How We Score AI Coding Tools: A Deep Dive Into Our Methodology
An inside look at the evaluation framework behind StackQuadrant's tool ratings — from benchmark tasks to weighted scoring across six dimensions.
When we set out to build StackQuadrant, we faced a fundamental question: how do you objectively compare tools that are evolving weekly, serve different use cases, and operate in fundamentally different ways?
The Problem With Subjective Rankings
Most "best AI coding tools" lists are based on the author's personal experience with 2-3 tools. They don't test systematically, they don't control for variables, and they rarely update their conclusions. We wanted something more rigorous.
Our Six Dimensions
Every tool is scored across six dimensions, each chosen because it maps to a concrete capability that developers care about:
- Code Generation (18.3%) — Can it write correct, idiomatic code?
- Context Understanding (18.3%) — Does it understand your codebase, not just the current file?
- Developer Experience (18.3%) — Is it pleasant to use daily?
- Multi-file Editing (16.5%) — Can it coordinate changes across multiple files?
- Debugging & Fixing (16.5%) — Can it find and fix bugs effectively?
- Ecosystem Integration (14.7%) — Does it work with your stack?
What Makes a 9 vs. a 7?
A score of 9.0+ means the tool is exceptional in that dimension — it handles edge cases, follows project conventions, and produces results that require minimal human intervention. A 7.0 means it's strong but has notable gaps. Below 5.0, the tool has significant limitations that affect daily usability.
Every score includes an evidence field explaining why a tool received that rating. We don't hide behind numbers.
Quarterly Re-evaluation
AI coding tools ship updates constantly. A tool that scored 7.5 in Q4 2025 might be an 8.5 by Q1 2026 thanks to a major model upgrade or feature release. We re-evaluate every quarter, and significant releases trigger out-of-cycle updates.