Once AI coding agents reach a high performance level, objective benchmarks become less important than a developer's subjective experience. Like a warrior choosing a sword, the best tool is often the one that has the right "feel," writes code in a preferred style, and integrates seamlessly into a human workflow.

Despite strong benchmark scores placing it near top proprietary models, real-world developer feedback is mixed, with some labeling MiniMax M2.1 a "junior software engineer." This highlights the growing disconnect between standardized tests and a model's practical utility for complex, real-world coding tasks.

Contrary to the belief that AI levels the playing field, senior engineers extract more value from it. They leverage their experience to guide the AI, critically review its output as they would a junior hire's code, and correct its mistakes. This allows them to accelerate their workflow without blindly shipping low-quality code.

Just as standardized tests fail to capture a student's full potential, AI benchmarks often don't reflect real-world performance. The true value comes from the 'last mile' ingenuity of productization and workflow integration, not just raw model scores, which can be misleading.

Simple, function-level evals are a "local optimization." Blitzy evaluates system changes by tasking them with completing large, real-world projects (e.g., modifying Apache Spark) and assessing the percentage of completion. This requires human "taste" to judge the gap between functional correctness and true user intent.

With AI generating code, a developer's value shifts from writing perfect syntax to validating that the system works as intended. Success is measured by outcomes—passing tests and meeting requirements—not by reading or understanding every line of the generated code.

Unlike testing simpler tools, the best way to evaluate a professional-grade AI coding agent is to apply it to your most difficult, real-world problems. Don't dumb down the task; use it on a complex bug or a massive, imperfect codebase to see its true reasoning and problem-solving capabilities.

Current benchmarks focus on whether code passes tests. The future of AI evaluation must assess qualitative, human-centric aspects like 'design taste,' code maintainability, and alignment with a team's specific coding style. These are hard to measure automatically and signal a shift toward more complex, human-in-the-loop or LLM-judged evaluation frameworks.

Since AI assistants make it easy for candidates to complete take-home coding exercises, simply evaluating the final product is no longer an effective screening method. The new best practice is to require candidates to build with AI and then explain their thought process, revealing their true engineering and problem-solving skills.