Datasets:
deep-dive benchmark: 8 prompts used to test gpt-oss-20b
after gpt-oss-20b won the initial model comparison (portscout + logpulse), we ran 8 additional prompts to stress-test reliability across different coding skills. all prompts were run through Pi coding agent with gpt-oss-20b Q4_K_M, llama-server ncmoe=30, 32K context, on an RTX 4060 Ti 8GB.
here are all 8 prompts, verbatim.
prompt 1: bug fix (easy)
the file contained a deliberate bug: instead of . tests whether the model can read existing code, identify a real bug, and fix it.
result: PASS. 13% context used, 0 self-fixes.
prompt 2: feature addition (medium)
tests modifying existing code, adding CLI arguments, integrating new stdlib modules, and testing multiple features.
result: PASS. context compacted during task, 2 self-fixes (hallucinated json.stdout, edit tool matching failures).
prompt 3: TDD (medium)
tests TDD workflow: create implementation + tests, iterate until green.
result: PASS. context compacted, 2 self-fixes (used eval() shortcut, ls -R ~ dumped 11K lines).
prompt 4: data pipeline (medium)
tests ability to parse real system files, handle structured data, and produce clean JSON output.
result: PASS. 19.7% context used, 0 self-fixes. cleanest run.
prompt 5: hallucination trap (easy)
trap: does the model hallucinate fake convenience modules (like a requests-style API) or use real stdlib (http.client, json, time)?
result: PASS. 6.3% context used, 0 self-fixes. no hallucinated modules, used real stdlib only.
prompt 6: error recovery (medium)
tests how the model handles deliberate failure (file does not exist), recovers gracefully, creates sensible defaults, and validates its own output.
result: PASS. 16.8% context used, 1 self-fix (sed quoting bug when parsing YAML via bash).
prompt 7: multi-module project (hard)
hardest prompt: three interconnected files, topological sort algorithm, config parsing, and tests that verify correctness of dependency graph logic.
result: PASS. 47.6% context used, 1 self-fix (topological sort direction was backwards, fixed with reversed()).
prompt 8: bash scripting (medium)
tests bash fluency, git command knowledge, and whether the model can find and use a real git repo to validate.
result: PASS. 23% context used, 1 self-fix (printf format string missing %s placeholder).
summary
| # | task | difficulty | context used | self-fixes |
|---|---|---|---|---|
| 1 | bug fix | easy | 13% | 0 |
| 2 | feature add | medium | compacted | 2 |
| 3 | TDD | medium | compacted | 2 |
| 4 | data pipeline | medium | 19.7% | 0 |
| 5 | hallucination trap | easy | 6.3% | 0 |
| 6 | error recovery | medium | 16.8% | 1 |
| 7 | multi-module | hard | 47.6% | 1 |
| 8 | bash scripting | medium | 23% | 1 |
8/8 pass. 7 self-fixes total. no hallucinated APIs.
the prompts are designed to cover: bug fixing, feature addition, test-driven development, data parsing, API hallucination detection, error recovery, multi-file architecture, and bash scripting. feel free to reuse them for testing other local models.