Interactive Research Lessons
Learn why text-based AI agents outperform visual agents by 2-5x in speed with 50-70% fewer errors
Research Sources
1. VisualWebArena Benchmark (ACL 2024)
Finding: Visual agents achieved only 16.4% task success vs. 88.7% for humans, while text-based coding agents (Claude 4) achieved 72.5% success on SWE-bench.
Source: Association for Computational Linguistics Conference 2024
2. SWE-bench Software Engineering Benchmark
Finding: Claude Opus 4 achieved 72.5% success rate, Claude Sonnet 4.5 achieved 77.2%, demonstrating consistent superiority of text-based agents across real-world engineering tasks.
Source: Anthropic Engineering (2025) • Official report
3. OpenAI & Anthropic Computer Use Benchmarks (2025)
Finding: OpenAI's Operator agent: 38.1% success on OSWorld tasks vs. 72.4% for humans. Anthropic's Computer Use: 22% success rate. Both significantly lag text-based agent performance, confirming visual automation limitations.
Source: OpenAI (2025) • Computer-Using Agent • WorkOS Comparison
4. Vision Models Context Understanding (ICML 2024)
Finding: AI vision models fundamentally struggle with context understanding, misidentifying objects in unfamiliar settings - demonstrating inherent limitations of visual processing that cannot be overcome through training alone.
Source: Tomaszewska, P., & Biecek, P., ICML 2024 • arXiv paper • ICML proceedings
5. Text-to-SQL Benchmark Performance (2024-2025)
Finding: Current LLMs achieve 57-80% accuracy on text-to-SQL benchmarks (Spider, BIRD-SQL), with best approaches reaching 85%+ using decomposition techniques. Performance demonstrates text-based agents can effectively query databases programmatically.
Source: Spider Benchmark, BIRD-SQL, Databricks Research (2024-2025) • State of Text2SQL 2024
6. AI Model Pricing and Cost Efficiency (2025)
Finding: Text-based API processing costs $3-5 per million input tokens (GPT-4o, Claude Sonnet). Vision models process images at same token rates but consume 700-1,100 tokens per image, making text-based approaches more cost-effective for high-volume automation.
Source: OpenAI Pricing, Anthropic Pricing (2025) • OpenAI Pricing • Claude Pricing
7. "API Agents vs. GUI Agents: Divergence and Convergence" (2025)
Finding: Comprehensive paper advocating for hybrid architectures, confirming that API agents excel with programmatic interfaces while GUI agents provide universality but lower performance.
Source: Academic research paper, 2025
Last updated: January 2025
For the complete technical analysis, see our comprehensive research document.
Ready to Experience the Difference?
Quallaa gives AI agents the text-based, programmatic environment where they can achieve that 2-5x performance advantage. Start building with the power of developer infrastructure and the simplicity of Notion.