Claude 3 is the latest family of large language models (LLMs) developed by AI startup Anthropic. This new suite of AI models includes three versions: Claude 3 Opus, Claude 3 Sonnet, and the unreleased Claude 3 Haiku. Anthropic claims that Claude 3 not only outperforms its predecessor, Claude 2.1, but also rivals the capabilities of OpenAI’s GPT-4 and Google’s Gemini Ultra.
GPT-4, on the other hand, is the most recent iteration of OpenAI’s Generative Pre-trained Transformer series. As a multimodal model, GPT-4 can process both text and images, although the image input capability is not yet publicly available. The exact number of parameters in GPT-4 remains undisclosed, but it is rumored to have more than 1 trillion parameters, significantly more than its predecessor, GPT-3, which has 175 billion parameters.
Both Claude 3 and GPT-4 represent the cutting edge of AI technology, pushing the boundaries of what is possible with large language models. As the AI arms race continues to heat up, these models are set to revolutionize various industries and change the way we interact with technology.
Benchmark Performance Comparison
Based on the benchmark results shared by Anthropic, Claude 3 Opus outperforms GPT-4 across several key metrics. In the GPQA benchmark, which measures graduate-level expert reasoning, Claude 3 Opus achieves an impressive score of 50.4%, surpassing GPT-4’s 35.7% by a significant margin of 14.7 percentage points. Claude 3 Opus also excels in basic math tests, scoring 95% compared to GPT-4’s 92%, and slightly edges out GPT-4 in the MMLU knowledge benchmark with a score of 86.8% versus GPT-4’s 86.4%.
However, it is important to note that the benchmark results may not paint a complete picture. As pointed out by Lukas Finnveden, Anthropic’s blog post included a footnote caveating their numbers. When comparing the performance of Claude 3 Opus with the more recent GPT-4-1106-preview model, GPT-4 still seems to have the upper hand in most benchmarks:
| Benchmark | Claude 3 Opus | GPT-4-1106-preview |
|---|---|---|
| GSM8K | 95.0% | 95.3% |
| MATH | 60.1% | – |
| HumanEval | 84.9% | – |
| Big Bench Hard | 86.8% | – |
| DROP (F1) | 93.1 | – |
| HellaSwag | 95.4% | – |
While Claude 3 Opus demonstrates impressive performance, it may not be pushing the frontier of LLM development as claimed by Anthropic. The NYT Connections benchmark, created by an AI Alignment Forum user, also shows GPT-4 Turbo outperforming Claude 3 Opus with scores of 31.0 and 27.3, respectively.
It is crucial to approach benchmark numbers with a degree of skepticism and consider the specific evaluation methods used by each company. As the AI landscape continues to evolve rapidly, with new breakthroughs happening almost daily, it is essential to keep a close eye on the latest developments and carefully assess the capabilities of each model based on a wide range of benchmarks and real-world applications.
Knowledge and Reasoning Benchmarks
Claude 3 Opus showcases its advanced knowledge and reasoning capabilities through its performance on various benchmarks. In the MMLU benchmark, which tests undergraduate-level knowledge across a wide range of subjects, Claude 3 Opus achieves a score of 86.8%, narrowly surpassing GPT-4’s 86.4%. This suggests that Claude 3 Opus has a comprehensive understanding of diverse academic topics, rivaling that of a well-educated human.
The model also excels in the GSM8K benchmark, which evaluates grade school math problem-solving skills. Claude 3 Opus scores an impressive 95%, outperforming GPT-4’s 92%. This demonstrates the model’s ability to apply mathematical reasoning and solve complex problems with high accuracy.
However, when comparing Claude 3 Opus to the more recent GPT-4-1106-preview model, the latter seems to have a slight edge in the GSM8K benchmark, scoring 95.3%. This highlights the rapid pace of development in the AI industry, with models constantly improving and pushing the boundaries of performance.
In the GPQA benchmark, which measures graduate-level expert reasoning, Claude 3 Opus achieves a score of 50.4%, significantly outperforming GPT-4’s 35.7%. This suggests that Claude 3 Opus has a more advanced ability to analyze complex information and draw accurate conclusions, making it a valuable tool for tasks requiring expert-level reasoning.
While these benchmark results are impressive, it is essential to approach them with a degree of caution. The specific evaluation methods used by each company may vary, and the rapidly evolving nature of the AI landscape means that new breakthroughs are happening almost daily. As such, it is crucial to consider a wide range of benchmarks and real-world applications when assessing the capabilities of each model.
Mathematical and Coding Abilities
Claude 3 Opus demonstrates impressive mathematical and coding abilities, as evidenced by its performance on various benchmarks. In the MATH benchmark, which evaluates mathematical problem-solving skills, Claude 3 Opus achieves a score of 60.1%. While the exact score of GPT-4 on this benchmark is not provided, Claude 3 Opus’s performance suggests a strong ability to tackle complex mathematical problems.
When it comes to coding, Claude 3 Opus showcases its prowess in the HumanEval benchmark, scoring 84.9%. This benchmark assesses a model’s ability to generate code that meets specific requirements and passes a set of test cases. Claude 3 Opus’s high score indicates its proficiency in understanding and generating code, making it a valuable tool for developers and programmers.
Although the exact scores of GPT-4 on the MATH and HumanEval benchmarks are not available for comparison, it is clear that Claude 3 Opus possesses advanced mathematical and coding capabilities. These skills are highly sought after in various industries, from finance and engineering to software development and data analysis.
It is worth noting that while benchmark scores provide a useful indication of a model’s abilities, they should be considered alongside real-world applications and use cases. As AI technology continues to evolve at a rapid pace, it is essential to evaluate the performance of models like Claude 3 Opus and GPT-4 in practical settings to fully understand their potential impact and limitations.
Multimodal Capabilities
While both Claude 3 and GPT-4 are cutting-edge language models, one key difference between them is their multimodal capabilities. GPT-4 is a multimodal model, meaning it can process both text and images, although the image input capability is not yet publicly available. This allows GPT-4 to generate text based on visual prompts, such as photographs and diagrams, opening up a wide range of potential applications.
Some common use cases for GPT-4’s multimodality include image captioning, visual question answering, and even generating code from rough sketches of website designs. In a famous demo, Greg Brockman, president and co-founder of OpenAI, showed GPT-4 a photo of a hand-drawn sketch for a website, and the model produced the necessary code to build the site from scratch.
However, it is important to note that GPT-4’s multimodal capabilities are still limited. The model cannot generate images itself, and its ability to process images is currently only available through the API, not to ChatGPT Plus subscribers using OpenAI’s apps. Additionally, GPT-4 may struggle with tasks requiring precise localization or layouts, such as reading analog clock faces or describing the exact positions of chess pieces.
In contrast, Claude 3’s multimodal capabilities are not as well-documented. While the models can process a wide range of visual formats, including photos, charts, graphs, and technical diagrams, there is limited information available on their performance in tasks such as object detection, visual question answering, and optical character recognition (OCR).
It is also worth noting that Claude 3 has some limitations when it comes to vision tasks. The model cannot be used to identify people in images and may struggle with low-quality, rotated, or very small images. Additionally, Claude 3’s spatial reasoning abilities are limited, and it may not always provide precise counts of objects in an image.
As the AI landscape continues to evolve, it will be interesting to see how both GPT-4 and Claude 3 further develop their multimodal capabilities and address their current limitations. While GPT-4 seems to have a slight edge in this area, the rapid pace of development in the field means that new breakthroughs could emerge at any time, potentially reshaping the competitive landscape.
Speed and Cost Comparison
When it comes to speed and cost, Claude 3 and GPT-4 offer different options to cater to various user needs. Claude 3 Sonnet, the balanced model in the Claude 3 family, is two times faster than its predecessors, Claude 2 and Claude 2.1, for the vast majority of workloads. This significant speed improvement allows for near-instant responsiveness, making it an ideal choice for applications that require quick turnaround times.
On the other hand, GPT-4 has recently introduced GPT-4 Turbo, a more powerful and cost-effective version of the model. GPT-4 Turbo boasts a 128K context window, equivalent to more than 300 pages of text in a single prompt, allowing for more complex and nuanced tasks. Additionally, GPT-4 Turbo is three times cheaper than earlier versions of GPT-4, with input costing $0.01 per 1,000 tokens and output costing $0.03 per 1,000 tokens.
To put this into perspective, using the GPT-4 API costs:
| Model | Prompt Cost per 1K Tokens | Completion Cost per 1K Tokens |
|---|---|---|
| GPT-4 | $0.03 | $0.06 |
| GPT-4-32K | $0.06 | $0.12 |
| GPT-4 Turbo | $0.01 | $0.03 |
| GPT-3.5 Turbo | $0.002 | $0.002 |
Compared to the ChatGPT API, which uses the GPT-3.5 Turbo model, GPT-4 Turbo is still more expensive. Prompts with GPT-4 Turbo are 14 times more costly than the ChatGPT API, while completions are 29 times more expensive. However, the value proposition of GPT-4 Turbo depends on the specific use case. For applications requiring legal expertise or general education and tutoring, GPT-4 Turbo may be worth the additional cost.
It is important to note that while the ChatGPT Plus subscription, priced at $20 per month, includes access to GPT-4, it is currently capped at 100 messages every 4 hours. In comparison, for the same $20, users can process approximately 444,000 tokens (or 333,000 words) with the GPT-4 API, assuming an equal distribution of prompt and completion tokens.
As for Claude 3, Anthropic has not yet released detailed pricing information for its API. However, the company has made the API generally available, allowing developers to integrate the powerful models into their applications immediately. The Sonnet model powers the free Claude experience at claude.ai, while Opus is available as part of the Claude Pro subscription.
In conclusion, both Claude 3 and GPT-4 offer compelling speed and cost options for users with different requirements. While GPT-4 Turbo provides a more affordable and context-rich alternative to earlier GPT-4 versions, it remains more expensive than the ChatGPT API. Claude 3 Sonnet’s impressive speed improvements make it an attractive choice for applications demanding fast response times, but the lack of transparent pricing information makes it difficult to directly compare costs with GPT-4. As the AI landscape continues to evolve, users will need to carefully evaluate their specific needs and budgets to determine which model best suits their requirements.
Strengths and Weaknesses
Claude 3 and GPT-4 both have their own unique strengths and weaknesses that set them apart in the rapidly evolving world of AI language models. Claude 3 Opus showcases its advanced knowledge and reasoning capabilities through its impressive performance on various benchmarks. In the MMLU benchmark, which tests undergraduate-level knowledge across a wide range of subjects, Claude 3 Opus achieves a score of 86.8%, narrowly surpassing GPT-4’s 86.4%. This suggests that Claude 3 Opus has a comprehensive understanding of diverse academic topics, rivaling that of a well-educated human.
Claude 3 Opus also excels in mathematical and coding abilities, as evidenced by its performance on the MATH and HumanEval benchmarks. These skills are highly sought after in various industries, from finance and engineering to software development and data analysis. Additionally, Claude 3 Sonnet, the balanced model in the Claude 3 family, boasts a significant speed improvement, allowing for near-instant responsiveness and making it an ideal choice for applications that require quick turnaround times.
However, Claude 3’s multimodal capabilities are not as well-documented as GPT-4’s. While the models can process a wide range of visual formats, there is limited information available on their performance in tasks such as object detection, visual question answering, and optical character recognition (OCR). Claude 3 also has some limitations when it comes to vision tasks, such as identifying people in images and struggling with low-quality, rotated, or very small images.
On the other hand, GPT-4’s multimodal capabilities allow it to generate text based on visual prompts, opening up a wide range of potential applications. GPT-4 can perform tasks such as image captioning, visual question answering, and even generating code from rough sketches of website designs. However, GPT-4’s multimodal capabilities are still limited, as the model cannot generate images itself, and its ability to process images is currently only available through the API.
GPT-4 has also introduced GPT-4 Turbo, a more powerful and cost-effective version of the model with a 128K context window, allowing for more complex and nuanced tasks. While GPT-4 Turbo is still more expensive than the ChatGPT API, it may be worth the additional cost for applications requiring legal expertise or general education and tutoring.
In terms of weaknesses, both Claude 3 and GPT-4 face challenges in ensuring factual accuracy and avoiding bias, especially on complex or controversial topics. This highlights the ongoing struggle to make AI models reliable and unbiased information sources. Additionally, while GPT-4 excels in many areas, it still has room for improvement in guaranteeing factual accuracy and unlocking its full creative potential.
It is essential to approach the capabilities of these models with a degree of caution, as the specific evaluation methods used by each company may vary, and the rapidly evolving nature of the AI landscape means that new breakthroughs are happening almost daily. As such, it is crucial to consider a wide range of benchmarks and real-world applications when assessing the strengths and weaknesses of each model.
In conclusion, both Claude 3 and GPT-4 have their own unique strengths and weaknesses, catering to different user needs and applications. While Claude 3 Opus demonstrates impressive knowledge, reasoning, and mathematical abilities, GPT-4 has a slight edge in multimodal capabilities and offers a more cost-effective option with GPT-4 Turbo. As the AI arms race continues to heat up, it will be fascinating to see how these models further develop and address their current limitations, ultimately reshaping the competitive landscape and revolutionizing various industries.
Conclusion: Is Claude 3 Better Than GPT-4?
After carefully examining the capabilities, strengths, and weaknesses of both Claude 3 and GPT-4, it is clear that both models have their own unique advantages and are pushing the boundaries of what is possible with AI technology. While Claude 3 Opus demonstrates impressive performance on various benchmarks, particularly in knowledge, reasoning, and mathematical abilities, GPT-4 still seems to have a slight edge in most areas when comparing the more recent GPT-4-1106-preview model.
GPT-4’s multimodal capabilities, allowing it to process both text and images, open up a wide range of potential applications and give it a distinct advantage over Claude 3. Additionally, the introduction of GPT-4 Turbo offers a more powerful and cost-effective option for users who require complex and nuanced tasks, such as legal expertise or general education and tutoring.
However, it is essential to note that the AI landscape is evolving at a rapid pace, with new breakthroughs happening almost daily. While GPT-4 currently appears to be the more advanced model overall, Claude 3’s impressive performance and the ongoing development efforts at Anthropic suggest that the gap between the two models may narrow in the near future.
Ultimately, the choice between Claude 3 and GPT-4 will depend on the specific needs and requirements of each user or application. For tasks that prioritize knowledge, reasoning, and mathematical abilities, Claude 3 Opus may be the preferred choice. On the other hand, applications that require multimodal processing, complex context understanding, or cost-effective solutions may find GPT-4 and its variants more suitable.
As the AI arms race continues to heat up, it will be fascinating to see how both Claude 3 and GPT-4 further develop and address their current limitations. While GPT-4 may currently have a slight advantage, the rapid advancements in AI technology mean that the competitive landscape could shift at any moment. As such, it is crucial for users and developers to stay informed about the latest developments and carefully evaluate the capabilities of each model based on their specific needs and real-world applications.