Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Abliterated Large V2 is a large language model developed by Abliteration AI for cybersecurity research, software engineering, and complex autonomous AI agent tasks. The model is derived from GLM 5.3 and uses a process known as abliteration to modify neural network weights associated with refusal behavior. This approach is intended to reduce content restrictions that can interfere with authorized security assessments, adversarial testing, and technical research. Its one-million-token context window supports processing large codebases, extensive technical documents, and multistep agent execution histories. The model provides advanced reasoning capabilities for programming, debugging, vulnerability reproduction, and technical problem-solving. Its cybersecurity applications include penetration testing research, security control evaluation, AI red teaming, and vulnerability validation in authorized environments. Native function calling and structured output capabilities allow developers to integrate the model with automated tools and external systems. Abliterated Large V2 is hosted using FP8 numerical precision and supports OpenAI-compatible and Anthropic-style API interfaces. The platform also provides automatic prompt caching and a zero-data-retention policy for API prompts and responses by default.
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
Yes
API Access
Has API
Yes
Integrations
Abliteration.ai
Yes
Model Context Protocol (MCP)
No
Pokee AI
No
Venice.ai
Yes
Integrations
Abliteration.ai
No
Model Context Protocol (MCP)
Yes
Pokee AI
Yes
Venice.ai
No
Pricing Details
$3 per 1M tokens (input)
$3.00 per 1 million input tokens, $5.00 per 1 million output tokens, and $0.30 per 1 million cached input tokens
Free Trial
No
Free Version
No
Pricing Details
$0.15 per 1M tokens
Free Trial
No
Free Version
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
Deployment
Web-Based
Yes
On-Premises
Yes
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
Yes
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
Abliteration.ai
Founded
2025
Country
United States
Website
abliteration.ai/
Vendor Details
Company Name
Pokee AI
Founded
2024
Country
United States
Website
console.pokee.ai/model