OpenAI slashes the cost of using its AI with a "mini" model
OpenAI launches GPT-4o mini, a cheaper model enhancing AI accessibility. Meta to release Llama 3. Market sees a mix of small and large models for cost-effective AI solutions.
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OpenAI has introduced a more affordable "mini" model, GPT-4o mini, aiming to broaden access to its AI technology. This new model is priced 60% lower than OpenAI's cheapest existing model while delivering improved performance. The move is part of OpenAI's strategy to enhance AI accessibility amidst a competitive landscape flooded with free and small open-source AI models. Meta is set to unveil Llama 3, a free and capable offering, next week. OpenAI's success in the cloud AI market, particularly with its ChatGPT chatbot, has spurred competitors like Google and startups to develop similar large language models. OpenAI emphasizes the importance of making intelligence more affordable and accessible, highlighting advancements in model architecture and training data to enhance the new GPT-4o mini. The market trend shows a shift towards combining small and large models to optimize product experiences at reasonable costs. OpenAI also hints at the potential development of models for customer-run devices based on demand.
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11 commentsLooking at https://openai.com/index/gpt-4o-mini-advancing-cost-efficien... , gpt-4o-mini is better than gpt-3.5 but worse than gpt-4o, as was expected. gpt-4o-mini is cheaper than both, however. Independent third-party performance benchmarks will help.
Since the launch of Claude 3 Opus, and then Claude 3.5 Sonnet, they have been significantly behind Anthropic in terms of the general intelligence of their models. And instead of deploying something on par or better, they are making demos of video generation (Sora) or audio-to-audio models, not releasing anything.
GPT-4o is quite bad at coding, often getting stuck in a loop, and “fixing” buggy code by rewriting it without any changes.
GPT-4o is speculated to be a distillation of a larger model, and now GPT-4o-mini is an even dumber smaller model. But what’s the point?
Who is actually using small/fast/cheap/dumb models in production apps? Most real apps require higher reliability than even the biggest/slowest/priciest/smartest models can provide today. For the use case of transformers that has taken off, aiding students and knowledge workers in one-off tasks like writing code and prose, most users want smarter, more reliable outputs, even at the expense of speed and cost.
GPT-4o-mini seems like a move to increase margins, not make customers happier. That, like demoing products without launching them, is what big old slow corporations do, not how world-leading startups operate.
Edit: I’m amazed by how offended some people are by such a simple question.
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