Claude 3.5 Sonnet: Anthropic’s Latest Conversational AI Model

The landscape of large language models (LLMs) is constantly evolving, with companies vying to deliver the most intelligent, versatile, and cost-effective solutions. Anthropic, a key player in this space, has just released Claude 3.5 Sonnet, the first model in its new Claude 3.5 family. This release promises significant improvements in reasoning, coding, and content creation while boasting twice the speed and five times lower cost compared to its predecessor, Claude 3 Opus. But what truly sets it apart? The answer lies in its groundbreaking “Artifacts” feature, alongside a host of other advancements. But… Is it enough to establish itself among a thousand rival models, particularly OpenAI’s GPT-4o?

Eh, eh, continue to read to discover it!

1. The Claude Model Family: A Symphony of AI
2. Claude 3.5 Sonnet: Faster, Cheaper, Smarter
3. Sonnet 3.5 vs Opus 3: a Clear Upgrade
4. Benchmark Battle: Claude 3.5 Sonnet vs GPT-4o
5. What are Artifacts and how do I use them?
6. Refusals and Safety for a Responsible AI Development
7. Conclusion

The Claude Model Family: A Symphony of AI

Before diving into the specifics of Claude 3.5 Sonnet, let’s take a look at the broader Claude model family: what we can see is that Anthropic’s approach involves creating distinct models tailored for different use cases.

Model	Focus	Ideal Use Cases
Claude 3 Haiku	Ultra-fast execution of simple tasks	Quick responses, swift data retrieval
Claude 3 Sonnet	Advanced reasoning and moderately complex tasks	Detailed customer inquiries, intricate data analysis
Claude 3 Opus	Handling extensive, multi-step tasks with precision	Higher-order mathematics, sophisticated coding, precise vision analysis

This multi-tiered approach ensures that users can select the model that best suits their needs and budget, from rapid data retrieval to intricate problem-solving.

Claude 3.5 Sonnet: Faster, Cheaper, Smarter

Thanks to its architectural optimizations, Claude 3.5 Sonnet is positioned as Anthropic’s new frontier in conversational AI.

Here’s what sets it apart:

• Double the Speed: Operates twice as fast as Claude 3 Opus, making it ideal for time-sensitive applications like customer support and real-time workflow management.
• Cost-Effective: Significantly cheaper than Claude 3 Opus, offering greater value for users without sacrificing performance.
• Enhanced Reasoning and Coding: Showcases impressive advancements in reasoning and coding proficiency, surpassing Claude 3 Opus in solving coding problems. This is particularly evident in its high score on the HumanEval benchmark, which measures a model’s ability to generate functioning code from natural language descriptions.
• Natural Language Proficiency: Excels at writing high-quality content with a natural, relatable tone, making it ideal for tasks like creative writing and content generation.
• Improved Visual Processing: Claude 3.5 Sonnet demonstrates state-of-the-art performance in various visual tasks, including:
  • MathVista: Solving mathematical problems presented visually.
  • ChartQA: Answering questions based on charts and graphs.
  • DocVQA: Understanding documents and extracting information.
  • AI2D: Answering questions about science diagrams.

Sonnet 3.5 vs Opus 3: a Clear Upgrade

So, we can say that the release of Claude 3.5 Sonnet marks a significant milestone in Anthropic’s model development, demonstrating noticeable improvements over its predecessor, Claude 3 Opus in several benchmarks. Also, this upgrade is evident in other areas, like the following ones…

Agentic Coding Evaluation:

Anthropic’s internal “Agentic Coding” evaluation provides a unique perspective on a model’s ability to perform real-world coding tasks. How it works?

Each model is presented with a natural language description of a desired improvement to an open-source codebase. It then has to understand the codebase, implement the change, and ensure all tests pass.

The Results? Claude 3.5 Sonnet achieved a remarkable 64% success rate, significantly surpassing Claude 3 Opus (38%) and highlighting a substantial leap in agentic coding capabilities.

Needle in a Haystack:

This evaluation measures a model’s ability to retrieve specific information from a large body of text (up to 200,000 tokens, the context window of Sonnet).

The result is that both Claude 3.5 Sonnet and Claude 3 Opus achieved near-perfect recall, showcasing their impressive ability to retain and retrieve information from long contexts.

Human Feedback Evaluations:

Anthropic also conducts human feedback evaluations, where raters directly compare models on various tasks. What emerges is…

• Significant Improvements: Claude 3.5 Sonnet demonstrated substantial improvements across core capabilities like coding, document processing, creative writing, and vision, as evidenced by its high win rates against Claude 3 Opus.
• Domain Expertise: Sonnet excelled in specific domains like Law, Finance, and Philosophy, suggesting its potential as a valuable tool for professionals in those fields.

Benchmark Battle: Claude 3.5 Sonnet vs GPT-4o

While Anthropic’s benchmarks showcase Claude 3.5 Sonnet’s impressive capabilities, it’s crucial to compare it against other industry-leading models like GPT-4o.

Standard Benchmarks:

Benchmark	Claude 3.5 Sonnet	GPT-4o
Graduate Level Reasoning (GPQA) (0-shot CoT)	59.4%	53.6%
General Reasoning (MMLU) (0-shot CoT)	88.3%	88.7%
Mathematical Problem Solving (MATH) (0-shot CoT)	71.1%	76.6%
Python Coding (HumanEval) (0-shot)	92.0%	90.2%
Multilingual Math (MGSM) (0-shot CoT)	91.6%	90.5%
Reading Comprehension & Arithmetic (DROP) (F1 Score, 3-shot)	87.1%	83.4%

As the table above demonstrates, Claude 3.5 Sonnet outperforms GPT-4o in several key benchmarks, particularly in reasoning, knowledge, and coding.

It’s also important to remember that context matters! In fact, the performance can vary based on the number of “shots” (examples provided) and the use of “Chain-of-Thought” prompting (encouraging the model to explain its reasoning), indicating the importance of prompt engineering for achieving optimal results.

Real-World Task Evaluation:

To further assess the models’ performance, independent researchers conducted evaluations across three practical tasks:

• Data Extraction from Legal Contracts: Both models performed similarly, correctly identifying 60-80% of the data, but neither excelled. This indicates the need for advanced prompting techniques in complex data extraction tasks.
• Customer Ticket Classification: Claude 3.5 Sonnet achieved 72% accuracy, outperforming GPT-4o (65%). However, GPT-4o maintained a higher precision (86.21%), indicating greater accuracy in avoiding false positives, crucial for customer satisfaction.
• Verbal Reasoning on Math Riddles: GPT-4o led with 69% accuracy, excelling in calculations and antonym identification. Claude 3.5 Sonnet struggled with numerical data, achieving only 44% accuracy.

Code Generation Showdown:

Beyond the HumanEval benchmark, researchers conducted specific coding tests to compare Claude 3.5 Sonnet and GPT-4o:

Test Case	Claude 3.5 Sonnet	GPT-4o
Python Code Generation (email address from name and domain)	Generated multiple email address patterns	Generated one email address pattern
Web Page Creation (simple personal portfolio)	Created a visually appealing web page with minimal information	Generated a basic web page lacking visual appeal
API Query Generation (cURL for Dall-E-3 image generation)	Directly generated a cURL and returned a result	Generated a bash script requiring additional steps

Based on these tests, Claude 3.5 Sonnet demonstrated a stronger capability in code generation, delivering expected outcomes with minimal need for subsequent prompts. However, the cURL vs bash script comparison is debatable, as GPT-4o’s output offered additional error validation, highlighting the importance of task-specific evaluation criteria.

Overall Comparison Summary:

Category	Claude 3.5 Sonnet	GPT-4o
Reasoning	Excellent, but struggles with numerical data	Very strong, particularly in calculations and antonym identification
Coding	Excellent, produces mature code with minimal errors	Strong, but requires more prompts for complex tasks
Content Writing	Excellent, writes naturally and with a relatable tone	Strong, but can be verbose
Cost	Cheaper than GPT-4o	More expensive
Speed	Faster than GPT-4o	Slower

What can we say?

While Claude 3.5 Sonnet excels in certain areas, the choice between Claude 3.5 Sonnet and GPT-4o ultimately depends on the specific use case. Both models offer impressive capabilities, with Claude 3.5 Sonnet emerging as a strong contender in coding and cost-effectiveness.

What are Artifacts and how do I use them?

And now finally the big new feature: the Artifacts! Think of them as AI-powered sidekicks in your creative process. When you interact with Claude 3.5 Sonnet and request things like code snippets, design mockups, or even full-fledged documents, Claude doesn’t just give you an answer: it creates an Artifact.

These Artifacts appear in a dedicated window alongside your conversation, creating a dynamic workspace where you can see, edit, and build upon Claude’s creations in real-time.

Here are some key features and benefits of using Artifacts:

• Significant and Standalone Content: Artifacts are typically used for complex, self-contained pieces of content over 15 lines long, such as documents, code snippets, websites, SVG images, diagrams, and interactive components.
• Easy Editing and Iteration: You can ask Claude to modify the Artifact, and updates are displayed directly in the Artifact window, preserving a version history for easy reference.
• Multi-Artifact Support: You can open and view multiple Artifacts in a single conversation, referencing them as needed.
• Dynamic Updates: Claude can update an existing Artifact in response to your messages, reflecting changes directly in the Artifact window.
• Code View, Copy, and Download: You can view the underlying code of an Artifact, copy its content, or download it as a file for external use.

So… What can you do with Artifacts? Here some examples:

• Writer’s Block Be Gone: Ask Claude to generate a creative writing prompt or a basic story outline as an Artifact. You can then use this as a springboard for your own writing, building upon the ideas and structure provided.
• Coding Jumpstart: Request a code snippet as an Artifact to get started with a new programming project. Claude can provide code for specific functionalities, helping you overcome initial hurdles and speed up development. You can even create a whole mini-game!
• Data Visualization Made Easy: If you’re struggling to represent a complex dataset visually, Claude can generate an Artifact like a chart or graph. This allows you to explore different visualization options and quickly gain insights from your data.
• Animating Complex Concepts: Need to understand a technical concept like the architecture of an AI Language Model? Claude can generate an animated Artifact using JavaScript and libraries like p5.js, providing a visual and engaging way to learn.
• From Document to Presentation: Tired of reading lengthy PDFs? Claude can transform a document into a visually engaging slide presentation, complete with key points, summaries, and visuals… All within an Artifact!

Remember that Anthropic is still developing Artifacts, with plans to incorporate features like team collaboration and support for even richer content formats: this hints at a future where AI not only “understands” our needs but actively assists us in achieving them!

Refusals and Safety for a Responsible AI Development

Anthropic emphasizes the importance of safety and responsibility in AI development. They train their models to be “Helpful, Honest, and Harmless” (HHH), which can sometimes lead to models refusing to answer potentially harmful or sensitive prompts.

• Wildchat and XSTest Datasets: These datasets are designed to assess a model’s ability to differentiate between harmful and benign requests. Claude 3.5 Sonnet demonstrated improvements in both avoiding unnecessary refusals for harmless prompts and maintaining caution with potentially harmful content.
• Safety Thresholds and AI Safety Level (ASL): Anthropic sets quantitative “thresholds of concern” for risk-relevant areas like Chemical, Biological, Radiological, and Nuclear (CBRN) risks, cybersecurity, and autonomous capabilities. Claude 3.5 Sonnet did not exceed these thresholds, earning it an AI Safety Level 2 (ASL-2) classification, indicating no risk of catastrophic harm.

Conclusion

Anthropic’s roadmap promises further advancements in the Claude 3.5 family. Claude 3.5 Haiku and Claude 3.5 Opus are expected later this year, potentially pushing the boundaries of AI capabilities even further.

The company is also exploring new features like “Memory”, enabling Claude to remember user preferences and interaction history, creating a more personalized and efficient experience.

With these ongoing advancements and its commitment to safety and privacy, Anthropic is poised to remain a major player in the rapidly evolving world of conversational AI. The introduction of Claude 3.5 Sonnet underscores the company’s dedication to pushing the boundaries of AI, delivering solutions that are faster, smarter, and more accessible than ever before.