Stanford and UW Researchers Create AI Reasoning Model Rivaling OpenAI's o1 for Under $50
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US researchers built a DeepSeek competitor for less than a tank of gas - and it's actually good
TL;DR: AI researchers from Standard and the University of Washington developed a competitive low-cost AI model, s1, using a small dataset and a budget under $50. AI researchers from Standard and the University of Washington claim to have made significant progress in the development of low-cost AI
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Researchers create reasoning model for under $50, performs similar to OpenAI's o1
Why it matters: Everyone's coming up with new and innovative ways to work around the massive costs involved with training and creating new AI models. After DeepSeek's impressive debut, which shook Silicon Valley, a group of researchers has developed an open rival that reportedly matches the
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Researchers created an open rival to OpenAI's o1 'reasoning' model for under $50 | TechCrunch
AI researchers at Stanford and the University of Washington were able to train an AI "reasoning" model for under $50 in cloud compute credits, according to a new research paper released last Friday. The model known as s1 performs similarly to cutting-edge reasoning models, such as OpenAI's o1 and
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Researchers trained an OpenAI rival in half an hour for less than $50
To do this, researchers at Stanford and the University of Washington used a method known as distillation -- which allows smaller models to draw from the answers produced by larger ones -- to refine s1 using answers from Google's AI reasoning model, Gemini 2.0 Flash Thinking Experimental. Google's
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Researchers created an AI reasoning model on par with OpenAI's o1 for less than $50
How researchers made a reasoning model on the cheap. Credit: Yuichiro Chino / Getty Images The floodgates have opened for building AI reasoning models on the cheap. Researchers at Stanford and the University of Washington have developed a model that performs comparably to OpenAI o1 and DeepSeek
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New AI Reasoning Model Rivaling OpenAI Trained on Less Than $50 in Compute
It's cheap to copy already built models from their outputs, but likely still expensive to train new models that push the boundaries. It is becoming increasingly clear that AI language models are a commodity tool, as the sudden rise of open source offerings like DeepSeek show they can be hacked
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This $6 AI model called s1 just challenged OpenAI's o1
A new AI model named s1, unveiled in a paper released on February 2, is garnering attention for its cost-effective performance that rivals OpenAI's o1, achieving significant capabilities at a training cost of just $6. The s1 model reaches performance levels close to state-of-the-art, utilizing
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Researchers Create a Low-Cost AI Model to Analyse How OpenAI's o1 Reasons
Researchers from Stanford University and Washington University have developed an open-source artificial intelligence (AI) model that is comparable in performance to OpenAI's o1 model. The main objective of the researchers was not to create a powerful reasoning-focused model but to understand how
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US researchers build $50 AI reasoning model, challenges OpenAI, DeepSeek
In tests involving math and coding, s1 exhibits performance comparable to cutting-edge models like OpenAI's o1 and DeepSeek's R1. "However, recent advances in reasoning, such as OpenAI's o1 and DeepSeek's r1, lack transparency, limiting broader research progress," said the research team. The
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Academic researchers find a way to train an AI reasoning model for less than $50
A small team of AI researchers from Stanford University and the University of Washington has found a way to train an AI reasoning model for a fraction of the price paid by big corporations that produce widely known products such as ChatGPT. The group has posted a paper on the arXiv preprint server
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Turns out, it's not that hard to do what OpenAI does for less
Even as OpenAI continues clinging to its assertion that the only path to AGI lies through massive financial and energy expenditures, independent researchers are leveraging open-source technologies to match the performance of its most powerful models -- and do so at a fraction of the price. Last
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Researchers from Stanford and the University of Washington have developed an AI reasoning model called s1, which performs comparably to OpenAI's o1 and DeepSeek's r1 in math and coding tasks. The model was created for less than $50 in cloud computing costs, challenging the notion that advanced AI development requires massive resources.
Stanford and UW Researchers Develop Low-Cost AI Reasoning Model
In a groundbreaking development, researchers from Stanford and the University of Washington have created an AI reasoning model that rivals industry leaders at a fraction of the cost. The model, named s1, demonstrates performance comparable to OpenAI's o1 and DeepSeek's r1 in math and coding tasks, while being developed for less than $50 in cloud computing costs
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.The s1 Model: A Cost-Effective Approach
The s1 model was built using a process called distillation, which allows smaller models to leverage the capabilities of larger ones during training. The researchers used Google's Gemini 2.0 Flash Thinking Experimental as the source model for distillation
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. The training process involved:- A carefully curated dataset of only 1,000 questions and answers
- Supervised fine-tuning (SFT) on an off-the-shelf AI model from Alibaba's Qwen
- A training time of less than 30 minutes using 16 Nvidia H100 GPUs
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Technical Innovations
The researchers employed several clever techniques to enhance s1's performance:
- Test-time scaling: By instructing the model to "wait," they extended its thinking time, leading to more accurate results
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. - Token budgeting: Controlling the amount of compute time for testing the model, forcing it to generate answers within set limits
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These approaches allowed s1 to achieve strong performance on certain AI benchmarks, particularly in coding and mathematics
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Implications for the AI Industry
The development of s1 has significant implications for the AI industry:
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Democratization of AI: It demonstrates that advanced AI models can be created without massive financial resources, potentially closing the gap between smaller players and industry giants
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Challenges to established business models: The ultra-low-cost training method questions the necessity of billions of dollars in compute power for AI development
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Legal and ethical considerations: The use of Google's Gemini model for distillation raises questions about intellectual property and terms of service violations
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Open-source availability: The s1 model, along with its training data and code, has been made available on GitHub, promoting transparency and collaboration in AI research
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Industry Reactions and Future Outlook
The development of s1 and similar low-cost models has sparked mixed reactions in the AI community:
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Excitement: Some view this as an opportunity for innovation without the need for massive financial backing
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Concern from major AI labs: OpenAI has accused DeepSeek of improperly harvesting data from its API for model distillation, highlighting the competitive tensions in the field
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Potential for further innovation: While distillation has shown promise in recreating existing capabilities, pushing the boundaries of AI may still require significant investment
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As the AI landscape continues to evolve, the development of s1 represents a significant step towards more accessible and cost-effective AI research and development. It challenges the status quo and may lead to a redistribution of power in the AI industry, from a few dominant players to a more diverse ecosystem of innovators
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