Meet Anthropic's Claude 3.5 Sonnet, launched on June 20, 2024. This AI model is designed to handle both text and image inputs, delivering text outputs with a context length of up to 200,000 tokens. It excels in coding, data science, visual processing, and agentic tasks, making it a versatile tool for various complex operations. Whether you're interpreting graphs or running code, Claude 3.5 Sonnet supports a range of parameters like max_tokens and temperature to tailor its performance to your needs. Discover how it can enhance your projects with its multimodal capabilities.
Use Cases
Here are a few ways teams apply Anthropic: Claude 3.5 Sonnet (2024-06-20) in practice—from fast drafting to multimodal understanding. Adapt these ideas to your workflow.
Autonomously write and run code
Enhance data science projects
Interpret visual data effectively
Solve multi-step problems
Engage with diverse systems
Key Features
A quick look at the capabilities that make this model useful in real projects.
Handles text and image inputs
Outputs text with 200,000 token context
Excels in coding and data science
Interprets charts and graphs
Performs complex agentic tasks
Specs
Overview
Vendor
anthropic
Model ID
anthropic/claude-3.5-sonnet-20240620
Release
2024-06-20
Modalities & context
Input
text · image · file
Output
text
Context
200,000 tokens
Parameters & defaults
Supported parameters: max_tokens, stop, temperature, tool_choice, tools, top_k, top_p
Defaults: temperature 0.2, top_p 0.95
Benchmark tests: Anthropic: Claude 3.5 Sonnet (2024-06-20)
We ran this model against a few representative prompts to show its range. Review the outputs below and be the judge.
Text
Prompt:
Write 150 words on how AI might positively upend work, leisure and creativity
The Anthropic Claude 3.5 Sonnet, released on June 20, 2024, is an advanced language model designed for a variety of natural language processing tasks. It is capable of generating coherent text, summarizing information, answering questions, and engaging in dialogue across multiple domains. Typical use cases include content creation, customer support automation, and educational assistance, where users can benefit from its ability to understand context and provide relevant responses.
Notable constraints include the model's reliance on the quality of input data, which can impact the accuracy and relevance of its outputs. Additionally, while Claude 3.5 Sonnet is designed to minimize biases, it may still reflect some inherent biases present in the training data. Users are encouraged to review and verify the information generated to ensure it meets their specific needs.
Run this prompt on Upend.AI