TLDRocket
23 July 2026
Poolside AI's release of Laguna S 2.1 this week crystallizes a broader shift in how the AI industry measures progress. The 118-billion-parameter mixture-of-experts model achieves 70.2% on Terminal-Bench 2.1 and 78.5% on SWE-bench Multilingual—outperforming Deepseek v4 Flash while costing less—by activating only 8 billion parameters per token. What matters isn't the raw parameter count but the engineering discipline behind it. Co-founder Eiso Kant's "Model Factory" approach completes full training cycles in eight weeks while running 10,000 to 20,000 experiments monthly across fewer than seventy researchers, using techniques like streaming data directly into training pipelines and low-precision compute. This efficiency model suggests the era of "bigger is better" may be ending, replaced by one where smaller teams with systematic rigor can compete against well-funded incumbents.
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