# VISTA: An End-to-End Benchmark for Visual Spec-to-Web-App Coding Agents

arXiv: https://arxiv.org/abs/2605.26144 · HTML: https://mlsanity.com/paper/2605.26144/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 22 May 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 12 of 12)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering](https://mlsanity.com/paper/2405.15793/) (2024)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [Large Language Models are not Fair Evaluators](https://mlsanity.com/paper/2305.17926/) (2023)
- [OpenHands: An Open Platform for AI Software Developers as Generalist Agents](https://mlsanity.com/paper/2407.16741/) (2024)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [The Stack: 3 TB of permissively licensed source code](https://mlsanity.com/paper/2211.15533/) (2022)
- [Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering](https://mlsanity.com/paper/2403.03163/) (2024)

## Built on by (most cited first, 3 of 3)

- [GameASG-Bench: Benchmarking Autonomous Software Generation for Game Development](https://mlsanity.com/paper/2609.21293/) (2026)
- [ProgramDistill: From Interactive Web Apps to Verifiable Reference-Guided SWE Tasks](https://mlsanity.com/paper/2609.18805/) (2026)
- [GameReplica: A Benchmark for Black-Box Visual Game Replication by Vision-Language Agents](https://mlsanity.com/paper/2609.22308/) (2026)

## Abstract (arXiv)

Coding agents can now build applications from design mockups, but a screen that looks right is not an application that works. Existing benchmarks either score visual reconstruction without a backend or test full-stack functionality from textual requirements, and few tie scores to the individual components a design requires. We introduce VISTA (VIsual Spec-To-App), an end-to-end benchmark in which coding agents turn multi-page design handoffs (Figma renders, structure, and textual requirements) into runnable full-stack Web and Mobile (Android) applications. VISTA provides 8,487 human-annotated interactive components across 18 applications (126 Web pages and 576 Android screens) and an executable evaluator that locates annotated components in the running application and probes their interactions, yielding localization, behavior, and joint scores traceable to individual design requirements. Across 14 deployed coding-agent systems, the best joint score is 0.553 on Web and 0.378 on Mobile, and 13 of 14 systems score lower on behavior than on localization on Web. Component probes detect unresponsive controls in 90.1% of audited Web deliveries. Development traces show where the process departs from the task: 44.5% of trajectories never open the design screenshots, 73.0% omit the prescribed self-audit, and 8.3% report success after a failed check. VISTA makes the gap between rendering a design and delivering a working application measurable at the level of individual components. Code is available at hrefhttps://github.com/kaboider/VISTABench.