Claude 3.5 Sonnet

Anthropic's advanced AI model for coding, reasoning, and computer-use tasks with a 200K context window.

4.8 (4)

审阅者 Daniel Nikulshyn·更新 2026年5月

概览

Claude 3.5 Sonnet is a large language model from Anthropic designed to handle complex reasoning, software engineering workflows, and long-document analysis. It offers a 200,000-token context window, making it well suited for working with extensive codebases, research papers, and multi-step problem solving. The model is notable for its computer use capability, which allows it to interpret screenshots and interact with desktop interfaces by moving the cursor, clicking, and typing. This positions it as a tool not just for chat-based assistance but for agentic automation of UI-driven tasks. Claude 3.5 Sonnet is available through Anthropic's API, the Claude web app, and major cloud platforms including Amazon Bedrock and Google Cloud Vertex AI, giving developers multiple integration paths.

主要功能

200K-token context window
Computer use for UI automation
Advanced code generation and debugging
Vision and document understanding
API access via Anthropic, AWS, and GCP
Improved multi-step reasoning

优点 & 缺点

优点

Strong performance on coding and reasoning benchmarks
Large 200K-token context window
Supports computer-use and agentic workflows
Available across multiple cloud platforms

缺点

Computer use is still in beta and can be unreliable
Usage costs can add up for long-context tasks
Rate limits may constrain heavy workloads

评测

4.8

4 个评分的平均值。

登录以留下评测。

Jamal Carter

Does the job

Pretty happy overall. API access via Anthropic, AWS, and GCP just works and available across multiple cloud platforms. but no dealbreakers — I'd recommend it to a friend without hesitating.

Elena Rossi

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on 200K-token context window, and large 200K-token context window caught me off guard. still, I'd recommend giving it a real trial.

Aisha Khan

Years in this space

I've evaluated a lot of these over the years. What stands out here is improved multi-step reasoning — handled better than most — and supports computer-use and agentic workflows. Rate limits may constrain heavy workloads is my one real gripe. Worth the time if this is your use case.

Leila Hassan

Solid for our team

We rolled this out across the team last quarter and strong performance on coding and reasoning benchmarks. 200K-token context window fits neatly into how we already work, and computer use for UI automation removed a step we used to do by hand. Usage costs can add up for long-context tasks, which is the main caveat, but it has held up under daily use.