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AI Code Generation Benchmarks

Real-world performance metrics for AILANG vs Python across multiple AI models.

Model Comparison​

Compare AI model performance across multiple dimensions:

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What These Numbers Mean​

Our benchmark suite tests AI models' ability to generate correct, working code in both AILANG and Python.

Success Metrics​

  • 0-Shot Success: Code works on first try (no repairs)
  • Final Success: Code works after M-EVAL-LOOP self-repair
  • Token Efficiency: Lower tokens = more concise code

Why This Matters​

These benchmarks demonstrate:

  1. Type Safety Works: AILANG's type system catches errors early
  2. Effects Are Clear: Explicit effect annotations help AI models
  3. Patterns Are Learnable: AI models understand functional programming
  4. Room to Grow: Benchmarks identify language gaps and guide development

How Benchmarks Guide Development​

The M-EVAL-LOOP system uses these benchmarks to:

  1. Identify Bugs: Failing benchmarks reveal language issues
  2. Validate Fixes: Compare before/after to confirm improvements
  3. Track Progress: Historical data shows language evolution
  4. Prioritize Features: High-impact failures guide roadmap

Case Study: Float Equality Bug​

The adt_option benchmark caught a critical bug where float comparisons with variables called eq_Int instead of eq_Float. The benchmark suite detected it, guided the fix, and validated the solution.

Result: Benchmark went from runtime_error → PASSING ✅

Try It Yourself​

Want to see AILANG in action?

Methodology: Benchmarks use deterministic seeds across multiple AI models. Each benchmark tests code generation, compilation, and execution. The M-EVAL-LOOP system provides structured error feedback for automatic repair.

Learn More: M-EVAL-LOOP Design | Evaluation Guide