Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Integrations
Qwen3
No
Surfer H
No
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