The AI Productivity Index for Software Engineering
The AI Productivity Index for Software Engineering (Ficus-Code) measures whether frontier AI agents can execute real-world software engineering tasks across integration, debugging, and observability.
The Ficus-Code leaderboard
We created Ficus-Code to evaluate agents on real-world software engineering — not synthetic coding puzzles. The tasks require agents to navigate complex codebases, understand system architecture, debug production issues, and ship working features.
Ficus-Code was built with senior engineers who created realistic scenarios spanning system integration, observability, and full-stack development. Each task mirrors the complexity of actual production work.
The benchmark evaluates both functional correctness and code quality, ensuring that AI models can produce production-ready software.
Model
Score
Opus 5.5Max
67.6% ±4.8%
Sonnet 5.5Max
66.4% ±4.9%
Opus 5Max
63.7% ±5.1%
Gemini 4 ArgonHigh
61.2% ±5.3%
Gemini 3.7 FlashHigh
58.9% ±5.5%
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System Integration
Tests AI on integrating APIs, microservices, and third-party libraries into existing codebases. Evaluates understanding of system boundaries and data flow.
Debugging & Observability
Evaluates AI ability to diagnose production issues using logs, traces, and metrics. Includes root cause analysis across distributed systems.
Full-Stack Development
Assesses AI on end-to-end feature development from database schema design through API implementation to frontend rendering and testing.
Frequently asked questions
What is the Ficus-Code benchmark?
Ficus-Code is a benchmark that measures how effectively AI models perform real-world software engineering tasks. It evaluates production-grade work across system integration, debugging, and full-stack development.
How does Ficus-Code differ from other SWE benchmarks?
Ficus-Code focuses on production-grade software engineering that mirrors actual developer workflows — not just isolated coding puzzles. Tasks require reasoning across multiple files, understanding system architecture, and producing deployable code.
Who creates the Ficus-Code tasks?
Senior software engineers and tech leads from production environments create the tasks, ensuring they reflect the complexity and nuances of real-world development work.
How are models evaluated?
Ficus-Code evaluates whether the generated code is functionally correct, properly integrated, and follows best practices. Expert-authored test suites and rubrics measure correctness, code quality, and architectural soundness.
Can I reproduce results?
Yes, on the open subset. The eval harness and sample tasks are available for independent verification. The full task set stays private to prevent training contamination.