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Description

Claude Sonnet 5.5 is a general-purpose AI model from Anthropic built for everyday professional tasks, agentic coding, knowledge work, and fast iteration. It is the second model in the Claude 5.5 family and is intended to complement Claude Opus 5.5 by offering lower-cost performance on more clearly defined workloads. Anthropic reports that Sonnet 5.5 generates output more than 30% faster than Sonnet 5 and usually completes tasks with fewer tokens. Pricing remains $2 per million input tokens and $10 per million output tokens, with cache reads priced at $0.20 per million tokens. On Terminal-Bench 4.0, the model scores 70.6%, compared with 10.3% for Sonnet 5, while also posting sizable improvements on FrontierCode, CursorBench, and other coding benchmarks. Its knowledge-work results are also substantially stronger, including a GDPval-AA score of 1844 and an AA-Briefcase score of 1811, both close to Claude Opus 5.5. Anthropic says early testers found the model faster, more collaborative, more concise, and particularly strong at design-oriented work such as polished interfaces and presentation decks. The model supports adjustable effort levels so users can trade off speed and cost against more extended reasoning and checking. Claude Sonnet 5.5 is available through Claude apps, Claude Code, the Claude Platform, and major cloud providers including AWS, Google Cloud, and Microsoft Azure.

Description

An advanced End-to-End MLLM is designed to accept various forms of references and effectively ground responses. The Ferret Model utilizes a combination of Hybrid Region Representation and a Spatial-aware Visual Sampler, which allows for detailed and flexible referring and grounding capabilities within the MLLM framework. The GRIT Dataset, comprising approximately 1.1 million entries, serves as a large-scale and hierarchical dataset specifically crafted for robust instruction tuning in the ground-and-refer category. Additionally, the Ferret-Bench is a comprehensive multimodal evaluation benchmark that simultaneously assesses referring, grounding, semantics, knowledge, and reasoning, ensuring a well-rounded evaluation of the model's capabilities. This intricate setup aims to enhance the interaction between language and visual data, paving the way for more intuitive AI systems.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Bash Yes 
Bind AI Yes 
C Yes 
C++ Yes 
Claude Design Yes 
Claude Desktop Yes 
Claude Science Yes 
Emergent Yes 
Go Yes 
IntelliJ IDEA Yes 
Microsoft Foundry Yes 
OpenCode Yes 
Perplexity Computer Yes 
PowerShell Yes 
Replit Yes 
Ruby Yes 
Rust Yes 
Simtheory Yes 
Sup AI Yes 
Trinity-Large-Thinking Yes 

Integrations

Bash No 
Bind AI No 
C No 
C++ No 
Claude Design No 
Claude Desktop No 
Claude Science No 
Emergent No 
Go No 
IntelliJ IDEA No 
Microsoft Foundry No 
OpenCode No 
Perplexity Computer No 
PowerShell No 
Replit No 
Ruby No 
Rust No 
Simtheory No 
Sup AI No 
Trinity-Large-Thinking No 

Pricing Details

$2 per 1M tokens (input)
$2 per million input tokens and $10 per million output tokens.

Cache reads, which account for most costs in agentic and coding workloads, are priced at just $0.20 per million tokens
Free Trial No 
Free Version No 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

Anthropic

Founded

2021

Country

United States

Website

claude.ai

Vendor Details

Company Name

Apple

Founded

1976

Country

United States

Website

github.com/apple/ml-ferret

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