OpenAI, a leader in scaling Generative Pre-trained Transformer (GPT) models, has now introduced GPT-4o Mini, shifting toward more compact AI solutions. This move addresses the challenges of large-scale AI, including high costs and energy-intensive training, and positions OpenAI to compete with rivals like Google and Claude. GPT-4o Mini offers a more efficient and affordable approach to multimodal AI. This article will explore what sets GPT-4o Mini apart by comparing it with Claude Haiku, Gemini Flash, and OpenAI’s GPT-3.5 Turbo. We’ll evaluate these models based on six key factors: modality support, performance, context window, processing speed, pricing, and accessibility, which are crucial for selecting the right AI model for various applications.
Unveiling GPT-4o Mini:
GPT-4o Mini is a compact multimodal AI model with text and vision intelligence capabilities. Although OpenAI hasn’t shared specific details about its development method, GPT-4o Mini builds on the foundation of the GPT series. It is designed for cost-effective and low-latency applications. GPT-4o Mini is useful for tasks that require chaining or parallelizing multiple model calls, handling large volumes of context, and providing fast, real-time text responses. These features are particularly vital for building applications such as retrieval augment generation (RAG) systems and chatbots.
Key features of GPT-4o Mini include:
- A context window of 128K tokens
- Support for up to 16K output tokens per request
- Enhanced handling of non-English text
- Knowledge up to October 2023
GPT-4o Mini vs. Claude Haiku vs. Gemini Flash: A Comparison of Small Multimodal AI Models
This section compares GPT-4o Mini with two existing small multimodal AI models: Claude Haiku and Gemini Flash. Claude Haiku, launched by Anthropic in March 2024, and Gemini Flash, introduced by Google in December 2023 with an updated version 1.5 released in May 2024, are significant competitors.
- Modality Support: Both GPT-4o Mini and Claude Haiku currently support text and image capabilities. OpenAI plans to add audio and video support in the future. In contrast, Gemini Flash already supports text, image, video, and audio.
- Performance: OpenAI researchers have benchmarked GPT-4o Mini against Gemini Flash and Claude Haiku across several key metrics. GPT-4o Mini consistently outperforms its rivals. In reasoning tasks involving text and vision, GPT-4o Mini scored 82.0% on MMLU, surpassing Gemini Flash’s 77.9% and Claude Haiku’s 73.8%. GPT-4o Mini achieved 87.0% in math and coding on MGSM, compared to Gemini Flash’s 75.5% and Claude Haiku’s 71.7%. On HumanEval, which measures coding performance, GPT-4o Mini scored 87.2%, ahead of Gemini Flash at 71.5% and Claude Haiku at 75.9%. Additionally, GPT-4o Mini excels in multimodal reasoning, scoring 59.4% on MMMU, compared to 56.1% for Gemini Flash and 50.2% for Claude Haiku.
- Context Window: A larger context window enables a model to provide coherent and detailed answers over extended passages. GPT-4o Mini offers a 128K token capacity and supports up to 16K output tokens per request. Claude Haiku has a longer context window of 200K tokens but returns fewer tokens per request, with a maximum of 4096 tokens. Gemini Flash boasts a significantly larger context window of 1 million tokens. Hence, Gemini Flash has an edge over GPT-4o Mini regarding context window.
- Processing Speed: GPT-4o Mini is faster than the other models. It processes 15 million tokens per minute, while Claude Haiku handles 1.26 million tokens per minute, and Gemini Flash processes 4 million tokens per minute.
- Pricing: GPT-4o Mini is more cost-effective, pricing at 15 cents per million input tokens and 60 cents per one million output tokens. Claude Haiku costs 25 cents per million input tokens and $1.25 per million response tokens. Gemini Flash is priced at 35 cents per million input tokens and $1.05 per million output tokens.
- Accessibility: GPT-4o Mini can be accessed via the Assistants API, Chat Completions API, and Batch API. Claude Haiku is available through a Claude Pro subscription on claude.ai, its API, Amazon Bedrock, and Google Cloud Vertex AI. Gemini Flash can be accessed at Google AI Studio and integrated into applications through the Google API, with additional availability on Google Cloud Vertex AI.
In this comparison, GPT-4o Mini stands out with its balanced performance, cost-effectiveness, and speed, making it a strong contender in the small multimodal AI model landscape.
GPT-4o Mini vs. GPT-3.5 Turbo: A Detailed Comparison
This section compares GPT-4o Mini with GPT-3.5 Turbo, OpenAI’s widely used large multimodal AI model.
- Size: Although OpenAI has not disclosed the exact number of parameters for GPT-4o Mini and GPT-3.5 Turbo, it is known that GPT-3.5 Turbo is classified as a large multimodal model, whereas GPT-4o Mini falls into the category of small multimodal models. It means that GPT-4o Mini requires significantly less computational resources than GPT-3.5 Turbo.
- Modality Support: GPT-4o Mini and GPT-3.5 Turbo support text and image-related tasks.
- Performance: GPT-4o Mini shows notable improvements over GPT-3.5 Turbo in various benchmarks such as MMLU, GPQA, DROP, MGSM, MATH, HumanEval, MMMU, and MathVista. It performs better in textual intelligence and multimodal reasoning, consistently surpassing GPT-3.5 Turbo.
- Context Window: GPT-4o Mini offers a much longer context window than GPT-3.5 Turbo’s 16K token capacity, enabling it to handle more extensive text and provide detailed, coherent responses over longer passages.
- Processing Speed: GPT-4o Mini processes tokens at an impressive rate of 15 million tokens per minute, far exceeding GPT-3.5 Turbo’s 4,650 tokens per minute.
- Price: GPT-4o Mini is also more cost-effective, over 60% cheaper than GPT-3.5 Turbo. It costs 15 cents per million input tokens and 60 cents per million output tokens, whereas GPT-3.5 Turbo is priced at 50 cents per million input tokens and $1.50 per million output tokens.
- Additional Capabilities: OpenAI highlights that GPT-4o Mini surpasses GPT-3.5 Turbo in function calling, enabling smoother integration with external systems. Moreover, its enhanced long-context performance makes it a more efficient and versatile tool for various AI applications.
The Bottom Line
OpenAI’s introduction of GPT-4o Mini represents a strategic shift towards more compact and cost-efficient AI solutions. This model effectively addresses the challenges of high operational costs and energy consumption associated with large-scale AI systems. GPT-4o Mini excels in performance, processing speed, and affordability compared to competitors like Claude Haiku and Gemini Flash. It also demonstrates superior capabilities over GPT-3.5 Turbo, with notable advantages in context handling and cost efficiency. GPT-4o Mini’s enhanced functionality and versatile application make it a strong choice for developers seeking high-performance, multimodal AI.
