dynaconf on gpt-5.6-luna: the first clean solve with both doors shut

This is one run: the real codex CLI on the user's subscription, model gpt-5.6-luna at high reasoning, on dynaconf__dynaconf-1225 from the swebench-live tier, under the same both-doors-closed harness as the gpt-5.5 run. It passed, cleanly. It is the first model in this whole comparison to solve dynaconf-1225 with no answer to look up.

Every earlier run on this task either failed honestly or passed by fetching the answer. gpt-5.6-luna is the run where a model actually derives the fix from the code and the tests go green.

Reproducibility

Run captured	2026-07-13, on the user's codex subscription
Tool	real codex CLI under -s workspace-write (shell has no network)
Model	gpt-5.6-luna, high reasoning effort
Task	dynaconf__dynaconf-1225, dynaconf at base commit 39acdee, history pruned, graded in a Python 3.12 venv
Verdict	PASS, clean. No answer fetch over network or git history
Result	FAIL_TO_PASS green, in-file PASS_TO_PASS stable. The two module-path settings-loader tests that broke every failing model here are green
Cost	10,120,493 tokens (input 10,081,964 at 98% cache hit, output 38,529 with 15,933 reasoning), 80 tool calls, 25 files touched, 880s, $1.4605 at gpt-5.6-luna API list price

Priced from the shared pricing table: $0.2457 uncached input, $0.9836 cache read, $0.2312 output.

What it did

luna took the same reading of the task every model here took. The bug is that a loader cannot be pointed at a named file, and the fix threads an identifier through the loader stack. So luna wrote the broad refactor: an identifier through the format loaders (toml, yaml, json, ini, redis, env), the loader dispatch, base.py, and on into cli.py, validator.py, and parse_conf.py. Twenty-five files touched, right in the range of the nineteen-edit gpt-5.5 run and the twenty-two and twenty-three-file claude runs.

Breadth is not what separated it. What separated it is that luna's version of the refactor handled the module-path loader variant. The tests that stayed red for gpt-5.4-mini, gpt-5.5, sonnet, and opus were test_load_using_settings_loader_with_one_env_named_file_module_path and its _multi_env sibling. Every failing model left those two red because their identifier plumbing did not carry through the module-path load. luna's did, so on grading the fail-to-pass targets turned green and nothing that was green went red.

Why it is the finding

The earlier open-door runs could not tell capability from network access, because a strong model would just fetch the merged pull request and "pass". With both doors shut, the leaderboard finally measures the fix on its merits, and on the merits dynaconf-1225 turned out to be a task nothing in the comparison could solve until this run.

gpt-5.6-luna is the model that clears the bar. It is also, and this is the part worth sitting with, the cheapest model in the group. It solved for $1.46 while gpt-5.5 spent $4.49 to fail, sonnet spent $10.32 to fail, and opus spent $47.18 to fail and break a test that started green. On this task the money did not buy the fix. The newer model did.

This is the honest bar tomo is measured against, and it is a real bar now. A capable scaffold on a capable model can solve dynaconf-1225 from the code alone. The tomo work that matters is making sure tomo, on whatever model, reaches for the fix cleanly and does not run away digging for the answer or ship a broad edit that breaks working behavior.

Reproduce it

bash ~/data/evals/codex-real/run_offline.sh dynaconf__dynaconf-1225 gpt-5.6-luna high
# Expect: VERDICT: PASS and fairness: CLEAN.