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Description
The Apple Foundation Models framework enables developers to leverage Apple's on-device model, which excels in language comprehension, organized output, and invoking tools. This framework grants access to the large language model integral to Apple Intelligence, thereby assisting applications in executing intelligent tasks tailored to their specific needs. By recognizing patterns, the text-based on-device model can produce relevant text in response to various prompts and has the capability to call upon developer-written code for targeted functionalities. Developers are empowered to create text content across a multitude of applications, such as summarization, entity extraction, text comprehension, enhancement, game dialogues, creative content crafting, classification, and beyond. Additionally, it offers guided generation features that enable developers to construct complete Swift data structures with robust assurances by utilizing the Generable macro, enhancing the versatility and functionality of the model. Ultimately, this framework significantly streamlines the process of integrating advanced AI capabilities into applications.
Description
The Pokee-Isaac text-only agentic model features an impressive context window capable of accommodating up to 10 million tokens. This model is engineered to facilitate reasoning, planning, tool invocation, and the execution of extensive tasks, all while being compact enough for deployment within a Virtual Private Cloud (VPC), on customer premises, on a workstation, or directly on devices. According to Pokee, Isaac excels in long-context performance across the RULER benchmarks, effectively handling token ranges from 256K to 10M and outperforming competitors in multi-needle retrieval tests at 256K, 512K, and 1M tokens. Its agentic framework is specifically designed for reliable function calling, maintaining coherence over multiple turns, executing in real-shell environments, and the ability to discover and integrate tools across live Multi-Cloud Platforms (MCP) servers. In controlled assessments by Pokee, Isaac secured the top position on BFCL v4 and τ³-bench, while placing second in the Terminal-Bench 2.1 text-only subset and third in MCP-Atlas. Furthermore, security evaluations using the DTAP method indicated that it achieved the lowest overall attack success rate in the comparative analysis, all while demonstrating robust performance on benign tasks. This combination of features underscores Isaac's capability as a versatile and secure model in various operational environments.
API Access
Has API
API Access
Has API
Pricing Details
Free
Free Trial
Free Version
Pricing Details
$0.15 per 1M tokens
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Apple
Founded
1976
Country
United States
Website
developer.apple.com/documentation/FoundationModels
Vendor Details
Company Name
Pokee AI
Founded
2024
Country
United States
Website
console.pokee.ai/model