nvidia · Open weights
Nvidia Nemotron 3.5 Super VL
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.
| Nvidia Nemotron 3.5 Super VLnvidiaThis model | Nvidia Nemotron 3 Ultra 550BnvidiaRemove | |
|---|---|---|
| Provider | nvidia | nvidia |
| Discovery tier | Open weights | Open weights |
| Added | Oct 15, 2026 | Jun 4, 2026 |
| Limits | ||
| Context window | 262.14K | 1MBest |
| Max output | 16.38K | 65.54KBest |
| Average output | 1.1K tokens | 56 tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.15Best | $0.50 |
| Output | $0.50Best | $2.50 |
| Cache read | $0.02 | — |
| Performance | ||
| Throughput | 654.7 tok/sBest | 111.9 tok/s |
| Time to first token | 1.8 s | 1.5 sBest |
| Request success (24h) | 98.1% | 100.0%Best |
| Benchmark indexes | ||
| Intelligence Index | 26.5Best | 22.9 |
| Coding Index | — | 49.3 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Vision | Yes | No |
| Structured output | Yes | No |
| Video input | Yes | No |
| Parallel tools | No | Yes |
| Reasoning effort | none, 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
Intelligence Index
- Nvidia Nemotron 3.5 Super VL26.5
- Nvidia Nemotron 3 Ultra 550B22.9
Nvidia Nemotron 3.5 Super VL: Better than 66% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Nvidia Nemotron 3.5 Super VL: —
- Nvidia Nemotron 3 Ultra 550B: 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: 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: 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: 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: 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: 5.0%
AA-LCRv1.1
Reasoning across long documents
- Nvidia Nemotron 3.5 Super VL: 69.0%
- Nvidia Nemotron 3 Ultra 550B: 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: 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: 28.4%
GPQA Diamond
Graduate-level science questions
- Nvidia Nemotron 3.5 Super VL: —
- Nvidia Nemotron 3 Ultra 550B: 86.7%
IFBench
Following precise output instructions
- Nvidia Nemotron 3.5 Super VL: —
- Nvidia Nemotron 3 Ultra 550B: 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: 0.5%Top
SciCode
Scientific computing in Python
- Nvidia Nemotron 3.5 Super VL: 47.8%Top
- Nvidia Nemotron 3 Ultra 550B: 40.3%
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Nvidia Nemotron 3.5 Super VL: —
- Nvidia Nemotron 3 Ultra 550B: 36.4%
τ²-Bench Telecom
Tool use in a customer-support simulation
- Nvidia Nemotron 3.5 Super VL: —
- Nvidia Nemotron 3 Ultra 550B: 83.3%
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Nvidia Nemotron 3.5 Super VL: 69.0%
- Nvidia Nemotron 3 Ultra 550B: 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.
- 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.
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
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