microsoft/Fara1.5-27B

Fara1.5-27B

Fara1.5-27B is a multimodal computer use agent (CUA) for web browsers, from Microsoft Research AI Frontiers. It observes the browser through screenshots and acts on the user's behalf by emitting structured tool calls — click, type, scroll, visit URL, web search, and so on — to complete tasks end-to-end.

The model is vision-only at perception time: it sees the browser through screenshots, not the DOM or accessibility tree. Internal reasoning and trajectory history are tracked as text. Given the latest screenshot and prior actions, it predicts the next action with grounded arguments (e.g., pixel coordinates for a click).

Fara1.5-27B is supervised fine-tuned from Qwen3.5-27B on data generated by FaraGen1.5, our multi-agent pipeline that synthesizes web tasks, executes trajectories to solve them, and verifies the results before training.

It's co-designed with MagenticLite, and that's the recommended deployment for both research and production.

Highlights

• End-to-end web task completion. Fills forms, books reservations, applies for jobs, plans trips, runs shopping carts. Not just clicking around — sequencing actions toward a goal.
• Vision-only perception. Operates on screenshots alone, no DOM access required. Matches the input modality available to a human user.
• Coordinate-grounded actions. Predicts pixel-level click and drag targets directly. No separate grounding model needed.
• Critical-points safety design. Trained to stop and ask before personal info entry, payments, submissions, sign-ins, sending messages, or other irreversible actions — even if it could technically continue.
• 262K context. Long enough for multi-screenshot trajectories with full action history.
• On-device viable. 27B parameters means it runs on a single A100/H100/B200 with room for the screenshot history.

Model Details

| | | |---|---| | Developer | Microsoft Research AI Frontiers | | Architecture | Multimodal decoder-only LM (image + text → text) | | Parameters | 27B | | Context length | 262,144 tokens | | Inputs | User goal (text), current screenshot(s), prior agent thoughts and actions | | Outputs | Chain-of-thought block followed by a tool-call block (XML-tagged) | | Training period | January 2026 – April 2026 | | Training compute | 64 × NVIDIA B200, 6 days | | Release date | 21 May 2026 | | License | MIT | | Base model | Qwen3.5-27B |

Recommended Deployment: MagenticLite

The safest way to run Fara1.5-27B is inside MagenticLite, which provides:

• Sandboxing — the browser runs in a Docker container with no access to host files or environment variables
• Allow-lists — restrict navigation to a user-specified set of domains
• Watch-mode — real-time monitoring of every action with full trace logs
• Pause — immediate halt of agent activity at any point

If you integrate Fara1.5-27B directly, you're responsible for these controls. Don't run this model with unrestricted browser access on a machine that has anything sensitive on it.

Quickstart

Requirements

• torch >= 2.11.0
• transformers >= 5.2.0
• vllm >= 0.19.1
• A set of GPUs with enough memory for a 27B model in bf16 (A6000, A100, H100, and B200 have been tested). We recommend sharding the model over at least 2 GPUs.

Serve with vLLM

vllm serve microsoft/Fara1.5-27B \
--dtype bfloat16 \
--max-model-len 262144 \
--limit-mm-per-prompt image=10

System prompt

Fara1.5-27B is trained against a specific system prompt. Use it verbatim for best results:

You are Fara, a computer use agent (CUA) specialized for web browsers. You are developed by Microsoft AI Frontiers. You assist users with completing and automating tasks that require the use of a web browser.

The model was trained in the timeframe of January - April 2026. You can effectively perform tasks even beyond this range by accessing the web browser and using the latest information on the live web. But your knowledge cutoff is limited to early 2026, so you may not be aware of events or developments that occurred after that time, without explicitly browsing and searching for latest information on the web.

This edition of the model was trained using SFT on top of Qwen3.5-27B, using a synthetic data mixture generated and developed by Microsoft AI Frontiers.

A critical point is a situation where we must pause and request information or confirmation from the user before proceeding. There are three types:

Case 1: Missing User Information — The task requires personal information that the user has not provided (e.g., email, phone number, address, payment details). Never fabricate or assume personal information. Fill in only what the user has explicitly provided, then pause and ask for any missing required fields.

Case 2: Underspecified Task — The task description is ambiguous or missing details needed to make a decision at the current step. Pause and ask for clarification.

Case 3: Irreversible Action — We are about to perform an action that cannot be undone (e.g., submitting a form, completing a purchase, sending a message, deleting data). If the user explicitly authorized the action, proceed. Otherwise, stop and ask for confirmation.

Only stop at a critical point if (1) required information is missing, (2) the task is ambiguous, OR (3) an irreversible action lacks explicit user authorization.

The full system prompt, including the complete computer_use tool schema, ships with the model in MagenticLite.

Tool schema

Fara1.5-27B emits actions as ... XML blocks containing a JSON object that calls the computer_use function. Supported actions:

| Action | Purpose | |---|---| | left_click, right_click, double_click, triple_click | Mouse clicks at (x, y) | | mouse_move, left_click_drag | Cursor positioning and drag | | type, key | Keyboard input | | scroll, hscroll | Page scrolling | | visit_url, history_back | Browser navigation | | web_search | Search query | | pause_and_memorize_fact | Persist a fact across the trajectory | | ask_user_question | Surface a clarifying question to the user | | wait | Sleep for N seconds | | terminate | End the task with a final answer |

The screen resolution Fara is most commonly trained with is 1440×900. Match this in your sandbox for the most reliable grounding.

Minimal agent loop

# Pseudocode for a single-step interaction....