Holo4 solves the core frustrations teams face when trying to automate real‑world computer work. Pure GUI agents break when a screen is unavailable, and tool‑calling agents stall whenever an application lacks an API. Frontier models that can handle both modalities are expensive to run and often come with restrictive licenses that block commercial self‑hosting. Developers also waste time stitching together separate workflows for desktop, web, mobile, sandbox and backend APIs, which leads to fragile integrations and higher maintenance costs.
Holo4 addresses these pain points with a single vision‑language model that can click, type, write code and invoke MCP or API tools. The same weights run on desktop browsers, Android emulators, code sandboxes and business APIs, so you only need one integration point instead of maintaining platform‑specific adapters. The model ships in two sizes: a dense 27B variant and a 35B‑A3B Mixture‑of‑Experts version with only 3B active parameters per token. Both support a 256K token context, letting the agent keep long histories and complex instructions in memory.
Cost is a major barrier for many teams. Holo4 27B runs on the H Models API at $0.40 input and $3.00 output per million tokens, while the 35B‑A3B version costs $0.30 input and $2.00 output per million tokens—far cheaper than comparable frontier models that charge several dollars per task. For organizations that need to self‑host, the 35B‑A3B weights are released under Apache 2.0, permitting commercial use without royalties. The 27B weights are CC BY‑NC 4.0 and are available through the API for commercial scenarios, while still allowing non‑commercial local experimentation.
Benchmark results show Holo4 27B matching or exceeding base model performance on OSWorld (85.2% vs 84.3%) and AndroidWorld (85.1%) at a fraction of the cost. On long‑horizon workflows it reaches 61.7% on OSWorld 2.0 for $1.22 per task, compared with $8.48 for Claude Opus 5.5. The model’s training data come from a verified Agentic Task Factory that guarantees tasks can be solved only through real interfaces, reducing the risk of brittle, simulation‑only solutions.
Deployment is straightforward. The API is OpenAI‑compatible at https://api.hcompany.ai/v1, and the weights are provided in BF16, FP8, NVFP4 and 4‑bit GGUF formats for serving with vLLM or llama.cpp. A simple quickstart guide walks you through setting up a self‑hosted endpoint or calling the hosted API, letting you go from prototype to production in minutes rather than weeks.
By unifying GUI interaction, code generation and tool calling in one affordable, commercially deployable model, Holo4 removes the need to juggle multiple agents, worry about licensing, or pay premium prices for frontier capabilities. Teams can now build reliable, cross‑platform automations that work wherever the user works—desktop, web, mobile or backend—without rewriting core logic for each environment.
#AI #Productivity #Automation #LLM #DevTools #AIAgents