Nemotron 3.5 Super VL Review: Screenshots, Charts and Video in One Chat Call
Pick Nemotron 3.5 Super VL for screenshots, charts and short clips at a low price; for plain printed text a dedicated OCR tool may do.
Freedom Score
Not yet tested
- Price
- $0.275 per 1M mixed tokens (3 input : 1 output)
- Output speed
- 155 tok/s
- Success rate
- Collecting data
Nemotron 3.5 Super VL is NVIDIA's vision-language model, released on 15 October 2026. NVIDIA describes a mixture-of-experts design with 120B parameters in total and 12B active per token, plus a vision encoder. On our API it is one OpenAI-compatible chat endpoint that takes text, images and video, reads up to 262,144 tokens in a request and writes back up to 262,144, at $0.20 per million input tokens and $0.50 output.
- SpicyAPI Engineering
- Tested Oct 28, 2026
- Methodology v3.3 →
- We sell these models; scores ignore price →
The verdict
This is a catalogue review: we checked the model against NVIDIA's published description and our endpoint limits, and we have not run our own quality or content tests on it. Every benchmark number below is NVIDIA's, not ours.
The reasons to pick it are the input types and the price. Screenshots, charts, forms, slides and sampled video frames go into the same message list as text, tool calling and JSON output work on the same request, and the thinking pass can be turned off for fast extraction. The reasons to hesitate are the default (reasoning effort starts at high, so a simple caption costs more tokens than it needs to), no audio or PDF input, and the fact that NVIDIA's own OCR numbers are close to, not ahead of, comparable Qwen models.
Key facts
- Tested by us: catalogue check and launch smoke run only, no quality or content tests; this page rests on NVIDIA's model card and our endpoint limits
- Price: $0.20 per million input tokens, $0.04 cached, $0.50 output
- Limits: 262,144-token context, replies up to 262,144 tokens (default 4,096), text, image and video input, up to 128 tools
- Reasoning: none, low, medium or high; high is the default
- Sources: the scoring rules are on our review methodology page, and the full parameter list is on the Nemotron 3.5 Super VL model page.
What we liked and what held it back
What we liked
- Images and video in the same call. Charts, tables, forms, slides and screenshots go in as image parts; video goes in as video parts, which NVIDIA describes as sampled frames.
- Thinking you can switch off.
reasoning_effortaccepts none, low, medium and high, so a caption or a field extraction does not have to pay for a long reasoning pass. - A low price for a vision model. $0.20 in and $0.50 out per million tokens, with cached input at $0.04.
- Tools and JSON on the same request. Up to 128 tool definitions with
tool_choiceandparallel_tool_calls, plusresponse_formatfor a JSON reply. - Open weights. NVIDIA publishes the model, so you can compare our hosted endpoint with a self-hosted copy of the same family.
What held it back
- High is the default reasoning effort. Reasoning tokens bill as output and count toward
max_tokens, so send none or low yourself for simple jobs and raise the cap when you leave it on high. - No audio and no PDF files. The endpoint takes text, images and video. Transcribe audio first; convert each PDF page to an image or send the extracted text.
- Video is sampled frames. It suits describing events and changes, not frame-exact timing.
- The window is 262,144 tokens here. NVIDIA's model page mentions up to 1M tokens in its description but lists 262,144 in its specification; 262,144 is what applies on this endpoint.
- Benchmarks are NVIDIA's. We have not reproduced them. NVIDIA also warns that the model can be susceptible to prompt injection, so treat text inside an image as untrusted input.
What we tested
We have not run this model through our capability sets or content levels. What this page uses: NVIDIA's model card and build page (read on 28 October 2026), the limits and parameters of our own endpoint, and the live price on the model page. Where a number comes from NVIDIA it is labelled that way. NVIDIA reports, among others, 89.52 on GPQA-Diamond, 71.10 on SWE-bench Verified and 78.16 on MathVision for the BF16 model, and an OCRBench v2 score of 68.12 that it places roughly level with a comparable Qwen model. None of these is a result from our test sets.
Prompts that worked
These are starting points based on the endpoint's shape, not prompts we benchmarked.
1. Read a chart. Attach the chart as an image part and ask for what can and cannot be read from it; the explicit second half keeps the answer honest about unreadable values.
Read this chart and report its labels, the trend of each series, and any values that cannot be established from the image.Run this prompt on Nemotron 3.5 Super VL →
2. A tool step. The shape of a tool-calling turn: one function, tool_choice set to auto, and your code running the call.
Call report_status with status ready.Run this prompt on Nemotron 3.5 Super VL →
Settings that matter: reasoning_effort none or low for captions and field extraction, high only for multi-step reasoning over a document; raise max_tokens above the 4,096 default when thinking is on; NVIDIA recommends temperature 1.0 and top_p 0.95 for text and 0.6 and 0.95 for vision tasks.
Roleplay and how far it writes
We have not tested Nemotron 3.5 Super VL on our content levels yet, so this review makes no claim about how far it writes or how often it softens or refuses. The model page carries no content-test results for it either. On our API we add no content filter; whatever comes back is the model's own behaviour.
Not tested yet: all five content levels, roleplay quality over many turns, and languages other than English. We will add results here once the runs are done.
Every character in a request should be an adult, keep real, identifiable people out of it, and you're responsible for the law where you publish.
Alternatives
- Want a model we have measured? Claude Haiku 5.5 reads text and images in a 1,000,000-token window and has a full capability review.
- A bigger window plus audio and video? Muse Wonder 1.0 accepts text, image, video and audio parts and reads about 1M tokens, at a much higher price.
- Text-only, with a long window? Ling 3.1 Flash is text-only with the same 262,144-token window at a lower price.
How Nemotron 3.5 Super VL earns its Spicy Index
Each dimension below links to its full workings: the table row every point came from and why points were lost.
- Public leaderboards 40% of quality
- Not yet tested
- Role-play 15% of quality
- Not yet tested
- Creative writing 15% of quality
- 60.5
- Instruction following 15% of quality
- 100.0
- Long-context memory 15% of quality
- Not yet tested
- Capability 10.5% weight
- 57.0
- Freedom Score 30% weight
- Not yet tested
No exact match on a tracked public leaderboard yet. How it works
Our role-play suite hasn’t run on this model yet. How it works
Our long-context suite hasn’t run on this model yet. How it works
Freedom evidence is untested or insufficient. The Index retains the original quality and capability score, is marked preliminary and still receives a rank. Missing evidence is not zero. How it works
Freedom evidence is untested or insufficient. The Index retains the original quality and capability score, is marked preliminary and still receives a rank. Missing evidence is not zero.
Open the full test report →Capability, item by item
Points from the public scoring table. Upscaled tiers and limits we set ourselves don’t count.
- Context window (creator-published)Not declared
- 0.0 / 30
- Input modalities on SpicyAPIText, plus images
- 12.0 / 25
- Tool useTools and tool_choice
- 15.0 / 15
- Structured outputAccepted, schema adherence verified
- 15.0 / 15
- Reasoning control4 levels: none · low · medium · high
- 15.0 / 15
What Nemotron 3.5 Super VL can do on SpicyAPI
Straight from the live model specification on SpicyAPI. Some creators offer more in their own apps; this is what you can call through our API today.
| Feature | Chat |
|---|---|
| Context window | 262,144 tokens |
| Max output per request | 262,144 tokens |
| Reasoning levels | none · low · medium · high |
| Tool calling | Yes |
| Structured output | Yes |
How long it really takes
Measured on real jobs over the last 30 days, from request to finished result. Times include our own test runs.
- Output speed
- 155 tok/sIncludes time to first token
- Success rate
- Collecting data
A time appears once at least 10 completed jobs are in. Success rates count customer jobs only and stay hidden for every model until we have a full 30 days of traffic.
Models to consider instead of Nemotron 3.5 Super VL
Anthropic
Claude Haiku 5.5
Want a model we have measured?
- Spicy Index
- 76.4−0.4
- Freedom Score
- 77.8
- Price
- $0.20−27%/ 1M tokens · 3:1 input/output
Meta
Muse Wonder 1.0
A bigger window plus audio and video?
- Spicy Index
- 81.0+4.2
- Freedom Score
- —
- Price
- $8.00+2,809%/ 1M tokens · 3:1 input/output
inclusionAI
Ling 3.1 Flash
Text-only, with a long window?
- Spicy Index
- 76.7−0.1
- Freedom Score
- —
- Price
- $0.113−59%/ 1M tokens · 3:1 input/output
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Nemotron 3.5 Super VL FAQ
Try Nemotron 3.5 Super VL yourself
Set every parameter in the Playground and see the exact price before you run it.
$0.275 per 1M mixed tokens (3 input : 1 output)
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