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Nvidia Nemotron Super 49B

Added Aug 8, 2025Open weights

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Llama-3.3-Nemotron-Super-49B-v1 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model's memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to this paper.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.15
Output
$0.15

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
16.4K

Side by side

Nvidia Nemotron Super 49B compared

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

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Nvidia Nemotron Super 49BnvidiaThis modelNvidia Nemotron 3.5 Super VLnvidiaRemove
Providernvidianvidia
Discovery tierOpen weightsOpen weights
AddedAug 8, 2025Oct 15, 2026
Limits
Context window128K262.14KBest
Max output16.38K16.38K
Average output—1.1K tokens
Pricing, USD per 1M tokens
Input$0.15$0.15
Output$0.15Best$0.50
Cache read—$0.02
Performance
Throughput—621.5 tok/s
Time to first token—1.7 s
Request success (24h)—98.3%
Benchmark indexes
Intelligence Index7.326.5Best
Math Index7.7—
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
Video inputNoYes
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 Super 49B
  • Nvidia Nemotron 3.5 Super VL

Intelligence Index

  • Nvidia Nemotron Super 49B7.3
  • Nvidia Nemotron 3.5 Super VL26.5

Nvidia Nemotron Super 49B: Better than 10% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Nvidia Nemotron Super 49B: 7.7
    • Nvidia Nemotron 3.5 Super VL: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Nvidia Nemotron Super 49B: —
    • Nvidia Nemotron 3.5 Super VL: 14.8%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Nvidia Nemotron Super 49B: —
    • Nvidia Nemotron 3.5 Super VL: 2.0%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Nvidia Nemotron Super 49B: —
    • Nvidia Nemotron 3.5 Super VL: 999 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Nvidia Nemotron Super 49B: —
    • Nvidia Nemotron 3.5 Super VL: 1,162 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Nvidia Nemotron Super 49B: —
    • Nvidia Nemotron 3.5 Super VL: 8.0%

  • AA-LCRv1.1

    Reasoning across long documents

    • Nvidia Nemotron Super 49B: 12.0%
    • Nvidia Nemotron 3.5 Super VL: 69.0%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Nvidia Nemotron Super 49B: —
    • Nvidia Nemotron 3.5 Super VL: 13.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Nvidia Nemotron Super 49B: 3.8%
    • Nvidia Nemotron 3.5 Super VL: 30.7%Top

  • GPQA Diamond

    Graduate-level science questions

    • Nvidia Nemotron Super 49B: 51.7%
    • Nvidia Nemotron 3.5 Super VL: —

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Nvidia Nemotron Super 49B: 69.8%
    • Nvidia Nemotron 3.5 Super VL: —

  • IFBench

    Following precise output instructions

    • Nvidia Nemotron Super 49B: 39.5%
    • Nvidia Nemotron 3.5 Super VL: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Nvidia Nemotron Super 49B: —
    • Nvidia Nemotron 3.5 Super VL: 0.0%

  • SciCode

    Scientific computing in Python

    • Nvidia Nemotron Super 49B: —
    • Nvidia Nemotron 3.5 Super VL: 47.8%

  • LiveCodeBench

    Recent competitive programming problems

    • Nvidia Nemotron Super 49B: 28.0%
    • Nvidia Nemotron 3.5 Super VL: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Nvidia Nemotron Super 49B: 7.7%
    • Nvidia Nemotron 3.5 Super VL: —

  • AIME

    Competition mathematics

    • Nvidia Nemotron Super 49B: 19.3%
    • Nvidia Nemotron 3.5 Super VL: —

  • MATH-500

    Competition math problems

    • Nvidia Nemotron Super 49B: 77.5%
    • Nvidia Nemotron 3.5 Super VL: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Nvidia Nemotron Super 49B: 0.0%
    • Nvidia Nemotron 3.5 Super VL: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Nvidia Nemotron Super 49B: 12.0%
    • Nvidia Nemotron 3.5 Super VL: 69.0%Top

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

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/Llama-3.3-Nemotron-Super-49B-v1",
    "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/Llama-3.3-Nemotron-Super-49B-v1",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

nvidia

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