimated cost of development models such as O1 of Openai.
• Optimized resource use: Deepseek R1 was trained using 2.78 million GPU time. This is a part of the 30.8 million GPU time used by Meta for the same size model.
• Innovative training: Using restricted Chinese GPUs, Deepseek R1 has avoided technical and geopolitical constraints and optimizes wigs.
• Impressive benchmark: Deepseek R1 has the same performance as Openai O1 on several benchmarks, and may be even higher in specific areas.

Deepseek R1 indicates that even small teams can compete with giants in the industry, with strategic resources allocation, innovation, and efficiency.

What is being made Deepseek R1 Revolutionary AI?

Deepseek R1 is not just raw power. This is to make advanced AI more accessible and costly. The reason why this stands out is as follows.

• Open Weights & Mit license: Deepseek R1 is completely open source using Mit license, so developers can build commercial applications without license restrictions.
• Distillation model: DeepSeek offers smaller and fine -tuned variants, such as QWEN models and LLAMA models, and provide excellent performance while maintaining the efficiency of various use cases.
• API access: Deepseek R1 is easy to access through API, free chat platform and affordable price for large applications.
• Cost -effective: Deepseek R1 is much more affordable than competitors. For example, the price of the API is only $ 0.55 in input, $ 2.19 output per 100 token, $ 15 in Openai, and $ 60 for output.

Deepseek R1’s open source model allows developers and companies to access the highest layer AI at some costs of other models in combination with cost -effective price settings.

Deepseek R1 architecture: Blend of power and efficiency

At the core of Deepseek R1 is 671 billion parameter architectures based on the previous Deepseek V3 base model. The complete model boasts an impressive size, but most tasks only have 37 billion parameters and optimize calculation efficiency. Deepseek also offers six distilled versions, and each is adjusted to a specific use case to ensure flexibility and scalability.

Distilled model lineup

1. Deepseek-R1-Distill-Qwen-1.5B
2. Deepseek-R1-Distill-Qwen-7b
3. Deepseek-R1-Distill-Lama-8B
4. Deepseek-R1-Distill-Qwen-14b
5. Deepseek-R1-Distill-Qwen-32B
6. Deepseek-R1-Distill-Lama-70B

These distillation models are smaller than a complete R1 model, which provides high performance, and is ideal for developing environments and local systems that are restricted in resources.

Costed training and distillation

One of the important drivers behind the success of Deepseek R1 is a cost -effective training strategy. Instead of relying on expensive monitoring fine adjustments, DeepSeek R1 has adopted a combination of R1. Enhanced learning (RL) And strategic distillation:

• Enhanced learning (RL): Deepseek R1 uses pure RL for training so that the model can be autonomously improved without relying on a huge amount of labeling data. This greatly reduced costs related to human annotations.
• Distilled for efficiency: The distillation process of Deepseek R1 has transmitted high -level inference functions to small models so that even lighter variants can be executed at high levels without the calculation burden on a larger model.

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Benchmark Performance: Openai O1’s closely rival

The performance of DeepSeek R1 on the key benchmark falls directly to Openai O1, which is excellent in fields such as mathematics and software engineering. How DeepSeek R1 is accumulating against Openai O1 is as follows:

Benchmark comparison

1. Aime 2024 (Mathematical problem solving):
   • Deepseek R1: 79.8 % accuracy
   • Openai O1: 79.2 % accuracy
     Deepseek R1 is excellent in mathematics problem solving.
2. Codeforces (Competitive programming):
   • Deepseek R1: 96.3 %
   • Openai O1: 96.6 %
     OPENAI O1 offers a slight performance in competitive programming.
3. GPQA diamond (General question response):
   • Deepseek R1: 71.5 %
   • Openai O1: 75.7 %
     Openai O1 exceeds DeepSeek R1 with a general -purpose question response.
4. Math-500 (Mathematical problem solving):
   • Deepseek R1: 97.3 % accuracy
   • Openai O1: 96.4 % accuracy
     Deepseek R1 leads the mathematical problem solving accuracy.
5. mmlu (Understanding general knowledge):
   • Deepseek R1: 90.8 %
   • Openai O1: 91.8 %
     OPENAI O1 has some advantages in general knowledge tasks.
6. SWE-Bench verification (Software engineering task):
   • Deepseek R1: 49.2 %
   • Openai O1: 48.9 %
     DeepSeek R1 has won the software engineering task.

Overall review:

• Deepseek R1: Strong in accuracy in math inference, software engineering, and mathematics solutions.
• Openai O1: General -purpose tasks, competitive programming, and a little excellent in general knowledge.

Both models function in important areas, and Deepseek R1 has excellent mathematics and problem solving tasks, but Openai O1 has a more wide range of general knowledge tasks and Q & A’s superiority.

Practical application and accessibility

Deepseek R1 and its distillation models can be used through several platforms and provide flexible deployment options.

• DeepSeek chat platform: Free access to full R1 models for users.
• API access: Large -scale use cases are affordable, and developers can easily access them.
• Local development: Distilling models such as QWEN 8B and QWEN 32B can be deployed on local systems or virtual machines.

DeepSeek’s commitment to accessibility makes it easier for developers to integrate their powerful AI functions into applications without breaking the bank.

Conclusion: A new era of AI innovation

Deepseek R1 represents the perfect fusion of state -of -the -art AI technology and strategic resource management. Open source nature, cost -effective, and impressive performance proves that Deepseek R1 can develop AI models that are comparable to the largest high -tech companies, even small teams with limited resources.

For companies and developers, Deepseek R1 offers attractive alternative means to existing market options. Deepseek R1 may be a breakthrough you are looking for, regardless of whether you are working on mathematics, code, or general inference tasks.

Is Deepseek R1 the future of AI? Time has passed, but there is one certain thing. Innovation, efficiency, and flexibility are an important stone for their success.