🔹 **Enhanced Agentic Capabilities:** Open-source SOTA in Agentic Coding benchmarks.

GPQAD | 94.4 | 90.9 | 92.7 | 92.4 | **95.1** | LCBv6 | 90.3 | 92.1 | 92.4 | 90.4 | **93.2** | SWEPro | 48.4 | 51.2 | _53.4_ | 51.3 | **54.2**

With only 27B parameters, it outperforms the Qwen3.5-397B-A17B (397B total / 17B active) on every major coding benchmark

Multiple community tests show llama.cpp running 1.8x faster than Ollama on the same hardware with the same model, 161 tokens per second versus 89.

The 66.6% medal rate on MLE Bench Lite, achieved autonomously over 24 hour windows, tells you something real about how this model behaves when you give it a hard problem and step back.

We improved Claude's RCA accuracy by 12pp on OpenRCA

NVIDIA yields unmatched inference throughput across the broadest range of workloads, from massive LLMs to advanced vision language models, to generative recommender systems and more, on industry-standard benchmarks.