Compare Holo2 vs. Laguna S 2.1 in 2026

Laguna S 2.1

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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.

Description

Laguna S 2.1 is an advanced open weight coding model that emphasizes long-term project completion and efficient reasoning capabilities. Featuring a 118-billion-parameter Mixture-of-Experts architecture, it activates 8 billion parameters for each token and accommodates a context window of up to one million tokens in both thinking and non-thinking modes. The model’s streamlined active size allows it to perform intricate tasks on local machines while still competing favorably against significantly larger models across various benchmarks, including terminal usage, software engineering, codebase question answering, and tool utilization. Designed for resilience, Laguna S 2.1 excels in tackling challenging assignments with enhanced persistence, meticulous verification, and a readiness to backtrack rather than prematurely claim success. In practical applications, it has successfully created and validated a browser rendering engine from scratch, optimized an agent harness for improved execution speed and reduced memory usage, and conducted extensive mathematical research using the available tools within its environment, demonstrating its versatility and effectiveness. This combination of features positions Laguna S 2.1 as a powerful tool for developers seeking innovative solutions.