Benchmarking EngGPT2-16B-A3B against Comparable Italian and International Open-source LLMs
This report benchmarks the performance of ENGINEERING Ingegneria Informatica S.p.A.'s EngGPT2MoE-16B-A3B LLM, a 16B parameter Mixture of Experts (MoE) model with 3B active parameters. Performance is investigated across a wide variety of representative benchmarks, and is compared against comparably-sized open-source MoE and dense models. In comparison with popular Italian models, namely FastwebMIIA-7B, Minerva-7B, Velvet-14B, and LLaMAntino-3-ANITA-8B, EngGPT2MoE-16B-A3B performs as well or better on international benchmarks: ARC-Challenge, GSM8K, AIME24, AIME25, MMLU, and HumanEval (HE). It achieves the best performance for the longest context setting (32k) of the RULER benchmark. On the ...
| Authors | Andrea Sassella, Andrea Chizzola, Tommaso Bianchi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 May 2026 |
| Last updated at source | 20 May 2026 |
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