Fara-7B: An Efficient Agentic Model for Computer Use

Progress in computer use agents (CUAs) has been constrained by the absence of large and high-quality
datasets that capture how humans interact with a computer. While LLMs have thrived on abundant
textual data, no comparable corpus exists for CUA trajectories. To address these gaps, we introduce
FaraGen, a novel synthetic data generation system for multi-step web tasks. FaraGen can propose
diverse tasks from frequently used websites, generate multiple solution attempts, and filter successful
trajectories using multiple verifiers. It achieves high throughput, yield, and diversity for multi-step
web tasks, producing verified trajectories at approximately $1 each. We use this data to train Fara-7B, a
native CUA model that perceives the computer using only screenshots, executes actions via predicted
coordinates, and is small enough to run on-device. We find that Fara-7B outperforms other CUA models
of comparable size on benchmarks like WebVoyager, Online-Mind2Web, and WebTailBench– our novel
benchmark that better captures under-represented web tasks in pre-existing benchmarks. Furthermore,
Fara-7B is competitive with much larger frontier models, illustrating key benefits of scalable data
generation systems in advancing small efficient agentic models. We are making Fara-7B open-weight on
Microsoft Foundry and HuggingFace, and we are releasing WebTailBench.