Claude 3.5 Sonnet : Anthropic’s Smartest, Fastest, and Most Personable Model

Santhosh Reddy Dandavolu Last Updated : 21 Jun, 2024
4 min read

Introduction

The article presents Anthropic’s latest Generative AI large language model, Claude 3.5 Sonnet, which is highly proficient at arithmetic, reasoning, coding, and multilingual activities. It also covers its vision capabilities, real-world uses, security precautions, and prospects going forward with models like Haiku and Opus. The article emphasizes Claude 3.5 Sonnet’s important contribution to the development of AI.

Overview

  • Understand how Anthropic’s Claude 3.5 Sonnet improves performance in reasoning, math, coding, and multilingual tasks.
  • Explore Claude 3.5 Sonnet’s capabilities in visual reasoning and text transcription from images.
  • Learn practical uses of Claude 3.5 Sonnet in tools like APIs for natural language processing and data extraction.
  • Discover safety measures in Claude 3.5 Sonnet ensuring privacy and ASL-2 compliance.
  • Anticipate future Claude models like Haiku and Opus, and enhancements in memory and new modalities.
Claude 3.5 Sonnet

What is Claude 3.5 Sonnet?

In March 2024, Anthropic introduced its Claude 3 family of models setting a new standard for performance and cost-effectiveness. GPT-4o and Gemini 1.5 Pro surpassed Claude 3 within a few months in both arenas. Now, it’s time for Anthropic to make a comeback with its Claude 3.5 Sonnet which is the best model on both performance and cost-effectiveness.

Claude 3.5 Sonnet

As we can see from the above image, the Claude 3.5 Sonnet has the best quality and is less costly than the previously best-performing GPT-4o model.

Reasoning and Question Answering

It sets new benchmarks for most of the industry-standard metrics covering reasoning, reading comprehension, math, science, and coding. 

  • GPQA (Graduate Level Q&A): Claude 3.5 Sonnet leads with 59.4% (0-shot) and 67.2% (5-shot), outperforming others.
  • MMLU (General Reasoning): It scores highest at 90.4% (5-shot), showing superior reasoning abilities.
  • MATH (Mathematical Problem Solving): Claude 3.5 Sonnet achieves 71.1% (0-shot), higher than previous models.
  • HumanEval (Python Coding): It excels with a 92.0% score, indicating strong coding proficiency.
  • MGSM (Multilingual Math): The model scores 91.6% (0-shot), leading in multilingual math.
  • DROP (Reading Comprehension): It achieves 87.1% (F1 Score, 3-shot), showing strong comprehension skills.
  • BIG-Bench Hard (Mixed Evaluations): It scores 93.1% (3-shot), indicating robust mixed task performance.
  • GSM8K (Grade School Math): Claude 3.5 Sonnet leads with 96.4% (0-shot), demonstrating excellent math problem-solving skills.
Claude 3.5 Sonnet

Vision Capabilities

Claude 3.5 Sonnet is the most powerful vision model on standard vision benchmarks. It excels in visual reasoning tasks, such as interpreting charts and graphs, and accurately transcribes text from imperfect images.

Claude 3.5 Sonnet

Tools and Agents

It can use external tools depending on the task at hand, and perform various tasks like returning API calls with natural language requests, extracting structured data, answering questions by searching databases, etc. We can even learn from Anthropic courses on GitHub itself about how to integrate tools.

Artifacts

Anthropic launched a new feature that revolutionizes user interaction with Claude. When users request content like code snippets, text documents, or website designs, these Artifacts now appear in a dedicated window alongside their conversation. This enhancement not only improves usability but also sets a new standard for interactive AI features.

Now let’s test the model’s vision capabilities with artifacts.

Claude 3.5 Sonnet

Here, we have given the ‘quality vs price’ chart taken from the above to the model and asked it “Which model is most cost-effective based on this chart?”

As we can see from the image, it answers the question correctly.

Then, we asked, “How can I make such a chart in Python?”. The model generated the code and displayed it on the side. 

We can enable the artifact feature in ‘feature preview’ if it is not already enabled.

And Claude 3.5 Sonnet can also recognize that the chart is showing it is the best-performing model.

How to Use?

Claude 3.5 Sonnet is the default model in Claude.ai chat. In the free version, there are limits on the number of messages per day which can vary depending on the traffic. If we can upgrade to Pro, we can also get access to Claude 3 Haiku and Opus models.

We can also access the model through Anthropic API. It costs $3 / 1 Million tokens, and $15 / 1 Million tokens for input and output respectively.

Safety and Privacy

All models undergo extensive testing to minimize misuse. Despite its leap in intelligence, Claude 3.5 Sonnet maintains an ASL-2 safety level, verified through rigorous red teaming assessments. All current LLMs appear to be ASL-2.

Claude 3.5 Sonnet was evaluated by the UK’s Artificial Intelligence Safety Institute, before deployment, with results shared with the US AI Safety Institute.

Feedback from policy experts and organizations like Thorn has been integrated to address emerging misuse trends. These insights have helped refine classifiers and improve model resilience against various abuses.

This model does not use user-submitted data for training generative models unless explicitly permitted by the user, ensuring robust protection of user privacy.

Conclusion

Like the Claude 3 family, Haiku and Opus models will be released soon. In addition to that features like memory, and new modalities are likely to be added. And of course, expect new models from OpenAI and Google as competition heats up.

Frequently Asked Questions

Q1. What is Claude 3.5 Sonnet?

A. It is Anthropic’s latest AI model, excelling in arithmetic, reasoning, coding, and multilingual tasks.

Q2. How does Claude 3.5 Sonnet perform in benchmarks?

A. It leads in various metrics such as GPQA, MMLU, MATH, HumanEval, MGSM, DROP, BIG-Bench Hard, and GSM8K.

Q3. What are its vision capabilities?

A. It Excels in visual reasoning, interpreting charts and graphs, and transcribing text from imperfect images.

I am working as an Associate Data Scientist at Analytics Vidhya, a platform dedicated to building the Data Science ecosystem. My interests lie in the fields of Natural Language Processing (NLP), Deep Learning, and AI Agents.

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