Output Format x Model Identity: Interaction Effects in Single-Round Coding Agent Performance

Output format is not a neutral implementation detail -- it can reorder model rankings, amplify or suppress individual model differences, and determine whether a coding agent succeeds or fails. We conducted a controlled single-round experiment with 3 models (DeepSeek V4, Doubao 2.0 Pro, Qwen 3.7 Max) x 3 output formats (full file, JSON Patch, unified diff) x 6 tasks x 20 repetitions, totaling 4,013 runs across 4 open-source projects. Only one project (tqdm) yielded non-zero success rates: dotenv, requests, and jsoup yielded zero successes in 2,551 runs. Our central finding is a format x model interaction with no universally optimal format. Doubao achieves 94% success with JSON Patch (Cohen's h = 1.57, p < 0.001), DeepSeek excels at unified diff (66%, h = 0.63), and Qwen shows a small but significant full-file preference (50%, h = 0.29, p < 0.05). Beyond these headline results, we identify a distinct failure mechanism -- format misuse -- where agents correctly diagnose a problem but execute it with excessive scope, most vividly when a one-line fix is applied as a full-file replacement. We propose a model-specific output strategy, a tool-design principle that constrains format semantics to the agent's own localization step, and release all data and templates for reproducibility. All experimental data are available at https://doi.org/10.5281/zenodo.21505157.