Microsoft Analysis’s AI Frontiers lab launched Fara1.5. It’s a household of computer-use agent (CUA) fashions for the browser. The discharge ships three sizes: Fara1.5-4B, Fara1.5-9B, and Fara1.5-27B. The fashions are built-in with MagenticLite, Microsoft’s sandboxed browser interface for these brokers.
Laptop-use brokers are pixel-to-action fashions that drive an actual browser. They learn screenshots and emit mouse and keyboard actions to finish duties. Latest agent merchandise like OpenAI’s Operator and Google’s Gemini 2.5 Laptop Use sit on this class.
Fara1.5-27B scores 72% activity success on On-line-Mind2Web. That benchmark covers 300 duties throughout 136 common websites. On the identical analysis, OpenAI’s Operator scores 58.3% and Gemini 2.5 Laptop Use scores 57.3%. Yutori’s Navigator n1 reaches 64.7%, and Fara1.5-9B scores 63.4%. That almost doubles the predecessor Fara-7B, which scored 34.1% on the identical benchmark.
Structure and agent loop
The fashions use Qwen3.5 base checkpoints of their 4B, 9B, and 27B variants. They function by means of an observe-think-act loop. At every step, the mannequin takes the prior dialog historical past and the three most up-to-date browser screenshots. It then emits ideas and a single subsequent motion.
The motion house contains commonplace mouse and keyboard inputs and web-specific actions like net search. It additionally exposes meta-actions for context administration. These embrace memorizing information for later use and asking the consumer clarification questions. These meta-actions let the agent function over longer horizons and work collaboratively with customers.
Coaching combine
Coaching makes use of supervised fine-tuning on roughly two million samples. The combination is 60% net trajectories and 12.8% artificial environments. Kind filling and consumer interactions account for 12.5%. Grounding contributes 8.8% and VQA 4.9%. Smaller slices cowl GUI drag, instruction following, and security. Loss is utilized solely to the three most up-to-date turns in every trajectory.
FaraGen1.5: the artificial knowledge pipeline
FaraGen1.5 is the artificial pipeline that produced the coaching trajectories. It has three modular elements: environments, solvers, and verifiers.
Environments break up into two varieties. Open-internet duties run on reside web sites that don’t require logins. Gated-domain duties require authenticated classes or take irreversible actions, like sending an e mail.
For gated domains, the group constructed six artificial clones referred to as FaraEnvs. They cowl Mail, Calendar, Stream, ML, Keep, and Scheduler. Every clone has a practical frontend, a totally practical API, and a database with persona-based seed knowledge.
These environments had been constructed utilizing GitHub Copilot CLI plus iterative human refinement. As a result of the group controls the complete stack, they know the right final result for each activity. For duties that mutate the backend, an LLM choose compares database snapshots earlier than and after execution. Duties that don’t change state are scored towards pre-computed reference solutions.
The solver agent makes use of OpenAI’s GPT-5.4 with customized instruments that mirror Fara1.5’s motion house. The solver scores 83% on On-line-Mind2Web utilizing automated WebJudge. The earlier Fara-7B solver scored 67% on the identical analysis. A consumer simulator is invoked when the solver points an ask_user name or when it finishes a activity.
Three verifiers gate which trajectories enter coaching. Correctness makes use of LLM-generated rubrics for open-internet duties and privileged database judging for artificial ones. Effectivity penalizes redundant or pointless actions. Person-interaction verification checks whether or not the agent paused at vital factors.
Essential factors and security
Fara1.5 is educated to cease and ask the consumer in three conditions. First: the duty requires private info the consumer has not offered. Second: the duty description is ambiguous or lacking particulars wanted to behave. Third: an irreversible motion is about to be carried out with out prior approval.
Security coaching makes use of public security datasets and inside duties aligned with Microsoft’s Accountable AI Coverage. Inside MagenticLite, all agent actions are logged and auditable. The sandboxed browser additionally acts as a safety boundary between the agent and the consumer’s machine.
Different benchmarks
On WebVoyager, Fara1.5-27B scores 88.6%, the 9B reaches 86.6%, and the 4B hits 80.8%. The 9B additionally tops similar-sized friends like MolmoWeb 8B, GUI-Owl-1.5 8B, and Holo2 8B. All Fara1.5 analysis runs use Browserbase to stabilize classes and cut back session-level blocking. Numbers are averaged over three impartial runs.
On WebTailBench v1.5, which targets long-tail net duties, Fara1.5-9B scores 64.5% course of success and 32.3% final result success. GPT-5.4 scores 79.6% course of and 57.4% final result on the identical benchmark.
Key Takeaways
Listed here are 5 one-line key takeaways:
- Microsoft Analysis launched Fara1.5, a household of browser computer-use brokers in 4B, 9B, and 27B sizes constructed on Qwen3.5.
- Fara1.5-27B scores 72% on On-line-Mind2Web, beating OpenAI Operator (58.3%), Gemini 2.5 CU (57.3%), and Yutori Navigator n1 (64.7%).
- The FaraGen1.5 artificial knowledge pipeline unlocks coaching on gated domains by way of six practical app clones (FaraEnvs) constructed with GitHub Copilot CLI.
- Fara1.5 pauses to ask the consumer at vital factors: lacking data, ambiguous duties, or irreversible actions with out approval.
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