Read GPT-4o vs GPT-4 Turbo in one article

Hello folks, I am Luga. Today we will talk about technologies related to the artificial intelligence (AI) ecological field - the GPT-4o model.

On May 13, 2024, OpenAI innovatively launched its most advanced and cutting-edge model GPT-4o, an initiative that marks a major breakthrough in the field of artificial intelligence chatbots and large-scale language models . Heralding a new era of artificial intelligence capabilities, GPT-4o boasts significant performance enhancements that surpass its predecessor, GPT-4, in both speed and versatility.

This groundbreaking advancement resolves the latency issues that often plagued its predecessor, ensuring a seamless and responsive user experience.

What is GPT-4o?

On May 13, 2024, OpenAI released its latest and most advanced artificial intelligence model GPT-4o, The "o" stands for "omni", which means "all" or "universal". This model is a new generation of large language model based on GPT-4 Turbo. Compared with previous models, GPT-4o has significantly improved in terms of output speed, answer quality, and supported languages, and has made revolutionary innovations in the format of processing input data.

The most noteworthy innovation of the GPT-4o+ model is that it abandons the previous model's practice of using independent neural networks to process different types of input data, and instead uses a single unified neural network to process all inputs. This innovative design gives GPT-4o+ unprecedented multi-modal fusion capabilities. Multimodal fusion refers to integrating different types of input data (such as images, text, audio, etc.) for processing to obtain more comprehensive and accurate results. Previous models needed to design different network structures when processing multi-modal data, which consumed a lot of computing resources and time. By using a unified neural network, GPT-4o+ achieves seamless connection of different types of input data, greatly improving processing efficiency. Traditional language models can usually only handle plain text input and cannot handle speech, Non-text data such as images. However, GPT-4o is unusual in that it can simultaneously detect and parse non-text signals such as background noise, multiple sound sources, and emotional colors in speech input, and fuse these multi-modal information into the semantic understanding and generation process to produce Richer, more contextual output.

In addition to processing multi-modal input, GPT-4o+ also demonstrates excellent excellent output capabilities when generating multi-lingual output. Not only does it output higher quality, more grammatically correct, and more concise expressions in mainstream languages ​​such as English, but GPT-4o+ can also maintain the same level of output in non-English language scenarios. This ensures that both English and other language users can enjoy GPT-4o+’s superior natural language generation capabilities.

In general, the biggest highlight of GPT-4o+ is that it breaks through the limitations of a single modality and achieves cross-modal comprehensive understanding and generation capabilities. With the help of innovative neural network architecture and training mechanism, GPT-4o+ can not only obtain information from multiple sensory channels, but also integrate it during generation to produce a more contextual and more personalized response.

GPT-4o and GPT-4 Turbo performance?

GPT-4 is the latest multi-modal large model launched by OpenAI. Compared with the previous generation GPT-4 Turbo, its performance is Great progress. Here we can conduct a comparative analysis of the two in the following key aspects. First, there is a difference in model size between GPT-4 and GPT-4 Turbo. GPT-4 has a larger number of parameters than GPT-4 Turbo, which means it can handle more complex tasks and larger data sets. This enables GPT-4 to have higher accuracy and fluency in semantic understanding, text generation, etc. Its

1. Inference speed

According to data published by OpenAI, under the same hardware conditions, the inference speed of GPT-4o is twice that of GPT-4 Turbo. This significant performance improvement is mainly attributed to its innovative single-model architecture, which avoids the efficiency loss caused by mode switching. The single-model architecture not only simplifies the calculation process but also significantly reduces resource overhead, allowing GPT-4o to process requests faster. Higher inference speed means that GPT-4o can provide users with responses with lower latency, significantly improving the interactive experience. Whether in real-time conversations, complex task processing, or applications in high-concurrency environments, users can experience smoother and more immediate service responses. This performance optimization not only improves the overall efficiency of the system, but also provides more reliable and efficient support for various application scenarios.

GPT-4o and GPT-4 Turbo latency comparison

2. Throughput

As we all know, the early GPT model had poor performance in throughput Performance is a bit lagging behind. For example, the latest GPT-4 Turbo can only generate 20 tokens per second. However, GPT-4o has made a major breakthrough in this regard, being able to generate 109 tokens per second. This improvement has significantly improved the processing speed of GPT-4o, providing higher efficiency for various application scenarios.

Despite this, GPT-4o is still not the fastest model. Taking Llama hosted on Groq as an example, it can generate 280 tokens per second, far exceeding GPT-4o. However, GPT-4o’s advantages go beyond speed. Its advanced functionality and reasoning capabilities make it stand out in real-time AI applications. GPT-4o's single model architecture and optimization algorithm not only improve computing efficiency, but also significantly reduce response time, giving it unique advantages in interactive experience.

GPT-4o and GPT-4 Turbo throughput comparison

Comparative analysis in different scenarios

Generally speaking, GPT- When 4o and GPT-4 Turbo handle different types of tasks, there are obvious differences in performance due to differences in architecture and modal fusion capabilities. Here, we mainly analyze the differences between the two from three representative task types: data extraction, classification and reasoning.

1. Data extraction

In text data extraction tasks, GPT-4 Turbo relies on its powerful natural language understanding capabilities to achieve good performance. But when encountering scenes containing unstructured data such as images and tables, its capabilities become somewhat limited.

In contrast, GPT-4o can seamlessly integrate data of different modalities. Whether it is in structured text or unstructured data such as images and PDFs, it can efficiently identify and Extract the required information. This advantage makes GPT-4o more competitive when processing complex mixed data.

Here, we take the contract scenario of a certain company as an example. The data set includes the master service agreement (MSA) between the company and the customer. Contracts vary in length, with some being as short as 5 pages and some being longer than 50 pages.

In this evaluation, we will extract a total of 12 fields, such as contract title, customer name, supplier name, details of termination clause, whether there is force majeure, etc. Through real data collection on 10 contracts, 12 custom evaluation indicators were set up using. These metrics are used to compare our real data to the LLM output for each parameter in the JSON generated by the model. Subsequently, we tested GPT-4 Turbo and GPT-4o. The following are the results of our evaluation report:

Evaluation based on the 12 indicators corresponding to each prompt Results

In the above comparison results, we can conclude that among these 12 fields, GPT-4o performs better than GPT-4 Turbo in 6 fields, and the results are the same in 5 fields. The performance dropped slightly in 1 field.

From an absolute perspective, GPT-4 and GPT-4o only correctly identify 60-80% of the data in most fields. Both models performed subpar in complex data extraction tasks that require high accuracy. Better results can be achieved by using advanced prompting techniques such as shot prompts or chain thought prompts.

Additionally, GPT-4o is 50-80% faster than GPT-4 Turbo in TTFT (time to first token), which gives GPT-4o an advantage in direct comparisons. The final conclusion is that GPT-4o outperforms GPT-4 Turbo due to its higher quality and lower latency.

2. Classification

Classification tasks often require extracting features from multi-modal information such as text and images, and then performing semantic-level understanding and judgment. At this point, since GPT-4 Turbo is limited to processing only a single text modality, its classification capabilities are relatively limited.

GPT-4o can fuse multi-modal information to form a more comprehensive semantic representation, thus showing excellent classification capabilities in fields such as text classification, image classification, and sentiment analysis, especially in some high-level applications. Difficulty in cross-modal classification scenarios.

In our tips, we provide clear instructions on when customer tickets are closed and add several examples to help resolve the most difficult cases.

By running the evaluation to test whether the model's output matches the ground truth data for 100 labeled test cases, here are the relevant results:

Classification analysis and evaluation reference

GPT-4o undoubtedly shows overwhelming advantages. Through a series of tests and comparisons on various complex tasks, we can see that GPT-4o far exceeds other competing models in overall accuracy, making it the first choice in many application fields.

However, while leaning towards GPT-4o as a general solution, we also need to keep in mind that choosing the best AI model is not an overnight decision-making process. After all, the performance of AI models often depends on specific application scenarios and trade-off preferences for different indicators such as precision, recall, and time efficiency.

3. Reasoning

Reasoning is a high-order cognitive ability of artificial intelligence systems, which requires the model to deduce reasonable conclusions from given preconditions. This is crucial for tasks such as logical reasoning and question and answer reasoning.

GPT-4 Turbo has performed well on text reasoning tasks, but its capabilities are limited when encountering situations that require multi-modal information fusion.

GPT-4o does not have this limitation. It can freely integrate semantic information from multiple modalities such as text, images, and speech, and perform more complex logical reasoning, causal reasoning, and inductive reasoning on this basis, thus giving the artificial intelligence system more "humanized" reasoning and judgment capabilities. .

Still based on the above scenario, let’s take a look at the comparison between the two at the inference level. For details, please refer to the following:

16 inference tasks Evaluation reference

According to the sample test of the GPT-4o model, we can observe that it performs increasingly better in the following inference tasks, as follows:

• Calendar calculation: GPT -4o is able to accurately identify when a specific date repeats, which means it can handle date-related calculations and reasoning.
• Time and angle calculation: GPT-4o is able to accurately calculate angles on clocks, which is very useful when dealing with clock and angle related problems.
• Vocabulary (Antonym Recognition): GPT-4o can effectively identify antonyms and understand the meaning of words, which is very important for semantic understanding and lexical reasoning.

Although GPT-4o performs increasingly better in certain reasoning tasks, it still faces challenges in tasks such as word manipulation, pattern recognition, analogical reasoning, and spatial reasoning. Future improvements and optimizations may further improve the model's performance in these areas.

To sum up, GPT-4o, which is based on a rate limit of up to 10 million tokens per minute, is a full 5 times that of GPT-4. This exciting performance indicator will undoubtedly accelerate the popularization of artificial intelligence in many intensive computing scenarios, especially in fields such as real-time video analysis and intelligent voice interaction. GPT-4o's high concurrency response capability will show unrivaled advantages .

The most shining innovation of GPT-4o is undoubtedly its revolutionary design that seamlessly integrates text, image, voice and other multi-modal input and output. By directly integrating and processing data from each modality through a single neural network, GPT-4o fundamentally solves the fragmented experience of switching between previous models, paving the way for building unified AI applications.

After realizing modal fusion, GPT-4o will have unprecedented broad prospects in application scenarios. Whether it is combining computer vision technology to create intelligent image analysis tools, seamlessly integrating with speech recognition frameworks to create multi-modal virtual assistants, or generating high-fidelity graphic advertisements based on text and image dual-modality, everything could only be achieved by integrating independent sub-models. The completed tasks, driven by the great intelligence of GPT-4o, will have new unified and efficient solutions.

Reference:

• [1] https://openai.com/index/hello-gpt-4o/?ref=blog.roboflow.com
• [2] https://blog.roboflow.com/gpt-4-vision/
• [3] https://www.vellum.ai/blog/analysis-gpt-4o-vs-gpt- 4-turbo#task1