The 10-Task Gauntlet: Measuring a Free Coding Model Before You Trust It

Description and use

The gauntlet reveals three main failure modes in free models: nondeterminism (different code for same prompt), context loss (degradation in multi-file tasks), and close-but-wrong output (plausible but failing code). It provides a quantitative scorecard to determine if a model is suitable for specific task types.

Problem

Most AI model evaluations rely on single demos ('vibes-based evaluation'), which fail to reveal variance, context limits, or reliability in real-world codebases. This leads to broken builds and unmeasured risks when integrating AI into development workflows.

Outcome

The gauntlet reveals three main failure modes in free models: nondeterminism (different code for same prompt), context loss (degradation in multi-file tasks), and close-but-wrong output (plausible but failing code). It provides a quantitative scorecard to determine if a model is suitable for specific task types.

Workflow

• Select ten tasks from real workload covering three classes: Greenfield (write function/test/CLI), Refactor (rename/extract/split/migrate), and Debug (fix test/null bug/race condition/slow query).
• Define one behavioral pass criterion per task (e.g., 'unit test passes', 'CLI exits 0').
• Create an assertion script for each task that checks behavior, not wording.
• Run each task three times using the provided bash harness or custom adapter.
• Record verdict (pass/fail), wall time, and token usage for each run.
• Calculate median time and pass rate.
• Apply the decision table: >=8/10 pass rate for general use, 5-7/10 for greenfield only, <5/10 for explanation/chat only.

Evidence

Most model reviews quote one demo. One demo proves one thing. It proves nothing about your codebase.

Source

Pass rate 8/10 or higher. The free tier fits your task mix. Review diffs as usual.

Source

Three failure modes show up fast. Nondeterminism. Context loss. Close-but-wrong output.

Source