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Nvidia Nemotron 3.5 Super VL

Added Oct 15, 2026Open weights

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Use in the app

NVIDIA’s open-weight 120B model (12B active) for reasoning, coding, tool use, document and GUI understanding, and video understanding. Text, images and video in; text out. A 262,144-token shared context window with responses limited to 16,384 tokens. OpenMDW 1.1 license.

  • Reasoning
  • Tools
  • Vision
  • Structured output
  • Video input
Input
Text, Images, Video
Output
Text
Reasoning effort
none · medium · high
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.15
Output
$0.50
Cache read
$0.02

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
16.4K
Average output (7 days)
1.1K tokens
Longer than 73% of 468 models

Side by side

Nvidia Nemotron 3.5 Super VL compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Nvidia Nemotron 3.5 Super VLnvidiaThis modelNvidia Nemotron 3 Ultra 550B ThinkingnvidiaRemove
Providernvidianvidia
Discovery tierOpen weightsOpen weights
AddedOct 15, 2026Jun 4, 2026
Limits
Context window262.14K1MBest
Max output16.38K65.54KBest
Average output1.1K tokens654 tokens
Pricing, USD per 1M tokens
Input$0.15Best$0.50
Output$0.50Best$2.50
Cache read$0.02—
Performance
Throughput654.7 tok/sBest79.0 tok/s
Time to first token1.8 sBest2.1 s
Request success (24h)98.1%99.7%Best
Benchmark indexes
Intelligence Index26.5Best22.9
Coding Index—49.3
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesNo
Structured outputYesNo
Video inputYesNo
Parallel toolsNoYes
Reasoning effortnone, medium, high—

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Nvidia Nemotron 3.5 Super VL
  • Nvidia Nemotron 3 Ultra 550B Thinking

Intelligence Index

  • Nvidia Nemotron 3.5 Super VL26.5
  • Nvidia Nemotron 3 Ultra 550B Thinking22.9

Nvidia Nemotron 3.5 Super VL: Better than 66% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Nvidia Nemotron 3.5 Super VL: —
    • Nvidia Nemotron 3 Ultra 550B Thinking: 49.3

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Nvidia Nemotron 3.5 Super VL: 14.8%Top
    • Nvidia Nemotron 3 Ultra 550B Thinking: 3.0%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Nvidia Nemotron 3.5 Super VL: 2.0%Top
    • Nvidia Nemotron 3 Ultra 550B Thinking: 0.3%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Nvidia Nemotron 3.5 Super VL: 999 EloTop
    • Nvidia Nemotron 3 Ultra 550B Thinking: 876 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Nvidia Nemotron 3.5 Super VL: 1,162 EloTop
    • Nvidia Nemotron 3 Ultra 550B Thinking: 1,013 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Nvidia Nemotron 3.5 Super VL: 8.0%Top
    • Nvidia Nemotron 3 Ultra 550B Thinking: 5.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Nvidia Nemotron 3.5 Super VL: 69.0%
    • Nvidia Nemotron 3 Ultra 550B Thinking: 79.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Nvidia Nemotron 3.5 Super VL: 13.3%Top
    • Nvidia Nemotron 3 Ultra 550B Thinking: 5.6%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Nvidia Nemotron 3.5 Super VL: 30.7%Top
    • Nvidia Nemotron 3 Ultra 550B Thinking: 28.4%

  • GPQA Diamond

    Graduate-level science questions

    • Nvidia Nemotron 3.5 Super VL: —
    • Nvidia Nemotron 3 Ultra 550B Thinking: 86.7%

  • IFBench

    Following precise output instructions

    • Nvidia Nemotron 3.5 Super VL: —
    • Nvidia Nemotron 3 Ultra 550B Thinking: 81.4%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Nvidia Nemotron 3.5 Super VL: 0.0%
    • Nvidia Nemotron 3 Ultra 550B Thinking: 0.5%Top

  • SciCode

    Scientific computing in Python

    • Nvidia Nemotron 3.5 Super VL: 47.8%Top
    • Nvidia Nemotron 3 Ultra 550B Thinking: 40.3%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Nvidia Nemotron 3.5 Super VL: —
    • Nvidia Nemotron 3 Ultra 550B Thinking: 36.4%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Nvidia Nemotron 3.5 Super VL: —
    • Nvidia Nemotron 3 Ultra 550B Thinking: 83.3%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Nvidia Nemotron 3.5 Super VL: 69.0%
    • Nvidia Nemotron 3 Ultra 550B Thinking: 79.3%Top

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Throughput
654.7 tok/s
Faster than 98% of 254 models
Time to first token
1.8 s
Quicker than 85% of 254 models
Prompt cache hits
66.9%
About $0.063 per 1M input tokens with caching
Request success (24h)
98.1%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 72.8%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nvidia/nemotron-3.5-super-vl",
    "reasoning_effort": "medium",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="nvidia/nemotron-3.5-super-vl",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

nvidia

Other nvidia models, starting with the same family.