Fable 5 vs. GPT-5.6 Sol on an NP-Hard Problem: Does /goal help?
TL;DR: I gave Claude Fable 5 and GPT-5.6 Sol the same unpublished NP-hard optimization problem, with and without their native /goal mode. Fable 5 is an absolute beast; /goal is not a game changer.
Context: This is an operations research problem originally submitted to students at a hackathon. I spent a week years ago writing C++ to solve it, so I have a useful human baseline.
Fable 5 was an absolute beast on this benchmark. It produced the best solution overall, and its consistency is unlike anything I have seen from a model on this problem. This is pure raw intelligence. Incredible.
The other result is that /goal is not a generic “try harder” switch. It changes the control loop and the search path. Sometimes that finds a better basin. Sometimes it gives a bad idea more time to mature.
All code, prompts, result tables, exclusions, and trajectory notes are in CLIArena . This is a follow-up to my first article about this benchmark .
KIRO is a fiber-network design problem I worked on as an engineering student in 2018. Given directed distance matrices for Grenoble, Nice, and Paris, the solver has to connect distribution points and terminals using loops and short chains while respecting several structural constraints. The objective is total cable length. Lower is better.