No synthetic benchmarks - only hands-on tests in real repositories. We test frontier models (Claude Opus/Fable, GPT-5.6 Sol, Kimi k3, GLM-5.3, Grok 4.6/4.7) against production codebases.

Core Engineering Pillars

Hands-on Repository Testing

Evaluating models on full-stack web, 3D graphics (Three.js), and backend microservices.

Price-to-Performance Ratios

Identifying which models excel at routine refactoring vs complex architectural reasoning.

Developer Toolchain Audits

Testing terminal tools, IDE extensions, and browser-automation engines.

  1. 1. Match Model to Task Tier

    Use fast/cost-effective models for generation and frontier models for reasoning & architecture.

  2. 2. Test with Real Codebases

    Never rely on public leaderboards; benchmark on your own internal test harness.

Frequently Asked Questions

What makes a good coding LLM for terminal agents?

Low latency, high instruction following fidelity, concise tool calling, and resilient recovery when compiler errors occur.

Field Reports & Case Studies

AI Dev Tools Articles (26)