How the channel uses LFGBenchLFGBench is the hands-on coding benchmark the Every team runs to see how much of a planned build a model can actually carry through on its own — the plan → build → test loop described below.### What it measuresThe structure is simple and demanding:- A model is given four tasks.- For each, it must plan, build, and test in a loop until done.- The score is the share of planned features actually completed.This makes LFGBench a test of long-horizon follow-through, not single-shot cleverness: can the model keep a whole feature set moving without abandoning it or drifting?### What it revealedIn the Opus 4.6 vs. Codex 5.3 head-to-head, the results split sharply:- Opus 4.6 completed 100% of planned features.- Codex 5.3 completed around 64%.The team's read: Codex thrives on precise specifications, while Opus is better at filling in the blanks — with the risk of occasionally doing the wrong thing confidently.In the same stream, Kieran Claassen described his personal /LFG command, which runs a similar loop; with Anthropic's agent swarms enabled, its runtime dropped from 20 minutes to 5 — though he noted swarms shine in implementation, where the plan is clear, less so in planning phases.### Key takeaways- LFGBench = plan → build → test in a loop until done, across four tasks, scored by completed features.- It favors models with long-horizon stamina and willingness to fill in gaps.- Pair it with other checks — speed, variance, design, writing — since a high LFGBench score alone doesn't capture a model's consistency.