This article evaluates how new frontier models—Claude Fable 5, Opus 4.8, and GPT-5.5—perform on the long-horizon FrogsGame post-training task. Fable 5 stands out by programmatically generating high-quality supervision traces via a backtracking algorithm, combining them with RL, and reliably improving the base model where others still struggle with noisy data and evals.

• Claude Fable 5 won by generating correct solution traces via a backtracking algorithm rather than fine-tuning on the base model's noisy outputs, then combining that SFT data with RL within a 20-hour budget.
• Opus 4.8 tripled its predecessor's performance (30.9% pass@4) and GPT-5.5 reached 9.7%, but both still relied on the base model's flawed self-generated data rather than an independent correct-answer source.
• Fable 5's self-assessment was far better calibrated (1.2x overconfidence vs. Opus 4.8's 4.9x) and it used nearly the full time budget while producing 3x more diverse training data.
• Evaluation itself remains a weak point across all models—small sample sizes and noisy metrics make it hard to reliably judge real progress on the task.