---
title: "Microsoft's Sico-Evolution Beats GPT-5.4 at GUI Tasks With a Tiny 4B Open Model"
subtitle: "Microsoft's Sico-Evolution doubles task success rate to 82.9% on a 4B model, beating GPT-5.4 and Claude Opus 4.7 on Edge and Copilot UI tasks"
url: "https://alphasignal.ai/news/microsoft-s-sico-evolution-beats-gpt-5-4-at-gui-tasks-with-a-tiny-4b-open-model"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "AlphaSignal"
company_url: "https://alphasignal.ai/company/alphasignal"
published: "2026-06-30T12:46:05.000Z"
updated: "2026-07-09T02:45:30.034Z"
type: "Model"
topics:
  - "Agents"
  - "Open Source"
subtopics:
  - "Computer Use"
upvotes: 185
source_url: "https://huggingface.co/microsoft/GELab-Zero-4B-preview-Sico-Evolution"
comments: 0
truncated: true
---

# Microsoft's Sico-Evolution Beats GPT-5.4 at GUI Tasks With a Tiny 4B Open Model

> Microsoft's Sico-Evolution doubles task success rate to 82.9% on a 4B model, beating GPT-5.4 and Claude Opus 4.7 on Edge and Copilot UI tasks

GUI agents -- AI systems that can look at a screen and autonomously click, type, and navigate software -- have been a frontier mostly dominated by large, expensive proprietary models. Microsoft just flipped that script. The team behind [Project Sico](https://www.microsoft.com/en-us/research/project/sico/) released [GELab-Zero-4B-preview-Sico-Evolution](https://huggingface.co/microsoft/GELab-Zero-4B-preview-Sico-Evolution), a 4-billion-parameter GUI agent that outperforms GPT-5.4, Claude Opus 4.6, and Claude Opus 4.7 on real Microsoft Edge and Copilot UI tasks -- all while being fully open-source under Apache 2.0.

## From 39.8% to 82.9% in one pipeline

Sico-Evolution achieves a dominant 82.9% Task Success Rate, a massive +43.1% absolute surge over the 39.8% base-model baseline. To put that in perspective, it edges out top proprietary giants like GPT-5.4 (79.7%), Claude Opus 4.6 (81.3%), and Claude Opus 4.7 (82.1%). It also vastly exceeds leading open-source competitors including kimi-k2.6 (62.6%) and UI-Venus-1.5-30B (61.0%).

The benchmark here is Task Success Rate (TSR) on real Edge and Copilot UI trajectories -- meaning the agent is scored on whether it actually completes a task end-to-end, not just whether individual clicks look right. That's a much harder bar than static localization benchmarks, and it's the one that matters for real deployments.

## What's actually powering this jump

Sico-Evolution is a 4B GUI agent fine-tuned with LoRA from the open-source GELab-Zero-4B-preview base model on Microsoft Edge and Copilot UI trajectories, built with a general-purpose GUI model evolution pipeline -- an iterative mechanism that keeps lifting an agent's real task success rate round after round, and transfers to any GUI app.

The base model itself, [GELab-Zero-4B-preview](https://huggingface.co/stepfun-ai/GELab-Zero-4B-preview), is built on Qwen3-VL-4B and was developed by StepFun's GELab team. It already had strong GUI fundamentals: GELab-Zero-4B-preview achieves 75.86% success rate on AndroidWorld testing, demonstrating excellent performance on complex mobile tasks. Microsoft's Sico pipeline then takes that foundation and evolves it specifically for Windows-native UI environments.

_Preview only. Full text requires an AlphaSignal Pro account._

## Sources

- [microsoft/GELab-Zero-4B-preview-Sico-Evolution · Hugging Face](https://huggingface.co/microsoft/GELab-Zero-4B-preview-Sico-Evolution)
- [Project Sico - Microsoft Research](https://www.microsoft.com/en-us/research/project/sico/)
- [stepfun-ai/GELab-Zero-4B-preview · Hugging Face](https://huggingface.co/stepfun-ai/GELab-Zero-4B-preview)
- [GELab-Zero GitHub Repository](https://github.com/stepfun-ai/gelab-zero)
- [Agentic Evolution Survey - Microsoft Research](https://www.microsoft.com/en-us/research/wp-content/uploads/2026/07/agentic-evolution.pdf)