Most multimodal pipelines collapse the moment you feed them a 200-page PDF alongside a recorded client call and a screen capture.

Stitching together four separate AI tools for one workflow is costing teams hours they do not have

Analysts and engineers currently juggle separate models for OCR, speech transcription, video summarization, and document reasoning. Every handoff introduces error, latency, and a new API bill.

One model now reads the document, listens to the audio, and watches the screen

Introducing NVIDIA Nemotron 3 Nano Omni accepts dense documents, multi-image inputs, long-form video, and raw audio in a single inference call, then outputs structured reasoning across all four modalities. You load your files or streams, pass them to the model via the HuggingFace checkpoint in BF16, FP8, or NVFP4 precision, and receive a unified response. The architecture pairs a Mamba-Transformer Mixture-of-Experts backbone with dedicated vision and audio encoders, so no modality is an afterthought bolted on after training.

The teams drowning in mixed-format evidence feel this first

• Legal operations analysts who spend days cross-referencing deposition audio against exhibit PDFs can get a single reasoned summary in one pass.
• Enterprise AI engineers building document agents who need one open checkpoint they can actually audit, fine-tune, and deploy on-premise without a vendor lock-in clause.
• Media and compliance teams who must timestamp claims in recorded meetings against written policy documents and need a model that scores at the top of video understanding benchmarks without a proprietary API.

NVIDIA published benchmark results showing Nemotron 3 Nano Omni leads MMlongbench-Doc, OCRBenchV2, WorldSense, DailyOmni, and VoiceBench simultaneously, which no single open model has done before. GPT-4o and Gemini 1.5 Pro have held this space commercially, and an open alternative that matches their cross-modal accuracy shifts the build-vs-buy calculation for any team with a GPU cluster.

What you can actually do with it today

• Extract structured data from dense multi-column financial PDFs at scale.
• Transcribe and summarize hour-long audio recordings with document context attached.
• Run agentic computer-use tasks that require reading both screen state and typed documents.
• Query long surveillance or instructional video for specific timestamped events.

Open weights, available now on HuggingFace. Pricing not listed for hosted inference — check our directory.

Nemotron 3 Nano Omni is open weights, which means your data stays on your infrastructure, but it also means you absorb the operational cost of running a large MoE model in production.

If you need a fully hosted multimodal API today, Gemini 1.5 Pro covers similar input types with no infrastructure burden. For document intelligence specifically, Qwen2.5-VL is a leaner open alternative with strong OCR benchmarks but no native audio support.

Open multimodal models are closing the gap on proprietary APIs faster than most teams expected

This release is a signal that the window for justifying closed-model costs on mixed-format enterprise data is narrowing. We cover tools like this every Friday — subscribe here and we’ll send the best ones straight to you.