Meta’s Muse Wonder agent ran Pokémon Red on launch day and failed to beat Brock, getting stuck in Viridian Forest after beginning the forest on turn 208. The run cost $96.06 after about 792 turns (roughly 6 hours) without clearing the first gym. The result shifts attention from benchmark wins to real-world reliability and highlights pricing gaps versus smaller, cheaper model runs that completed the gym.
Memento 3, a frozen-LMM agent, continually updates a natural-language rulebook by observing environment transitions, revising hypotheses, compiling executable prediction/planning code, and verifying code fidelity via replay. It reached a mean Relative Human Action Efficiency (RHAE) of 100.0 on ARC-AGI-3, clearing all levels across 25 public games while using 44% of the human action count. This changes learning by shifting from fixed behavior to validated, recursive self-improvement of an explicit world model without changing the underlying LLM.
Artificial Analysis benchmark standings place Opus 5.5 slightly ahead of Wonder Full on the index reported by VentureBeat. Opus 5.5 scores just above Wonder Full on that Artificial Analysis benchmark. Wonder Full is limited to Muse Code plans with no token API access, while Opus 5.5 has comparatively broader availability.
Artificial Analysis benchmarked Meta’s Muse Wonder and Muse Wonder Full against other top models and found Muse Wonder Full slightly ahead of Anthropic’s Claude Fable 5.1 on the Intelligence Index. Muse Wonder Full scored 56 points, while Muse Wonder scored 52. Meta’s models are positioned as better at agent-style tasks and have lower hallucination rates, but Muse Wonder’s higher per-token pricing (including a higher cost than its predecessor) offsets some of the value.
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