Hi everyone, with you as always Radzivon from Latenode and today we are waiting for the final article on AI Anthropic Claude 3, we went to this for a long time and spent a huge amount of time researching this AI Model.
The experiments were aimed at evaluating the models' abilities in areas such as generating attractive content, analyzing complex scientific texts, creating personalized recommendations, writing code, and translating from foreign languages. Additionally, we will examine the accessibility and pricing policies of services based on ChatGPT-4 and Claude 3 Opus.

On the Latenode platform, there is the ability to utilize both ChatGPT-4 and Claude 3 Opus, which can be valuable tools for the community focused on low-code automation and empowering users. The strong analytical and problem-solving skills of these models, particularly Claude 3's impressive capabilities in tackling complex problems, can make them indispensable assistants for Latenode users working on automation projects. Additionally, the personalized recommendation and translation abilities of these language models, such as Claude 3's nuanced approach, can significantly enhance the user experience and enable seamless collaboration across the global Latenode community.

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The point of this article is to do a full scale research and comparison of the two major players in the AI market, namely Chat GPT 4 and Claude 3 Opus. Well, let's start comparing.

Comparing the Ability to Write a Guide For Simple Automation

The objective of the experiment was to compare the performance of two advanced language models, ChatGPT-4 and Claude 3 Opus, in creating an informative and engaging guide on simple automation using Google Sheets integration. The goal was to determine which model could produce content that is more structured, understandable, and useful for readers, particularly those interested in low-code automation solutions.

Evaluation of results: The texts generated by ChatGPT-4 and Claude 3 Opus were offered to the Latenode’s Low-code automation community for 450 people and this is what we got:
The participants of the experiment were asked to choose the best variant in their opinion. According to the voting results, the text generated by Claude 3 Opus received a significant advantage: 80% of people voted for it. ChatGPT-4 was able to interest only 20% participants.

This experiment demonstrates the superiority of Claude 3 Opus over ChatGPT-4 in generating texts that appeal to readers, at least in this particular case. Of course, a large-scale study on a larger amount of data is required for more accurate conclusions. Nevertheless, the result of this test can serve as one of the indicators of the potential and competitive advantages of Claude 3 Opus.

For clarity, here are three illustrations showing the key features of the winning text generated by Claude 3 Opus:

Conclusions: The illustrated features of the text generated by Claude 3 Opus help the reader to better than GPT-4 understand the topic, follow the instructions and put the knowledge into practice. It is these qualities that allowed Claude 3 Opus to win a convincing victory over ChatGPT-4 in this experiment.

Solving Logical Problems

Objective of the Experiment was to assess the reasoning capabilities of Claude 3 and ChatGPT-4 by presenting them with the classic Monty Hall problem, a well-known logic puzzle that has a counterintuitive solution.
Comparison and Analysis of Results: When solving the Monty Hall problem, Claude 3 demonstrated a deep understanding of the underlying logic and probabilities. It provided a detailed explanation, walking through the reasoning step-by-step. Claude 3 meticulously explained why the participant should switch their choice to the other unopened door to increase their probability of winning from 1/3 to 2/3.

ChatGPT-4 was also able to correctly solve the Monty Hall problem and arrive at the same conclusion - that the participant should switch their choice. However, its response did not go into the same level of depth as Claude 3 in explaining the logic and probabilities behind the solution.

Both AI models correctly solved the Monty Hall problem, but there was a notable difference in their approaches:

• Claude 3 took a more thorough and analytical approach, providing a comprehensive explanation that explored the underlying reasoning and detailed probabilities involved. This approach not only solved the problem but also educated the user on why the solution works, enhancing understanding of the logic behind the decision to switch doors.
• ChatGPT-4, while arriving at the correct solution, provided a more concise explanation. It did not offer the same level of detailed justification as Claude 3, which suggests it might be less effective in helping users fully understand the rationale behind the solution in more complex logical reasoning tasks.

Conclusions: This experiment highlights that while both Claude 3 and ChatGPT-4 are capable of solving logical problems like the Monty Hall problem, Claude 3 has an advantage in providing more comprehensive and insightful explanations. Claude 3’s ability to delve deeper into the logic and probabilities makes it more suitable for tasks that require not just an answer, but a thorough understanding of the reasoning process involved. This suggests that in complex logical reasoning tasks, Claude 3 may be the preferred choice for users looking for detailed and educative explanations.

Comprehension of complex scientific text

Both models were provided with a scientific text describing a study aimed at reducing prescribing errors in public hospitals in Kuwait. The task was to analyze the text and provide a brief summary of the study objectives, methodology and limitations.

‍Evaluation of results: Claude 3 demonstrated a deeper understanding of the text and provided a more accurate and complete summary of the study. The model accurately highlighted key objectives including developing a "no-name-no-fault" reporting system, creating a national training program, and comparing error rates before and after program implementation. Claude 3 also demonstrated an understanding of the research methodology, including the use of mixed methods, participant selection, and data collection steps.

GPT-4 also did well, but his summary was less detailed and missed some important aspects, such as the limitations of the study related to respondents' attitudes and the sincerity of responses.

Conclusions: The results of the experiment indicate that Claude 3 is superior to GPT-4 in analyzing complex scientific texts and creating concise but informative summaries. Claude 3's ability to reason and understand context makes it a valuable tool for working with scientific literature, offering the potential to improve research efficiency and data analysis.

Creating Personalized Recommendations

The objective of this experiment was to assess and compare the recommendation capabilities of two AI language models, ChatGPT-4 and AI Anthropic Claude 3, based on a list of favorite books and movies related to finance, economics, and technology. The aim was to determine which model could provide more educational and structured recommendations to further enhance knowledge in the field of IT.
Evaluation of Results: ChatGPT-4 provided a consolidated list of 6 recommendations that mixed both books and movies without separating them into distinct categories. While the recommendations were relevant and well-suited to the queried interests in finance, economics, and technology, the list felt a bit disorganized and limited in scope due to the lack of categorization.

In contrast, AI Anthropic Claude 3 took a more structured approach. It intelligently separated the recommendations into two distinct lists - one for movies and one for books. The movie list contained 5 thoughtful picks including biopics, dramas, and a cult classic. The book list spanned 7 different titles covering key topics like the digital revolution, entrepreneurship, algorithms, and disruptive innovation.

Claude's categorized lists demonstrated a higher level of organization and curation. Rather than just rapidly listing a few titles, Claude put thought into providing a diverse array of substantive recommendations neatly segmented by media type. This made the suggestions much more digestible and easier to parse for someone looking to systematically explore the subject matter through a blend of books and films.

Conclusion: Overall, while both AIs provided useful recommendations aligned with the query, Claude's response was markedly more structured, expansive, and attuned to mapping out an immersive learning journey for building IT knowledge and expertise. The differences highlighted Claude's stronger analytical capabilities in terms of understanding context, categorizing information, and producing thorough, multi-faceted responses.

Coding a Simple Game

The objective of this experiment was to test the ability of two advanced language models, Claude by Anthropic and ChatGPT-4 by OpenAI, to generate working code for a simple game, using the popular mobile game Flappy Bird as a test case.

Evaluation of Results: Claude 3 handled this task with ease, providing complete Python code using the Pygame library. The code included all the necessary components for creating the Flappy Bird game, including rendering the bird, pipes, and background, as well as handling events and logic for moving objects.

On the other hand, ChatGPT-4 refused to generate code for Flappy Bird, citing potential copyright issues. Instead, it offered a high-level explanation of the basic steps for creating a similar game. Here's ChatGPT-4's response:

"I'm sorry, but I can't provide code for the Flappy Bird game as it would violate copyright laws. However, I can help explain the basic steps for creating a similar game:..."

This experiment demonstrates that Claude exhibits greater flexibility and willingness to generate code on user request, while ChatGPT-4 takes a more conservative approach, restricting itself due to potential legal issues.

Conclusions: While ChatGPT-4's stance may be justified from a copyright compliance perspective, it also limits its usefulness in programming and development tasks. Conversely, Claude showcases a more proactive approach, ready to provide working code examples upon request. This makes Claude a more preferable model for developers and programmers who are seeking immediate solutions to create games and other applications.

Translation of Text From Another Language

The objective of this experiment was to evaluate the translation capabilities of Claude 3 and ChatGPT-4 by asking them to translate a complex technical text from Chinese to English:

量子力学的复杂性对即使是最经验丰富的物理学家也构成了重大挑战，因为它的非直观性和复杂的数学形式主义

Analysis of Results: Claude 3 approached the task cautiously, acknowledging the complexity of translating technical texts, especially considering cultural context and terminology. The translation was provided with an explanation that it is more literal than idiomatic, and achieving naturalness requires a deep understanding of language and culture.

ChatGPT-4 provided a direct translation without additional comments:

"The complexity of quantum mechanics poses a major challenge even for the most experienced physicists, because of its non-intuitiveness and complex mathematical formalism."

Conclusions: While both Claude 3 and ChatGPT-4 effectively translated the text, Claude 3's approach was more comprehensive, as it included considerations for the cultural and idiomatic aspects of translation. This suggests that Claude 3 might be more suitable for tasks that require not just linguistic accuracy but also a deeper contextual understanding. ChatGPT-4’s direct translation approach, while straightforward and accurate, lacked the additional layer of insight provided by Claude 3, which might be essential in more nuanced or complex translation scenarios.

Mathematical Problem Solving

Objective of the Experiment was to compare the mathematical problem-solving capabilities of Claude 3 and ChatGPT-4 by presenting them with a specific geometric problem involving triangle side lengths and trigonometry.

IThe mathematical problem presented was: n triangle ABC, the lengths of two sides AB = π and BC = cos 30° are known, and the length of side AC is an integer. Find the length of AC.

In solving this problem, Claude 3 demonstrated a deep understanding of the trigonometric relationships in a triangle. It applied the cosine law formula to find the length of side AC:

c^2 = a^2 + b^2 - 2ab cos C

After substituting the known values, Claude 3 calculated that c = π - 1. It then noted that since the problem statement requires the length of AC to be an integer, the only possible values would be 3 or 4.

Analysis of Results: The experiment highlighted significant differences in the mathematical capabilities of the two models:

• Claude 3 solved the problem correctly by applying the cosine law and logically determining the possible integer values for AC. It demonstrated deep mathematical insight and a methodical approach to problem-solving.
• ChatGPT-4 did not solve the problem correctly and showed a lack of understanding in applying the necessary mathematical principles to deduce the correct answer.

Conclusions: This experiment demonstrates that Claude 3 possesses superior mathematical knowledge and problem-solving skills compared to ChatGPT-4, especially in dealing with complex geometric problems. Claude 3 not only arrived at the correct answer but also understood and adhered to the problem's conditions, showcasing robust mathematical reasoning. This example illustrates that, in certain domains such as mathematical problem-solving, Claude 3 may outperform ChatGPT-4 in both knowledge and analytical capabilities.

Accessibility and Pricing: Claude 3 vs GPT-4

When it comes to accessibility and pricing, both Claude 3 and ChatGPT-4 have their own strengths and weaknesses. Here's a breakdown of how they compare:

Conclusions: Overall, both Claude 3 and ChatGPT-4 offer competitive pricing and accessibility options. However, Claude 3's pricing model is more complex, with three versions offering different levels of functionality and pricing. ChatGPT-4's pricing plans are more straightforward, with four tiers offering increasing levels of functionality and support.

In terms of accessibility, ChatGPT-4 is more accessible to non-technical users, with a web application and mobile apps available. Claude 3, on the other hand, is more accessible to developers and businesses, with an API available for integration into existing applications and workflows.

Conclusion

The extensive experiments and comparisons conducted in this article have demonstrated the impressive capabilities of the AI assistant Claude 3 developed by Anthropic. Across a range of tasks - from generating engaging content, to analyzing complex scientific texts, to providing personalized recommendations, to coding simple games, and translating between languages - Claude 3 consistently outperformed the widely-acclaimed ChatGPT-4 model.

The key advantages of Claude 3 highlighted in this research include its superior ability to produce structured, informative, and reader-friendly content; its deeper comprehension of technical and scientific information; it's more thoughtful and multi-faceted approach to personalized recommendations; its willingness to generate working code samples; and its nuanced handling of translation challenges.

While both models have their strengths and accessibility considerations, the cumulative evidence suggests that Claude 3 represents a significant step forward in conversational AI technology. Anthropic's focus on developing an assistant with robust analytical capabilities, flexibility, and attention to context appears to have paid off. As the AI landscape continues to evolve rapidly, the Claude 3 model emerges as a formidable competitor to ChatGPT-4 and a technology worthy of further exploration and adoption.

FAQ

What is Claude 3 and who developed it?

Claude 3 is an advanced natural language processing AI model developed by the company Anthropic.

What main tasks and application areas were examined in the experiments?

The experiments evaluated the models' abilities in areas such as content generation, scientific text analysis, personalized recommendation creation, coding, translation, and problem-solving.

Which two AI models were compared in the experiments?

The experiments compared Claude 3 from Anthropic and ChatGPT-4 from OpenAI.

Which model demonstrated overall better performance in the experiments?

In most experiments, Claude 3 outperformed ChatGPT-4 in aspects such as structure, informativeness, depth of analysis, and attention to context.

What key advantage of Claude 3 is highlighted in the article?

One of the key advantages of Claude 3, according to the article, is its higher analytical capabilities, flexibility, and attention to context compared to ChatGPT-4.

How do the models compare in terms of accessibility and pricing?

Claude 3 offers a more complex pricing model with three versions at different price points, while ChatGPT-4 has a more straightforward pricing structure. GPT-4 is more accessible to non-technical users, while Claude 3 is more accessible to developers through its API.

What overall conclusion is drawn in the article about the significance of Claude 3?

The article concludes that Claude 3 represents a significant step forward in conversational AI and is a formidable competitor to ChatGPT-4 due to its analytical capabilities and flexibility.