In a groundbreaking update, OpenAI has launched GPT-4o mini, a cost-efficient, small-scale model expected to replace the popular GPT-3.5 Turbo. This new model promises to broaden the accessibility of AI, enabling businesses to leverage advanced technologies for general-purpose tasks and Retrieval-Augmented Generation (RAG) applications at a fraction of the cost. Recently, Claude launched 3.5 Sonnet which is currently beating all the benchmarks as per their evals. Let’s dive into what makes GPT-4o mini a game-changer, and how it stands out against competitors like Gemini Flash and Claude Haiku, and whether it is better than the previously launched GPT-4o
Why GPT-4o Mini?
GPT-3.5 turbo has been the default model for several AI applications as well as in ChatGPT. It has been a very effective model, the cheapest model offered by OpenAI. It is widely used even when there are much more powerful models such as Claude 3.5 sonnet and GPT-4o available. This launch puts a big question mark on the use of GPT-3.5-turbo as the new model is cheaper and better. Let’s look at the key aspects of GPT-4o Mini.
Cost-Efficiency
One of the standout features of the GPT-4o mini is its striking cost-effectiveness. Priced at just $0.15 per million input tokens and $0.60 per million output tokens, it is more than 60% cheaper than GPT-3.5 Turbo. This significant reduction in pricing makes it a highly appealing option for businesses that need to process large volumes of data or interact with clients in real-time.
Superior Performance
Despite its smaller size, GPT-4o mini outshines its predecessors and competitors on various academic benchmarks. For instance, it scores an impressive 82% on the MMLU (Massive Multitask Language Understanding), compared to 77.9% for Gemini Flash and 73.8% for Claude Haiku. This high performance extends across textual intelligence, multimodal reasoning, mathematical reasoning, and coding tasks.
Enhanced Multimodal Capabilities
Another advantage of GPT-4o mini is its ability to handle text and vision inputs proficiently, with future plans to support audio and video inputs. A context window of 128K tokens ensures it can handle extensive textual and multimedia data, making it ideal for intricate applications that require deep contextual understanding.
Is GPT-4o mini better than GPT-4o?
While GPT-4o mini offers impressive capabilities at a fraction of the cost of GPT-4o, determining which model is “better” depends on the specific use case and requirements. We tried several use cases for different personas. You can test each of these prompts in Bind AI Copilot and select different models to compare
| Use Case | Persona | Prompt | URL |
|---|---|---|---|
| Code Generation | Software Developer | Generate Python code that reads a CSV file, filters out rows where the ‘age’ column is less than 18, and saves the filtered data to a new CSV file. | Run Query |
| Code Generation | Data Scientist | Write Python code to train a Random Forest classifier on the Iris dataset, including data preprocessing steps and evaluation metrics like accuracy and F1-score. | Run Query |
| Content Writing | Blogger | Write a 500-word blog post about the benefits of mindfulness meditation, including personal anecdotes and practical tips for beginners. | Run Query |
| Content Writing | Marketing Professional | Create a compelling product description for a new line of eco-friendly reusable water bottles, focusing on their unique features and environmental benefits. | Run Query |
| General Reasoning | Educator | Explain the causes and consequences of the fall of the Roman Empire in a way that is understandable for high school students. | Run Query |
| General Reasoning | Policy Analyst | Analyze the potential long-term effects of implementing free college tuition on the national economy and workforce development. | Run Query |
Based on benchmarks and our evaluation, below is a high level assessment
- Performance: GPT-4o consistently outperforms GPT-4o mini across various benchmarks. For instance, on the MMLU benchmark, GPT-4o scores 88.7% compared to GPT-4o mini’s 82.0%. Similar patterns are seen in other evaluations like MGSM and HumanEval.
- Cost-effectiveness: GPT-4o mini is significantly more affordable, with input costs at $0.150 per million tokens compared to GPT-4o’s $5.00. This makes GPT-4o mini a more accessible option for a wider range of applications and developers.
- Input Tokens: GPT-4o Mini is approximately 97% cheaper than GPT-4o.
- Output Tokens: GPT-4o Mini is approximately 96% cheaper than GPT-4o.
- Capabilities: Both models offer multimodal capabilities and a 128K token context window. However, GPT-4o is described as having “stronger vision capabilities” and being faster than previous versions.
- Use case suitability: GPT-4o mini excels in tasks requiring low latency and high throughput, such as real-time customer interactions or processing large volumes of data. GPT-4o, with its superior performance, might be better suited for more complex tasks requiring the highest level of accuracy and capability.
- Resource efficiency: For many applications, GPT-4o mini may provide sufficient accuracy and capabilities while being more resource-efficient, allowing for wider deployment and scaling.
We previously wrote about Claude 3.5 Sonnet vs GPT-4o. It is clear that Claude 3.5 is better than GPT-4o, so the Mini version isn’t expected to be better than Claude.
GPT-4o mini is not “better” than GPT-4o in absolute terms, but it offers a compelling alternative that balances performance and cost. For many applications, GPT-4o mini may be the better choice due to its cost-effectiveness and strong capabilities, especially for RAG applications and general purpose AI usage. However, for tasks requiring the highest level of performance and advanced capabilities, GPT-4o remains the superior option. The “better” model ultimately depends on the specific requirements, budget constraints, and performance needs of each individual use case.
GPT-4o Mini vs GPT-3.5 turbo: Price Comparison
When compared to other models like GPT-3.5 turbo, Gemini Flash and Claude Haiku, GPT-4o mini stands out not only for its superior cost-efficiency but also for its higher efficacy across multiple benchmarks. It offers 8X the context window and 3X cheaper compared to GPT-3.5. It is 33X cheaper compared to GPT-4o, which is insane! While its not expected to be as powerful as GPT-4o or Claude 3.5 Sonnet, it will be the best in class model for its size.
Mini is also 60% cheaper than Claude Haiku. While Haiku has a higher context window of 200K tokens (vs 128K for GPT-4o mini), it is not as powerful. Even for simple tasks, it will be more effective to use GPT-4o Mini. For tasks such as code generation, Haiku only scores 75.9% vs 87.2% of GPT 4o mini, that’s a significant difference.
You can test GPT-4o, Claude Sonnet and Haiku with Bind AI
Take a look at the comparative table highlighting the key aspects:
| GPT-4o Mini | GPT-4o | Gemini Flash | Claude Haiku | |
|---|---|---|---|---|
| Pricing (Input Tokens) | $0.15 / 1M | $5.00 / 1M | $0.35 / 1M | $0.25 / 1M |
| Pricing (Batch API) | $0.075 / 1M | $2.50 / 1M | N/A | N/A |
| Pricing (Output Tokens) | $0.60 / 1M | $15.00 / 1M | $1.05 / 1M | $1.25 / 1M |
| Pricing (Batch API) | $0.30 / 1M | $7.50 / 1M | Not specified | Not specified |
| Features | Text & Vision | Text & Vision | Text & Vision | Text & Images |
| Context Window | 128K | 128K | Upto 1 million | 200K |
| Speed of Response | Fast | Fast | Moderate | Moderate |
| MMLU Score | 82% | 88.7% | 77.9% | 73.8% |
| Mathematical Reasoning | 87% (MGSM) | 90.5% (MGSM) | 75.5% (MGSM) | 71.7% (MGSM) |
| Coding Proficiency | 87.2% (HumanEval) | 90.2% (HumanEval) | 71.5% (HumanEval) | 75.9% (HumanEval) |
This table provides a comprehensive comparison across different key aspects, helping businesses make informed decisions about which model best fits their specific needs and resource availability.
Practical Applications
General Purpose Tasks
The design and affordability of GPT-4o mini make it perfect for a wide array of general-purpose tasks. Businesses can use it for text-based tasks like drafting emails, processing customer queries, summarizing documents, and extracting structured data from unstructured sources.
RAG Applications
Retrieval-Augmented Generation (RAG) involves the use of a retrieval system to fetch relevant information which the model then uses to generate responses. GPT-4o mini is particularly efficient for RAG applications due to its enhanced speed and lower costs, which enable the chaining of multiple model calls or handling a large volume of context. This can significantly improve the productivity and efficiency of customer support, content creation, and data synthesis applications.
Conclusion
OpenAI’s GPT-4o mini is setting new standards in the AI industry with its remarkable combination of cost-efficiency and performance. Tailored to replace GPT-3.5 Turbo, this new model broadens the range of applications for businesses while reducing operational costs significantly. Its superior efficacy in handling both general-purpose tasks and RAG applications, coupled with its affordability, makes GPT-4o mini a compelling choice for companies looking to leverage the power of AI in a more accessible and efficient manner.
We will be doing a follow up post with detailed comparison for practical use cases.
Meanwhile, Try Bind AI if you want to compare models such as GPT-4o, Claude 3.5 Sonnet, Mistral. If you are a developer, try Bind AI Code Generator,