Laguna XS.2 vs. Holo4

Laguna XS.2 stands out as Poolside's groundbreaking open-weight coding model, noted for being the lightest and fastest in the Laguna lineup. Equipped with a staggering 33 billion parameters organized in a Mixture of Experts structure, of which 3 billion are active, this model has undergone extensive training in-house utilizing 30 trillion tokens. As the most recent generation model available to the public, it features a second-generation architecture and represents Poolside's first open-weight release, benefiting from lessons learned during the Laguna M.1 training process, which utilized synthetic data and reinforcement learning. Tailored specifically to optimize agentic coding workflows, Laguna XS.2 is exceptional in coding, acting, and rapid iteration, particularly within Poolside's coding agent ecosystem. This model is especially beneficial for developers and teams in need of a lightweight and efficient coding solution, as opposed to more complex frontier systems. Released under the flexible Apache 2.0 license, it enables the community to evaluate, refine, quantize, and build upon its weights, fostering an environment of collaborative development. Ultimately, Laguna XS.2 not only serves as a powerful tool for agentic coding but also promotes creativity and experimentation among its users, allowing for a diverse range of applications and enhancements.

Holo4 is a family of agentic AI models developed by H Company for computer use and multi-step automation across desktop, web, mobile, terminal, MCP, and API environments. The series consists of Holo4 27B, a dense 27-billion-parameter model, and Holo4 35B-A3B, a 35-billion-parameter Mixture-of-Experts model with 3 billion active parameters. Rather than specializing exclusively in graphical interfaces or tool calling, Holo4 can click and type on screens, write and run its own code, and invoke MCP or API tools as different stages of a workflow require. The same model can therefore move between desktop applications, websites, Android applications, code sandboxes, and business APIs without switching to a separate model for each interface. H Company's Agentic Task Factory generated approximately 10,000 tasks across web applications, MCP servers, desktop software, and hybrid environments to support model development and evaluation. Holo4 underwent supervised fine-tuning on 127 billion tokens, with roughly three-quarters of that training data consisting of successful agentic trajectories covering desktop, web, MCP/API, and mobile tasks. Two reinforcement-learning experts were subsequently trained for desktop/web workflows and terminal/MCP/API workflows before being merged into the final generalist model. In H Company's evaluations, Holo4 27B scored 85.2% on OSWorld, 61.7% on OSWorld 2.0, 45.4% on AutomationBench, and 85.1% on AndroidWorld, although the company notes that reference-model results can use different harnesses and effort levels. Holo4 27B supports a 256K context window and is priced through the H Models API at $0.40 per million input tokens, $0.04 per million cached input tokens, and $3.00 per million output tokens. Holo4 35B-A3B also supports 256K context and is priced at $0.30 per million input tokens, $0.03 per million cached input tokens, and $2.00 per million output tokens.