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.
API Pricing
Cheapest on Crusoe — 33% below avg| Provider | Input / 1M | Output / 1M | Cached / 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 →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
About Nemotron 3.5 Super VL
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.