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Artificial Intelligence (AI) has made significant strides in recent years, with language models like Claude Models and GPT-4 leading the way. This blog post provides a comprehensive comparison of these two models, discussing their accuracy benchmarks, pricing, outputs, pros, and cons.
Accuracy Benchmarks
Claude Models
Claude Models, particularly Claude 3, have shown impressive performance in various benchmarks. In logical reasoning tests requiring deductive, inductive, and abductive inference, Claude scores around 70% accuracy. However, it’s worth noting that this lags behind human expert performance of 85-92% on comparable test suites.
GPT-4
GPT-4 exhibits human-level performance on various professional and academic benchmarks. For instance, it passes a simulated bar exam with a score around the top 10% of test takers. It also displays sizable jumps in its reasoning and comprehension capabilities, with 40% higher scores on high school math and science exams, 90% reading comprehension accuracy summarizing books and articles, and 80-90% accuracy answering advanced science questions.
Pricing
Claude Models
Claude Models offer different pricing for their models. For instance, Claude 3 Opus, their most powerful model, is priced at $15 per million tokens for input and $75 per million tokens for output. They also offer a cheaper model, Claude Instant, priced at $0.25 per million tokens for input and $1.25 per million tokens for output.
GPT-4
OpenAI offers different pricing for GPT-4. For instance, for models with 128k context lengths, the price is $0.01 per 1k prompt tokens. They also offer a subscription service, ChatGPT Plus, which costs $20 a month and provides access to GPT-4.
Strong Areas
Claude Models
Claude Models excel at open-ended conversation, collaboration on ideas, coding tasks, and working with text. They also offer advanced vision capabilities, allowing you to process and analyze visual input such as charts, graphs, and photos.
GPT-4
GPT-4 leverages its advanced AI language model to generate human-like responses on a variety of topics. It’s an invaluable asset for conversation, providing answers, creating text, and more.
Context Length
Claude Models
Claude Models offer excellent long document capabilities and can process up to 200,000 pieces of information.
GPT-4
GPT-4 offers enhanced creativity and time-saving benefits. However, it is significantly more expensive than its predecessor, GPT-3, and requires large amounts of data and compute, which can be cost-prohibitive for some. GPT-4 has a maximum content length of 32k.
Summary
The Verdict
After testing the Claude 3 Opus model for a day, it seems like a capable model but falters on tasks where you expect it to excel. In our commonsense reasoning tests, the Opus model doesn’t perform well, and it’s behind GPT-4 and Gemini 1.5 Pro. Except for following user instructions, it doesn’t do well in NIAH (supposed to be its strong suit) and maths.
Also, keep in mind that Anthropic has compared the benchmark score of Claude 3 Opus with GPT-4’s initial reported score, when it was first released in March 2023. When compared with the latest benchmark scores of GPT-4, Claude 3 Opus loses to GPT-4, as pointed out by Tolga Bilge on X.
That said, Claude 3 Opus has its own strengths. A user on X reported that Claude 3 Opus was able to translate from Russian to Circassian (a rare language spoken by very few) with just a database of translation pairs. Kevin Fischer further shared that Claude 3 understood nuances of PhD-level quantum physics. Another user demonstrated that Claude 3 Opus learns self types annotation in one shot, better than GPT-4.
So beyond benchmark and tricky questions, there are specialized areas where Claude 3 can perform better. So go ahead, check out the Claude 3 Opus model and see whether it fits your workflow.
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Take the first step towards this exciting journey by booking a free demo call with us today. Let’s explore the possibilities together and unlock the full potential of AI for your organization. Remember, the future belongs to those who prepare for it today.
| Decision points | Open-Source LLM | Close-Source LLM |
|---|---|---|
| Accessibility | The code behind the LLM is freely available for anyone to inspect, modify, and use. This fosters collaboration and innovation. | The underlying code is proprietary and not accessible to the public. Users rely on the terms and conditions set by the developer. |
| Customization | LLMs can be customized and adapted for specific tasks or applications. Developers can fine-tune the models and experiment with new techniques. | Customization options are typically limited. Users might have some options to adjust parameters, but are restricted to the functionalities provided by the developer. |
| Community & Development | Benefit from a thriving community of developers and researchers who contribute to improvements, bug fixes, and feature enhancements. | Development is controlled by the owning company, with limited external contributions. |
| Support | Support may come from the community, but users may need to rely on in-house expertise for troubleshooting and maintenance. | Typically comes with dedicated support from the developer, offering professional assistance and guidance. |
| Cost | Generally free to use, with minimal costs for running the model on your own infrastructure, & may require investment in technical expertise for customization and maintenance. | May involve licensing fees, pay-per-use models or require cloud-based access with associated costs. |
| Transparency & Bias | Greater transparency as the training data and methods are open to scrutiny, potentially reducing bias. | Limited transparency makes it harder to identify and address potential biases within the model. |
| IP | Code and potentially training data are publicly accessible, can be used as a foundation for building new models. | Code and training data are considered trade secrets, no external contributions |
| Security | Training data might be accessible, raising privacy concerns if it contains sensitive information & Security relies on the community | The codebase is not publicly accessible, control over the training data and stricter privacy measures & Security depends on the vendor's commitment |
| Scalability | Users might need to invest in their own infrastructure to train and run very large models & require leveraging community experts resources | Companies often have access to significant resources for training and scaling their models and can be offered as cloud-based services |
| Deployment & Integration Complexity | Offers greater flexibility for customization and integration into specific workflows but often requires more technical knowledge | Typically designed for ease of deployment and integration with minimal technical setup. Customization options might be limited to functionalities offered by the vendor. |
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