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NVIDIA

Nemotron 3.5 Super VL

Nemotron 3.5 Super VL is a vision-language chat model from NVIDIA, positioned in the Super tier of the Nemotron 3.5 family and measured at roughly 192 output tokens per second.

Input from
$0.100 / 1M tokens
across 3 providers

API Pricing

Cheapest on Crusoe — 33% below avg
ProviderInput / 1MOutput / 1MCached / 1M
$0.100$0.400$0.080
$0.150$0.500$0.020
$0.200$0.500$0.040

Prices updated daily. Last check: Oct 16, 2026

Compare API pricing for every Nemotron model →

Nemotron 3.5 Super VL pricing by provider

Input, output, and batch rates, plus alternatives, for one provider at a time.

Performance & Benchmarks

Source: Artificial Analysis →
Intelligence
26.5 / 100
Output Speed
192 t/s
Latency (TTFT)
445ms

Reasoning & Knowledge

  • Humanity's Last Exam30.7%

Coding

  • SciCode47.8%

Instruction & Long Context

  • Long-Context Reasoning69.0%

Benchmarks measured Oct 2026. Scores are independent evaluations, not vendor-reported.

Model Details

General

Creator
NVIDIA
Modalities
Text

Capabilities

Strengths & Limitations

Strengths

  • Vision-language configuration, so image input can be combined with text prompts in a single chat turn
  • Measured output throughput of about 192 tokens per second, suitable for streaming interactive responses
  • Time to first token of roughly 445 ms on the measured endpoint, keeping perceived latency low in chat UIs
  • Part of NVIDIA's Nemotron 3.5 generation, which ships multiple tiers so you can move up or down in size within one family
  • Super tier sits between smaller and larger Nemotron variants, giving a middle option on the capability/throughput trade-off
  • Available through multiple hosting routes, which lets you compare providers in the pricing table on this page

Limitations

  • We do not track a confirmed context window length for this model, so long-document limits should be verified on NVIDIA's model card
  • No quality benchmark scores (reasoning, coding, or vision benchmarks) are recorded in our database for this entry
  • Throughput and latency figures come from a single measured endpoint and will vary by provider, region, and load
  • Fewer independent evaluations and community tooling references than more widely deployed vision-language chat models
  • Tier naming within Nemotron generations can make it hard to compare directly against non-NVIDIA models without running your own evals

Key Features

•Vision-language chat interface (VL variant)
•Approximately 192 output tokens per second measured throughput
•Approximately 445 ms time to first token
•Nemotron 3.5 generation, Super tier
•Streaming chat response support
•Multimodal prompting combining images and text
•Multiple hosting providers tracked in our pricing comparison

About Nemotron 3.5 Super VL

Nemotron 3.5 Super VL is an NVIDIA model in the Nemotron family, carrying the 3.5 generation label and the "Super" tier designation that NVIDIA uses to mark a mid-to-upper size point within a Nemotron release, between the smaller and larger variants of the same generation. The "VL" suffix indicates a vision-language configuration — a chat model intended to accept visual input alongside text rather than a text-only sibling. Our catalog records it as a chat model with independent serving measurements from Artificial Analysis: approximately 192.09 output tokens per second and a time to first token of around 445 ms. Those figures place it in the range where interactive, streaming chat feels responsive, with a sub-half-second wait before the first token arrives on the measured endpoint. We do not currently track a confirmed context window, parameter count, or quality benchmark suite for this entry, so those specifics should be checked against NVIDIA's own model card before committing to a deployment. In practice, models in NVIDIA's Nemotron line are published for use through NVIDIA's own inference endpoints and through third-party hosts, and tier names like Super are the main signal for where a given checkpoint sits on the size/throughput curve within its generation. If you are comparing Nemotron 3.5 Super VL against other hosted vision-capable chat models, the throughput and latency numbers above are the measurements we have; evaluate output quality on your own multimodal prompts, since we do not carry accuracy benchmarks for this entry.

Common Use Cases

Nemotron 3.5 Super VL fits workloads that pair images with conversational text: describing or summarizing screenshots and documents, answering questions about charts and diagrams, visual QA inside support tools, and assistant interfaces where a user can paste an image and ask about it. The measured 445 ms time to first token and ~192 tokens per second output rate make it a reasonable candidate for live, user-facing chat where responses stream rather than arriving in a single block. As a Super-tier model it is aimed at general-purpose multimodal work rather than the very high-volume, low-cost classification jobs that smaller tier models target. Because we do not carry accuracy benchmarks for this entry, teams with strict quality requirements on tasks like OCR-heavy document extraction or fine-grained visual reasoning should benchmark it on representative inputs before rolling it out.

Frequently Asked Questions

How much does Nemotron 3.5 Super VL cost to run?

Pricing depends on which provider hosts the model and on the pricing type — per-input-token and per-output-token rates differ, and some providers bill image inputs separately or offer dedicated/batch rates. Rates change frequently, so check the pricing table on this page for current per-provider numbers rather than relying on a figure quoted in text.

What is Nemotron 3.5 Super VL best used for?

It is a vision-language chat model, so it is best suited to tasks that combine images with text: visual question answering, screenshot and diagram interpretation, image-grounded support chat, and multimodal assistants. Its measured latency and throughput also make it workable for interactive streaming chat.

What does the "VL" and "Super" in the name mean?

"VL" marks this as the vision-language configuration, meaning it is built to take visual input in addition to text. "Super" is NVIDIA's tier label within a Nemotron generation, indicating a mid-to-upper size point in the 3.5 lineup rather than the smallest or largest variant.

How fast is it compared with other chat models?

Artificial Analysis measurements in our database show roughly 192 output tokens per second and about 445 ms to the first token. That is fast enough for responsive streaming chat, though real-world numbers depend on the provider, the prompt length, whether images are attached, and current load.

What context window does it support?

We do not currently track a confirmed context window for Nemotron 3.5 Super VL. Check NVIDIA's model card or your chosen provider's API documentation for the supported maximum before designing around long inputs.