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Claude Haiku 3.5

by Anthropic

AI Model
Low Confidence

Claude 3.5 Haiku is a lightweight, fast, and cost-effective large language model (LLM) developed by Anthropic. It is part of the Claude 3.5 model family, designed for high-speed performance and efficiency in tasks requiring quick response times, such as customer support, content moderation, and real-time data processing. Despite its smaller size, it retains strong capabilities in text understanding, generation, and contextual reasoning, making it suitable for applications where latency and cost are critical factors.

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Transparency Score

Weighted across four pillars · updated February 10, 2026

Overall grade
70% weighted
ABCDF
Model
—
Not yet assessed
Infrastructure
—
Not yet assessed
Openness Assessment
7%Closed
1 Open
0 Partial
0 Closed
13 Unknown
Availability
0/5 Open
Documentation
1/6 Open
Access Methods
0/3 Open
Data from EU Open Source AI Index (DOI: 10.5281/zenodo.15386042), licensed under CC-BY 4.0

Capabilities

Text Generation
Conversation
Summarization
Translation
Code Generation
Content Moderation

Vendor Information

Complete information about the vendor/provider of this AI application

AnthropicView all products →
Anthropic PBC(Trading as: Anthropic)
Contact Information
anthropic.com
press@anthropic.com
Registered Address
548 Market Street, San Fransisco, CA, 94105, US

EU AI Act Provider Information

Verification Status:
Verified
(Last verified: August 3, 2026)
Anthropic PBC(Trading as: Anthropic)
548 Market Street, San Fransisco, CA, 94105, US
press@anthropic.com
Anthropic Ireland, Limited
6th Floor South Bank House, Barrow Street, Dublin, IE
Compliance Documents
CE Marking: Pending
Instructions for Use
Available in EU Member States
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The vendor states that its AI models may produce harmful, biased, or inaccurate responses and that research is conducted to understand and mitigate these risks. Techniques like Constitutional AI are used to align models with human values, but limitations in safety, accuracy, and potential reproduction of trained content remain.

Potential Risks

1 considerations identified

1

Review recommended before use

These considerations are automatically identified based on publicly available information about the vendor and AI catalog data. Actual risks may vary based on your specific use case and implementation.

Supply Chain Network

Visual representation of the vendor's digital supply chain relationships

Compliance & Risk

Get insights into risk by running assessments on this AI application.

Work at Anthropic? Claim this listing to correct or complete the data.

Added: February 10, 2026
Updated: February 10, 2026

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