Poolside's Laguna S 2.1 spirals on a car-wash prompt

Poolside's Laguna S 2.1 spiraled into an endless reasoning loop over a trivial prompt about walking 69 meters to a car wash, per a widely-shared LocalLLaMA thread.

That matters more than any benchmark score for builders chaining this model into agent workflows or paying per token. A loop that won't terminate costs money and time before it costs accuracy.

The thread, the model's first sustained hands-on test since launch, pulled 70 upvotes and 75 comments. Its poster wasn't dismissive. They called it a solid model that could lead the roughly 120B class outright once Poolside fixes what they described as overthinking loops.

Not every test went sideways. One practitioner used the model to solve a data-restructuring problem that had eaten days of their time. That's the kind of result a benchmark score promises but rarely proves.

Both reports describe the same model: strong on genuinely hard problems, undone by reasoning it can't switch off on easy ones.

Laguna S 2.1 runs 118B total parameters with 8B active and handles a 1M token context, scoring 70.2% on Terminal-Bench 2.1. NVFP4 weights let it fit on a single DGX Spark. That's one machine for a model this size, not a GPU rack.