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Average Ratings 0 Ratings

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features
design
support

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Description

Holo4 is H Company's series of generalist computer-use and agentic AI models built to perform multi-step work across software interfaces. It is available as Holo4 27B, a dense 27-billion-parameter model, and Holo4 35B-A3B, a Mixture-of-Experts model containing 35 billion total parameters with 3 billion active. Holo4 can interact with applications by clicking and typing through graphical interfaces, writing and executing code, or calling MCP and API tools. The same model can operate across desktops, websites, Android devices, code sandboxes, and business APIs without requiring developers to select a separate specialized model for each environment. H Company trained Holo4 using 127 billion supervised fine-tuning tokens, with approximately three-quarters consisting of successful agentic trajectories spanning desktop, web, MCP/API, and mobile tasks. Reinforcement learning then trained separate experts for desktop and web interaction and for terminal, MCP, and API work before merging them into a single model. Holo4 27B scored 85.2% on OSWorld, 61.7% on OSWorld 2.0, 45.4% on AutomationBench, and 85.1% on AndroidWorld in the evaluations reported by H Company. The models support a 256K context window, with the 27B model positioned for greater accuracy on long multi-step tasks and the 35B-A3B version positioned as a faster and less expensive alternative. Holo4 is available through a hosted API and downloadable model weights, enabling developers and enterprises to build agents that perform workflows spanning multiple applications and interaction methods.

Description

Liquid AI's LFM2.5 represents an advanced iteration of on-device AI foundation models, engineered to provide high-efficiency and performance for AI inference on edge devices like smartphones, laptops, vehicles, IoT systems, and embedded hardware without the need for cloud computing resources. This new version builds upon the earlier LFM2 framework by greatly enhancing the scale of pretraining and the stages of reinforcement learning, resulting in a suite of hybrid models that boast around 1.2 billion parameters while effectively balancing instruction adherence, reasoning skills, and multimodal functionalities for practical applications. The LFM2.5 series comprises various models including Base (for fine-tuning and personalization), Instruct (designed for general-purpose instruction), Japanese-optimized, Vision-Language, and Audio-Language variants, all meticulously crafted for rapid on-device inference even with stringent memory limitations. These models are also made available as open-weight options, facilitating deployment through platforms such as llama.cpp, MLX, vLLM, and ONNX, thus ensuring versatility for developers. With these enhancements, LFM2.5 positions itself as a robust solution for diverse AI-driven tasks in real-world environments.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Amazon Bedrock Yes 
Auggie CLI Yes 
ChatGPT Atlas Yes 
ChatGPT Go Yes 
ChatGPT Images Yes 
ChatGPT Work Yes 
Flowise Yes 
GPT-5.5-Cyber Yes 
GPT‑5.4‑Cyber Yes 
Gemma 4 No 
GitHub Yes 
Kubernetes Yes 
Microsoft Copilot Studio Yes 
OpenRouter Yes 
Prism Yes 
SQL Yes 
Simtheory Yes 
ThreadMaster.ai Yes 
Use AI Yes 
Verdent Yes 

Integrations

Amazon Bedrock Yes 
Auggie CLI No 
ChatGPT Atlas No 
ChatGPT Go No 
ChatGPT Images No 
ChatGPT Work No 
Flowise No 
GPT-5.5-Cyber No 
GPT‑5.4‑Cyber No 
Gemma 4 Yes 
GitHub No 
Kubernetes No 
Microsoft Copilot Studio No 
OpenRouter No 
Prism No 
SQL No 
Simtheory No 
ThreadMaster.ai No 
Use AI No 
Verdent No 

Pricing Details

$0.40 per 1M tokens (input)
Input: $0.40 per 1 million tokens
Output: $3 per 1 million tokens
Free Trial No 
Free Version No 

Pricing Details

Free
Free Trial Yes 
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 Yes 
iPad App Yes 
Android App Yes 
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) Yes 
In Person No 

Vendor Details

Company Name

H Company

Founded

2023

Country

France

Website

openai.com

Vendor Details

Company Name

Liquid AI

Founded

2023

Country

United States

Website

www.liquid.ai/blog/introducing-lfm2-5-the-next-generation-of-on-device-ai

Product Features

Product Features

Alternatives

No Alternatives

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