Claude 3 Opus vs GPT 4o: A Comprehensive Comparison

Claude 3 Opus and GPT-4 are two powerful language models that have been making waves in the tech industry. Claude 3 Opus has a more human-like approach to conversation, while GPT-4 is known for its ability to process and generate vast amounts of data.

GPT-4 is a large language model that was trained on a massive dataset of text from the internet. It has a 175 billion parameter count, which is significantly higher than Claude 3 Opus's 13 billion parameter count. This difference in size and complexity gives GPT-4 a significant edge in terms of processing power and data generation capabilities.

Claude 3 Opus is designed to be more conversational and user-friendly, with a focus on providing helpful and accurate answers to user queries. It has a more streamlined approach to conversation, making it easier for users to get the information they need quickly and efficiently.

A different take: Claude Ai vs Gpt 4

Claude 3 Opus is relatively expensive, especially when it comes to output tokens.

The output tokens in particular are 150% more expensive than GPT-4 Turbo's.

GPT-4 Turbo is the more economical choice, especially for use cases requiring a large number of output tokens.

Here's a comparison of the two models:

This comparison highlights the significant price difference between the two models, making GPT-4 Turbo a more cost-effective option for many use cases.

Claude 3 Opus has a throughput of approximately 23 tokens/second, which is a significant improvement over its predecessor, but still lags behind GPT-4o's throughput of ~109 tokens/second when it was first launched.

One key area where GPT-4o excels is in reasoning tasks, outperforming Claude 3.5 Sonnet with 69% accuracy compared to 44%.

Here are some key differences in pricing between Claude 3.5 Sonnet and GPT-4o:

Claude 3 Sonnet is priced 95% lower for input tokens and 87.5% lower for output tokens compared to GPT-4.

Classification is a crucial task where AI models are put to the test. Claude 3.5 Sonnet has an accuracy of 0.72, which is better than GPT-4o's 0.65, but GPT-4 has the highest mean absolute score of 0.77.

Curious to learn more? Check out: Claude 3 Opus for Corporate Finance vs Gpt 4 Turbo

Claude 3.5 Sonnet has 5 specific cases where it performs worse than GPT-4o, but these issues are not significant. This shows that more research and working with the prompt is needed to eliminate these regressions. GPT-4, on the other hand, had the most improvements compared to GPT-4o.

In terms of precision, recall, and F1 score, GPT-4o has the highest precision at 86.21%, indicating it is the best at avoiding false positives. This means when GPT-4o classifies a ticket as resolved, it is more likely to be accurate, thus reducing the chance of incorrectly marking unresolved tickets as resolved.

Claude 3.5 Sonnet is climbing the ranks for precision, with 85%, and is a good alternative to GPT-4o. Here's a comparison of the models' precision, recall, and F1 score:

Overall, GPT-4o has the highest precision across the board, making it a reliable choice for classification tasks.

GPT-4o outperformed Claude 3.5 Sonnet on 5 of the 14 fields in the data extraction task, with similar performance on 7 fields and degraded performance on 2 fields.

The contracts used in the evaluation varied in length, ranging from 5 pages to over 50 pages. This made the task even more challenging for the models.

GPT-4o only identified 60-80% of the data correctly in most fields, which indicates that both models fall short of the mark for this complex data extraction task.

Claude 3.5 Sonnet, on the other hand, accurately extracted all information from financial reports, even the most complicated parts of the chart, in Hanane D.'s multi-modal evaluations.

Here's a summary of the comparison points:

Both models showed room for improvement, especially in terms of accuracy, which highlights the need for advanced prompting techniques like few-shot or chain of thought prompting.

Latency is a critical factor to consider when evaluating AI models. Claude 3.5 Sonnet is 2x faster than Claude 3 Opus.

Claude 3.5 Sonnet still lags behind GPT-4o in terms of latency.

The difference in latency between these models can impact the overall user experience.

A fresh viewpoint: Generative vs Discriminative Models

The throughput of a model is a key performance indicator that measures how many tokens it can output per second. The throughput for Claude 3.5 Sonnet has improved by approximately 3.43x from Claude 3 Opus, which generated 23 tokens/second.

GPT-4o had a throughput of around 109 tokens/second when it was launched a month ago.

GPT-4 Turbo is a cost-effective option for those who need a large number of output tokens. It's a more economical choice, especially for use cases requiring a large number of output tokens.

One of the key advantages of GPT-4 Turbo is its pricing, which is significantly lower than Claude 3 Opus. For example, 1000 output tokens on GPT-4 Turbo cost $0.03, compared to $0.075 on Claude 3 Opus.

GPT-4 Turbo also offers a lower cost per input token, at $0.01 compared to Claude 3 Opus's $0.015. This makes it a more affordable option for users who need to process a large number of input tokens.

Here's a comparison of the pricing for GPT-4 Turbo and Claude 3 Opus:

Claude 3 Opus has a strong foundation in conversational AI, with capabilities that allow it to understand and respond to a wide range of topics and questions.

Claude 3 Opus can generate human-like responses, making it a valuable tool for customer service, content creation, and more.

Its ability to learn from user interactions and adapt to new information enables Claude 3 Opus to stay up-to-date and provide accurate answers.

Claude 3 Opus also excels at handling complex tasks, such as writing articles, creating dialogue, and even developing chatbots.

Claude 3.5 Sonnet and GPT-4o were put to the test for their data extraction capabilities, specifically extracting key pieces of information from Master Services Agreements (MSAs).

The contracts used in the evaluation varied in length, with some as short as 5 pages and others longer than 50 pages. The goal was to extract 12 specific fields, including Contract Title, Name of Customer, and details of Termination Clause.

GPT-4o outperformed Claude 3.5 Sonnet on 5 of the 14 fields, and maintained similar performance on 7 fields, but showed degraded performance on 2 fields. Both models struggled to accurately extract data, with only 60-80% of data being correctly identified in most fields.

Interestingly, Hanane D. ran her own evaluation using Claude 3.5 Sonnet to extract data from financial reports, and found that it accurately extracted all information, even the most complicated parts of the chart. This suggests that Claude 3.5 Sonnet may have an advantage over GPT-4o when it comes to data extraction with images.

Here's a summary of the evaluation results:

Claude 3 Opus excels in Multilingual Math scores, coming in second with 90.7%, just behind Claude 3.5 Sonnet's impressive 91.6%.

Claude 3 Opus performs well in Reasoning Over Text, but falls short of Claude 3.5 Sonnet's 87.1% score, coming in second with a score of 83.5%.

In comparison to other models, Claude 3 Opus shows strong performance in Graduate Level Reasoning, although it's worth noting that Claude 3.5 Sonnet takes the top spot in this area.

Here's a comparison of Claude 3 Opus's performance in various benchmarks:

GPT-4 is a powerful model that has been compared to Claude 3.5 Sonnet in various benchmarks.

GPT-4o, a variant of GPT-4, excels in Graduate Level Reasoning, Undergraduate Level Knowledge, and Code, coming in second only to Claude 3.5 Sonnet.

GPT-4o also performs well in Reasoning Over Text, but falls behind Claude 3.5 Sonnet with a score of 83.5%.

In contrast, Claude 3.5 Sonnet outperforms GPT-4o in Multilingual Math, with a score of 91.6%, making it a strong contender in this area.

Here's a comparison of GPT-4o and Claude 3.5 Sonnet's performance in various benchmarks:

Claude 3 Opus and GPT-4 Turbo are two prominent AI models, each with its own strengths and weaknesses. Claude 3 Opus was designed to prioritize humanity and ethics above all, making it a more trustworthy and safe option.

Anthropic's mission in developing Claude was to create an AI model that acts more in line with what users want. This focus on ethics resulted in a system with better safety measures.

Claude 3 Opus and GPT-4 Turbo have been compared through various benchmarking tests. Here's a summary of their performance:

These benchmarks show that Claude 3 Opus and GPT-4 Turbo have different strengths and weaknesses, and which one performs better depends on the specific task at hand.

Pricing can be a significant factor when choosing a language model. GPT-4 Turbo costs $10 per one million input tokens and $30 per one million output tokens.

Claude 3 Opus has a higher input token pricing of $15 per one million input tokens, which is 1.5 times more expensive than GPT-4 Turbo. Google's Gemini 1.5 Pro, on the other hand, is priced at $7 per one million input tokens, making it a more affordable option.

For personal use, Claude 3 Opus costs $20 per month, while GPT-4 Turbo and Gemini 1.5 Pro do not have a monthly subscription fee mentioned in the pricing comparison. Google's Gemini 1.5 Pro has preview pricing starting May 2, 2024, at $7 per one million input tokens and $21 per one million output tokens.

Worth a look: Google Claude 3

Claude is a chat product designed as a response to ChatGPT's drawbacks.

It was created by a company called Anthropic and runs on their own proprietary LLM, which is also called Claude.

Anthropic's mission in developing Claude was to create an AI model that prioritizes humanity and ethics above all.

They wanted to make AI more predictable, creating a system with better safety measures, so it acts more in line with what users want.

Anthropic focused on the ethical side of AI to build a tool that is not only as good as others but also better at being trustworthy, safe, and more aligned with the human experience of conversation.

Claude has a different approach than ChatGPT, with a focus on high EQ rather than high IQ.

This means Claude is designed to be more empathetic and understanding in its interactions.

For more insights, see: Claude Ai Pro vs Chatgpt 4

AI benchmarks are a crucial aspect of evaluating the capabilities of different AI models. Companies can manipulate data or use prompt engineering techniques to achieve target benchmark points, which directly proves Goodhart's Law.

GPT-4 Turbo, Claude 3 Opus, and Gemini 1.5 Pro are among the models that have been put through various benchmarking tests. Here's a summary of their performance:

It's worth noting that these benchmarks are not without their limitations, as they can be manipulated by companies to achieve desired results.

Claude 3 Opus and GPT-4 are both impressive language models, but how do they stack up against other models in the field? The Google BERT model, for example, is known for its ability to process complex sentences and understand nuances in language.

In terms of performance, the Claude 3 Opus model has shown to be faster than GPT-4 in certain tasks, with a response time of 100 milliseconds compared to GPT-4's 150 milliseconds. This is likely due to Claude 3 Opus's more streamlined architecture.

The Microsoft Turing-NLG model, on the other hand, is known for its ability to generate coherent and engaging text, but it falls short in terms of creativity and originality compared to GPT-4. GPT-4's ability to generate human-like text has been a major selling point for the model.

In comparison, the Claude 3 Opus model is more geared towards practical applications, such as customer service chatbots and language translation, where accuracy and efficiency are key.

For another approach, see: Claude Ai Models Ranked