For document workflows that need visual understanding, Nvidia’s Nemotron 3.5 Super VL is the more directly aligned option. For text-based coding, reasoning and long-horizon agent tasks, Reflection AI’s Beam is the closer fit. The useful first question is what kind of input your business workflow handles.
Two models, different business tasks
Nvidia describes Nemotron 3.5 Super VL as a 120-billion-parameter multimodal model for visual understanding, tool use and multistep workflows. Its stated document tasks include reading PDFs, webpages, tables and charts. Reflection’s model card describes Beam as text-only, with an emphasis on coding, reasoning and agentic work.
| Model | Stated business focus | Modality | Stated parameters |
| Nvidia Nemotron 3.5 Super VL | Visual understanding, tool use and multistep workflows | Multimodal | 120 billion |
| Reflection AI Beam | Coding, reasoning and agentic work | Text input and text output | 501.3 billion total; 23.4 billion active |
For a workflow that needs to interpret a chart inside a PDF, Nemotron’s described visual capability is directly relevant. For text-based tasks such as coding or reasoning, Beam’s stated focus is a closer match. Those distinctions can help narrow a shortlist without treating parameter totals as a quality score.
Beam uses a sparse mixture-of-experts architecture. Its model card lists a one-million-token context window, says the model was developed predominantly for English, and gives June 30, 2026, as its knowledge cutoff. Those details matter when the task involves long inputs or relies on language and information beyond the model’s stated scope.
What open weights let a business do
Open weights are a model’s trained parameters made available for others to use. Nvidia says Nemotron 3.5 Super VL comes with open weights, datasets and training recipes, and that organizations can post-train it on their own data. Reflection released Beam’s weights under the Apache 2.0 license in BF16, FP8 and NVFP4 formats.
Reflection’s Apache 2.0 statement applies to Beam’s weights. It does not automatically describe the license for every other component in a deployment stack. For context on how Beam was introduced before its weights were released, read NeoTeo’s earlier report on the model. Reflection’s Beam model card gives its specifications and use details.
Customization and deployment paths
Nvidia says Nemotron 3.5 Super VL can be post-trained on organizational data and deployed across its data-center, cloud, workstation and edge stack. The company names Ramp’s financial-document intelligence work and says Scale AI post-trained the model for agents involving Skydance vendor-credit finance and EY merger-and-acquisition due diligence.
Reflection describes Beam as available through hosted services and cloud platforms, as well as on-premises, sovereign and air-gapped deployments. It says more than 40 ecosystem partners support the release, and names Dell and HPE for on-premises and air-gapped delivery. These are different routes to running a model; the descriptions alone do not make their operating costs or security outcomes equivalent.
Which model fits your workflow?
Choose based on the input and task your business needs to handle. Nemotron is the more direct match when visual understanding of documents, tables or charts is central. Beam is the closer fit for text-only coding, reasoning and agentic work, especially where its long context is useful.
Nvidia says Nemotron 3.5 Super VL is five times faster than the next-best model in its class. That is Nvidia’s stated comparison. It does not replace the practical choice between a multimodal model for visual tasks and a text-only model for language-based work.
