Microsoft's Free AI Just Beat OpenAI and Google at Browsing the Web

TL;DR

Microsoft's Fara1.5-27B model scored 72% on Online-Mind2Web, outperforming OpenAI's Operator and Google's Gemini 2.5. The Fara1.5 models, available in various sizes, promise advanced browsing capabilities without special plugins.

Key points

Fara1.5-27B scored 72% on Online-Mind2Web
Beats OpenAI Operator and Gemini 2.5
Models come in 4B, 9B, and 27B parameter sizes
Built on fine-tuned Qwen 3.5
Fara1.5-9B is live on Azure AI Foundry

Mentioned in this story

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In brief

Fara1.5-27B scored 72% on Online-Mind2Web, beating OpenAI Operator (58.3%) and Gemini 2.5 Computer Use (57.3%).
The models are open-weight, come in 4 billion, 9 billion, and 27 billion parameter sizes, and are built on fine-tuned Qwen 3.5.
Fara1.5-9B is live now on Azure AI Foundry; 4B and 27B arrive shortly.

Imagine telling your computer to look up vacation rentals, compare five sites, fill out the booking form, and confirm the one closest to the beach. You go make coffee. It’s done when you get back. That is the promise of "computer use agents"—AI that reads your browser screen and clicks, scrolls, and types exactly as a human would, with no special plugins required.

OpenAI tried this first with Operator, launched in January 2025 at $200 a month before being folded into ChatGPT Agent and shut down in August. Google has Gemini 2.5 Computer Use. Both are proprietary, cloud-based, and expensive to run.

This week, Microsoft Research released a tiny model named Fara1.5—and on the benchmarks that count, it beats them both.

The family comes in three sizes: 4 billion, 9 billion, and 27 billion parameters, all built on Qwen3.5, an Alibaba base model that Microsoft fine-tuned for browser work, with all weights publicly released. (Parameters are what determine an AI model's breadth of knowledge, with more generally meaning a higher capacity.)

Getting there required rethinking the whole development process from scratch. "We started with a simple question: What does it take to make a small model genuinely good at agentic tasks?" the AI Frontiers team wrote. "The answer spanned the full lifecycle—data generation, training objectives, model design, and orchestration had to be redesigned together rather than in isolation."

The benchmarks

Online-Mind2Web is the benchmark that matters in the task Microsoft wanted to excel. It tests how often an AI agent correctly completes 300 diverse, real-world tasks across 136 popular live websites—things like comparing products, filling forms, and booking services—scored as a percentage of tasks finished correctly on the actual, changing internet.

Fara1.5-27B scored 72%. OpenAI Operator scored 58.3%. Google's Gemini 2.5 Computer Use scored 57.3%. Yutori's Navigator n1, the top proprietary alternative, reached 64.7%. Even Fara1.5-9B, the mid-sized model, hit 63.4%—ahead of both OpenAI and Google.