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features
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support

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Description

Beam represents Reflection’s inaugural open-weight model, characterized as a sparse Mixture-of-Experts framework featuring a staggering 501 billion parameters, of which 23 billion are actively utilized, specifically crafted for tasks involving coding, reasoning, and agentic functions. Its prowess is derived from extensive pretraining and reinforcement learning, having been developed using a vast dataset of 23.8 trillion diverse, high-quality tokens sourced from the web, public domains, and proprietary licensed materials. With a targeted emphasis on enhancing coding and agentic capabilities, Beam is engineered to provide competitive open-weight performance while ensuring efficient inference compute. This model adeptly handles a wide array of tasks, including intricate software engineering, terminal operations, STEM activities, web searches, tool utilization, and general knowledge inquiries. Reinforcement learning techniques have been employed to bolster its abilities in multi-step reasoning, tool application, and responsiveness to environmental feedback. Moreover, users have the flexibility to manage the balance between efficiency and performance through an adjustable reasoning effort parameter, thus tailoring the model's output to better suit their specific needs.

Description

The Holo2 model family from H Company offers a blend of affordability and high performance in vision-language models specifically designed for computer-based agents that can navigate, localize user interface elements, and function across web, desktop, and mobile platforms. This new series, which is available in sizes of 4 billion, 8 billion, and 30 billion parameters, builds upon the foundations laid by the earlier Holo1 and Holo1.5 models, ensuring strong grounding in user interfaces while making substantial improvements to navigation abilities. Utilizing a mixture-of-experts (MoE) architecture, the Holo2 models activate only the necessary parameters to maximize operational efficiency. These models have been trained on carefully curated datasets focused on localization and agent functionality, allowing them to seamlessly replace their predecessors. They provide support for effortless inference in environments compatible with Qwen3-VL models and can be easily incorporated into agentic workflows such as Surfer 2. In benchmark evaluations, the Holo2-30B-A3B model demonstrated impressive results, achieving 66.1% accuracy on the ScreenSpot-Pro test and 76.1% on the OSWorld-G benchmark, thereby establishing itself as the leader in the UI localization sector. Additionally, the advancements in the Holo2 models make them a compelling choice for developers looking to enhance the efficiency and performance of their applications.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Qwen3 No 
Surfer H No 

Integrations

Qwen3 Yes 
Surfer H Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Deployment

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

Types of Training

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

Types of Training

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

Vendor Details

Company Name

Reflection

Country

United States

Website

reflection.ai/blog/introducing-beam

Vendor Details

Company Name

H Company

Founded

2023

Country

France

Website

www.hcompany.ai/blog/holo2

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