Introducing GPT-4o Mini: Most Cost-Efficient Small Model!

WorldofAI
18 Jul 202408:48

TLDROpenAI introduces GPT-4 Omni Mini, a cost-efficient AI model designed for developers, scoring 82% on the MMLU Benchmark. Priced at 15 cents per million input tokens, it's ideal for tasks requiring low cost and latency, such as customer support chatbots. The model supports text and vision, with plans to include multimodal inputs and outputs. It also features a 128k token context window and improved tokenizer for non-English text, ensuring safe and reliable responses with continuous monitoring for enhanced safety.

Takeaways

  • 🚀 OpenAI introduces GPT-4 Omni Mini, a cost-efficient AI model for developers.
  • 💰 The model is priced at 15 cents per million input tokens and 60 cents per million output tokens, making it significantly cheaper than previous models.
  • 📊 GPT-4 Omni Mini scores 82% on the MMLU Benchmark, outperforming GPT-4.1 in chat preferences.
  • 🔍 Ideal for tasks requiring low cost and latency, such as chaining or parallelizing multiple model calls.
  • 🔗 Supports text and vision in the API, with plans to include text, image, video, and audio inputs and outputs in the future.
  • 📚 Has a 128k token context window and supports up to 16k output tokens per request, allowing it to handle large volumes of context.
  • 🌐 Improved tokenizer enhances handling of non-English text, making it more efficient for multilingual applications.
  • 🏆 Outperforms GPT-3.5 Turbo and other small models in various tests, including reasoning, math, and coding tasks.
  • 🛡️ Built-in safety measures ensure reliable and safe responses, with harmful content filtered out during development.
  • 🔄 Uses reinforcement learning with human feedback to resist malicious prompts and improve safety for large-scale use cases.
  • 🆕 GPT-4 Omni Mini is available in the Assistant API for immediate access, with fine-tuning planned for the coming days.

Q & A

  • What is the GPT-4 Omni Mini model?

    -The GPT-4 Omni Mini is a cost-efficient AI model introduced by Open AI. It is designed to be more accessible and affordable, with reduced token usage, making it ideal for developers.

  • How does the GPT-4 Omni Mini perform on the MMLU Benchmark?

    -The GPT-4 Omni Mini scores an 82% on the MMLU Benchmark, outperforming the GPT-4.1 in chat preferences on the LMS leaderboard.

  • What are the pricing details for the GPT-4 Omni Mini model?

    -The GPT-4 Omni Mini is priced at 15 cents per million input tokens and 60 cents per million output tokens, which is significantly cheaper than previous models.

  • What tasks is the GPT-4 Omni Mini suitable for?

    -The GPT-4 Omni Mini is ideal for tasks that require low cost and latency such as chaining or parallelizing multiple model calls. It can handle large volumes of context or provide fast real-time text responses like customer support chat bots.

  • What types of inputs and outputs does the GPT-4 Omni Mini currently support?

    -Currently, the GPT-4 Omni Mini supports text and vision in the API, with future plans to include text, image, video, and audio inputs as well as outputs.

  • What is the token context window and output token limit for the GPT-4 Omni Mini?

    -The GPT-4 Omni Mini has a 128k token context window and supports up to 16k output tokens per request.

  • How does the GPT-4 Omni Mini handle non-English text?

    -The GPT-4 Omni Mini has an improved tokenizer that makes it more efficient in handling non-English text.

  • What safety measures are built into the GPT-4 Omni Mini?

    -The GPT-4 Omni Mini has built-in safety measures similar to the GPT-4 Omni model. It ensures reliable and safe responses, filters out harmful content during development, and uses techniques like reinforcement learning with human feedback to improve safety.

  • How does the GPT-4 Omni Mini compare to other models in terms of performance?

    -The GPT-4 Omni Mini outperforms models like GPT-3.5 Turbo, Gemini Flash, and Claude Hu in various tests, scoring higher in tasks requiring reasoning, math, and coding.

  • What are the future plans for the GPT-4 Omni Mini in terms of access and updates?

    -The GPT-4 Omni Mini is available as a text and vision model in the assistant API. Plans include rolling out fine-tuning for the model in the coming days, and it will replace the GPT-3.5 model in Chat GPT for free users and be accessible to enterprise users in the following week.

Outlines

00:00

🚀 Launch of GPT-4 Omni Mini: Affordable AI for Developers

The script introduces the GPT-4 Omni Mini, a cost-efficient AI model designed to make AI more accessible and affordable. With reduced token usage, it's particularly appealing to developers. The model scored an impressive 82% on the MML U Benchmark, outperforming its predecessor, GPT-4.1. Priced at 15 cents per million input tokens and 60 cents per million output tokens, it's significantly cheaper than previous models. The GPT-4 Omni Mini is capable of handling large volumes of context and providing fast real-time text responses, making it ideal for customer support chatbots and other tasks requiring low cost and latency. It currently supports text and vision in the API, with plans to include text, image, video, and audio inputs and outputs. The model also features a 128k token context window and supports up to 16k output tokens per request, enhancing its ability to retain and contain diverse knowledge up to October 2023. Its improved tokenizer makes it more efficient with non-English text.

05:00

🛡️ Enhanced Safety and Performance of GPT-4 Omni Mini

The second paragraph delves into the safety measures and performance of the GPT-4 Omni Mini. It has built-in safety features similar to the GPT-4 Omni model, ensuring reliable and safe responses by filtering out harmful content during development. Over 70 experts tested the model for risk, leading to improvements. The model also resists malicious prompts, making it safer for large-scale use cases. The script mentions the model's centralized nature and the author's preference for open-source models due to less restriction in content generation. The GPT-4 Omni Mini is available in the assistant API for text and vision, with competitive pricing that equates to roughly 2,500 pages of a standard book. Fine-tuning for the model is planned for the near future. The model is set to replace GPT-3.5 in Chat GPT's free and team user platforms, with enterprise users gaining access shortly. The script concludes with speculation about the release of GPT 5 and appreciation for Open AI's continuous innovation in reducing costs while improving model capabilities.

Mindmap

Keywords

💡GPT-4 Omni Mini

The GPT-4 Omni Mini is a newly introduced AI model by Open AI, designed to be cost-efficient and accessible for developers. It is characterized by reduced token usage, making AI more affordable and expanding its application range. In the video script, the model is highlighted for its performance on the MMLU Benchmark and its competitive pricing, which is significantly cheaper than previous models, making it ideal for tasks requiring low cost and latency.

💡Cost Efficiency

Cost Efficiency in the context of the video refers to the reduced expenses associated with using the GPT-4 Omni Mini model. It is a key selling point for developers who are looking to integrate AI into their projects without incurring high costs. The script mentions that the model is priced at 15 cents per million input tokens and 60 cents per million output tokens, which is over 60% less expensive than GPT 3.5 Turbo.

💡Token Usage

Token Usage pertains to the number of tokens, or units of text, that an AI model processes. The GPT-4 Omni Mini is praised for its reduced token usage, which directly translates to cost savings for developers. The script indicates that this model is particularly attractive because of its efficiency in handling tokens, contributing to its cost efficiency.

💡MMLU Benchmark

The MMLU Benchmark is a measure of an AI model's performance, specifically its ability to understand and process language. The GPT-4 Omni Mini scores an 82% on this benchmark, outperforming GPT 4.1 in chat preferences, which underscores its language comprehension capabilities and its suitability for various language-related tasks.

💡Latency

Latency in the context of AI refers to the delay between the input of a query and the model's response. The GPT-4 Omni Mini is noted for its low latency, making it suitable for real-time applications such as customer support chatbots. The script mentions that the model can handle large volumes of context or provide fast text responses, which is crucial for maintaining user satisfaction in real-time interactions.

💡Context Window

The Context Window is the amount of information an AI model can consider when generating a response. The GPT-4 Omni Mini has a 128k token context window, allowing it to retain and contain different types of knowledge up to October 2023. This is important for understanding the model's capacity to handle complex queries that require a deep understanding of context.

💡Output Tokens

Output Tokens represent the number of tokens that an AI model can generate in a single response. The GPT-4 Omni Mini supports up to 16k output tokens per request, which is significant for tasks that require comprehensive and detailed answers, such as generating lengthy reports or responses.

💡Multimodal

Multimodal refers to the ability of an AI model to process and understand multiple types of data, such as text, images, video, and audio. The script mentions that the GPT-4 Omni Mini currently supports text and vision in the API, with plans to include other modalities, enhancing its versatility and applicability in various applications.

💡Safety Measures

Safety Measures in AI models are protocols designed to ensure the model's responses are reliable and safe. The GPT-4 Omni Mini has built-in safety measures similar to the GPT-4 model, which filters out harmful content during development to align with safety policies. The script discusses the use of reinforcement learning with human feedback to test and improve the model's safety.

💡Fine-tuning

Fine-tuning is a process where an AI model is further trained on a specific dataset to improve its performance for a particular task. The script mentions that Open AI plans to roll out fine-tuning for the GPT-4 Omni Mini, which will allow developers to customize the model for their specific needs, enhancing its effectiveness in targeted applications.

💡Centralization

Centralization in the context of AI models refers to the control and standardization of responses and behaviors by a central authority or developer. The script expresses a concern about the potential for over-centralization, which may restrict creative generation and limit the diversity of outputs that can be prompted from the model.

Highlights

Introduction of GPT-4 Omni Mini, a cost-efficient AI model for developers.

Aims to make AI more accessible and affordable with reduced token usage.

GPT-4 Omni Mini scored 82% on the MML U Benchmark, outperforming GPT-4.1.

Pricing at 15 cents per million input tokens and 60 cents per million output tokens.

Over 60% less expensive than GPT 3.5 Turbo.

Ideal for tasks requiring low cost and latency, such as customer support chat bots.

Supports text and vision in the API, with plans to include text, image, video, and audio inputs/outputs.

128k token context window and supports up to 16k output tokens per request.

Improved tokenizer for better handling of non-English text.

GPT-4 Omni Mini excels in understanding text and handling multiple types of data.

Outperforms GPT 3.5 Turbo and other small models in various tests.

Supports many languages and performs well in reasoning, math, and coding tasks.

Scored 82% or 87% on the MGSM Benchmark for math and coding proficiency.

Strong performance on multimodal reasoning evaluation (MMU) with a 59.4% score.

Companies like Ramp and Superhuman found GPT-4 Omni Mini better for data extraction and email responses.

Built-in safety measures ensure reliable and safe responses, aligning with safety policies.

Uses reinforcement learning with human feedback to improve safety.

Continuous monitoring to enhance safety over time.

Available as a text and vision model in the Assistant API.

Pricing equivalent to 2,500 pages in a standard book for the same cost.

Fine-tuning for GPT-4 Omni Mini planned in the coming days.

Free access for Chat GPT Free and Pro users, and Enterprise users next week.

AI is getting smarter and cheaper, with a 99% drop in token usage compared to previous models.

Future models are expected to lower costs while improving performance.

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