此显著的成本削减可归功于多个技术因素的协同作用，包括算法效率的提升、模型构建流程的优化、硬件加速器的部署、专用芯片的应用，以及基于具体任务场景的计算资源合理配置。Gartner副总裁兼杰出分析师强调了这些因素的共同影响。
The compact models exceed expectations for their scale. The E4-billion reaches 42.5% on AIME 2026 and 52.0% on LiveCodeBench—impressive for a model operating on a T4 GPU. The E2-billion, even smaller, achieves 37.5% and 44.0% respectively. Both substantially surpass Gemma 3 27B (without reasoning) on most benchmarks despite being considerably smaller, attributable to integrated reasoning capacity.，更多细节参见网易邮箱大师
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Georgia Furness