Agentic systems often reason across screens, documents, audio, video, and text within a single perception‑to‑action loop. However, they still rely on fragmented model chains—separate stacks for vision, audio, and text. This increases inference hops and orchestration complexity, driving up inference costs while weakening cross-modal context consistency.
NVIDIA Nemotron 3 Nano Omni, a new addition to the Nemotron 3 family, brings unified multimodal reasoning into a single, highly efficient open model. Built to replace fragmented vision‑language‑audio stacks, Nemotron 3 Nano Omni functions as the multimodal perception and context sub‑agent within agentic systems.
With this, agents can perceive and reason across visual, audio, and textual inputs within a single shared perception‑to‑action loop, improving convergence and reducing orchestration complexity and inference cost.
It delivers best-in-class accuracy on document intelligence leaderboards such as MMlongbench-Doc and OCRBenchV2, while also leading in video and audio understanding, WorldSense, DailyOmni, and VoiceBench.
Beyond accuracy, MediaPerf—an open industry benchmark that evaluates video understanding models on real media data and production tasks across quality, cost, and throughput—shows Nemotron 3 Nano Omni achieving the highest throughput across every task and the lowest inference cost for video-level tagging. Read this post to learn more.
