Poolside AI released Laguna S 2.1, a 118-billion-parameter mixture-of-experts model with only 8 billion active parameters per token and open weights on Hugging Face. The model achieved 70.2% on Terminal-Bench 2.1 and 78.5% on SWE-bench Multilingual, reportedly outperforming Deepseek v4 Flash at lower cost. If benchmark claims hold, it could become a leading open-source model in the 120B class and influence competitor release schedules.
Poolside AI, co-founded by Eiso Kant, released smaller models like Laguna S 2.1 that outperform much larger competitors, backed by a systematic engineering approach called the Model Factory. The company completes model cycles in 8 weeks while running 10,000–20,000 experiments monthly across fewer than 70 researchers, using techniques like streaming data directly into training and low-precision compute. This efficiency enables Poolside to compete as an independent open-weights model company rather than consolidating into an AI oligopoly, shifting the focus from raw model scale to engineering rigor and data efficiency.
Together released a production inference platform for running open-weight AI models with features including deployment profiles, traffic-based autoscaling, canary/blue-green rollouts, and A/B testing capabilities. The platform achieves approximately 4x faster warm starts for frontier models and supports deployment times ranging from 2–14 minutes depending on model size. Companies can now safely iterate on model versions in production without building custom infrastructure or changing their application layer.
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