Research & Insights

Auker Optimized Itself and Surpassed GPT-5.6 Luna to Redefine a Benchmark Pareto Frontier

Our data shows that up to 70% of AI inference spend is wasted on poor allocation, from using the same heavy machinery for every task. Our agentic model, Auker, fixes that by managing how each query uses AI models, agents, tools, and reasoning through code-as-policy. After 116 experiments, Auker beat GPT-5.6 Luna and Qwen3.7 Plus on MMMU-Pro while spending less. It was the most cost-effective model among those above 80% accuracy. The win came from better allocation of AI capabilities, not a larger model. Now we're bringing that same self-improving loop to enterprise workloads: cost-effective intelligence for the work your teams actually run, turning inference spend into measurable ROI.