Holo4 Reviews in 2026

Audience

AI developers, agent platform builders, automation teams, enterprises, and researchers that need generalist computer-use agents capable of completing multi-step workflows across GUIs, desktop and mobile applications, websites, code environments, MCP servers, and business APIs

About Holo4

Holo4 is a family of generalist agentic AI models from H Company designed to operate software across graphical interfaces, code environments, MCP servers, APIs, desktop applications, the web, and mobile devices. The family includes a 27B dense model and a 35B-A3B Mixture-of-Experts model with 3B active parameters, both supporting a 256K context window. Unlike agents optimized around a single interface, Holo4 can click and type on screens, write and execute code, and call MCP or API tools depending on what a task requires. The models were trained with supervised fine-tuning and reinforcement learning on agentic workflows spanning desktop, web, MCP/API, mobile, coding, multimodal reasoning, and GUI grounding. Holo4 27B achieved 85.2% on OSWorld at a reported cost of $0.08 per task, while Holo4 35B-A3B scored 80.8% at $0.05 per task in H Company's evaluation.

Other Popular Alternatives & Related Software

Holo2

H Company’s Holo2 model family delivers cost-efficient, high-performance vision-language models tailored for computer-use agents that navigate, localize UI elements, and act across web, desktop, and mobile environments. The series, available in 4 B, 8 B, and 30 B-A3B sizes, builds on their earlier Holo1 and Holo1.5 models, retaining strong UI grounding while significantly enhancing navigation capabilities. Holo2 models use a mixture-of-experts (MoE) architecture, activating only necessary parameters, to optimize efficiency. Trained on curated localization and agent datasets, they can be deployed as drop-in replacements for their predecessors. They support seamless inference in frameworks compatible with Qwen3-VL models and can be integrated into agentic pipelines like Surfer 2. In benchmark testing, Holo2-30B-A3B achieved 66.1% accuracy on ScreenSpot-Pro and 76.1% on OSWorld-G, leading the UI localization category.

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Nemotron 3 Ultra

Nemotron 3 Nano is a compact, open large language model in NVIDIA’s Nemotron 3 family, designed for efficient agentic reasoning, conversational AI, and coding tasks. It uses a hybrid Mixture-of-Experts Mamba-Transformer architecture that activates only a small subset of parameters per token, enabling low-latency inference while maintaining strong accuracy and reasoning performance. It has approximately 31.6 billion total parameters with around 3.2 billion active (3.6 billion including embeddings), allowing it to achieve higher accuracy than previous Nemotron 2 Nano while using less computation per forward pass. Nemotron 3 Nano supports long-context processing of up to one million tokens, enabling it to handle large documents, multi-step workflows, and extended reasoning chains in a single pass. It is designed for high-throughput, real-time execution, excelling in multi-turn conversations, tool calling, and agent-based workflows where tasks require planning, reasoning, and more.

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Holo3

Holo3 is a state-of-the-art multimodal AI model developed by H Company, specifically designed to operate computers and execute tasks within graphical user interfaces (GUIs) across web, desktop, and mobile environments. Unlike traditional language models that generate text, Holo3 functions as a “computer-use” model: it takes screenshots of a system as input, interprets the visual interface, and outputs precise actions such as clicks, typing, and scrolling to complete real tasks step by step. Built on a Mixture-of-Experts architecture, it efficiently handles complex, multi-step workflows while reducing computational cost by activating only a subset of parameters per task. The model is engineered for real-world deployment and integrates into enterprise workflows through an agent-based platform that allows organizations to configure, deploy, and monitor automated processes end to end.

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Inkling

Inkling is an open-weights multimodal AI model from Thinking Machines designed as a customizable foundation model for developers, researchers, and enterprises. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, and support for context windows up to 1 million tokens. Inkling was trained from scratch on text, images, audio, and video, giving it native capabilities across reasoning, coding, agentic tool use, vision, audio, factuality, and instruction following. It is built with controllable thinking effort so users can balance performance, latency, and token efficiency for different workloads. The model is available for fine-tuning on Tinker, with playground access, API availability through ecosystem partners, and full weights published on Hugging Face. Built for customization, Inkling gives teams an open-weights base model for building domain-specific AI systems, multimodal agents, coding workflows, research tools, and more.

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Pricing

Starting Price:

$0.40 per 1M tokens (input)

Pricing Details:

Input: $0.40 per 1 million tokens
Output: $3 per 1 million tokens

Integrations

API:

Yes, Holo4 offers API access

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Ratings/Reviews

Overall 0.0 / 5

ease 0.0 / 5

features 0.0 / 5

design 0.0 / 5

support 0.0 / 5

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