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Nvidia Nemotron 3 Nano 30B

Added Mar 1, 2026Open weights

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Nvidia's latest Nemotron 3 Nano model with 30B total parameters (3B active) using hybrid Mamba-Transformer MoE architecture. Features excellent throughput and strong reasoning capabilities.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.17
Output
$0.68

Reference prices as published by the model provider. API example

Specifications

Context window
262.1K
Max output
—
Average output (7 days)
564 tokens
Longer than 53% of 468 models

Side by side

Nvidia Nemotron 3 Nano 30B 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 Nano 30BnvidiaThis modelNvidia Nemotron 3.5 Lightning ThinkingnvidiaRemove
Providernvidianvidia
Discovery tierOpen weightsOpen weights
AddedMar 1, 2026Aug 12, 2026
Limits
Context window262.14K1MBest
Max output—65.54K
Average output564 tokens6.1K tokens
Pricing, USD per 1M tokens
Input$0.17$0.05Best
Output$0.68$0.20Best
Cache read—$0.01
Performance
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index6.812.9Best
Coding Index—26.8
Math Index13.3—
Capabilities
ReasoningNoYes
ToolsNoYes
Parallel toolsNoYes
Reasoning effort—none, high

Public evaluations

Benchmarks

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

  • Nvidia Nemotron 3 Nano 30B
  • Nvidia Nemotron 3.5 Lightning Thinking

Intelligence Index

  • Nvidia Nemotron 3 Nano 30B6.8
  • Nvidia Nemotron 3.5 Lightning Thinking12.9

Nvidia Nemotron 3 Nano 30B: Better than 8% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Nvidia Nemotron 3 Nano 30B: —
    • Nvidia Nemotron 3.5 Lightning Thinking: 26.8
  • Math Index

    Composite of the math evaluations

    • Nvidia Nemotron 3 Nano 30B: 13.3
    • Nvidia Nemotron 3.5 Lightning Thinking: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Nvidia Nemotron 3 Nano 30B: —
    • Nvidia Nemotron 3.5 Lightning Thinking: 0.8%
  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Nvidia Nemotron 3 Nano 30B: —
    • Nvidia Nemotron 3.5 Lightning Thinking: 0.0%
  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Nvidia Nemotron 3 Nano 30B: —
    • Nvidia Nemotron 3.5 Lightning Thinking: 506 Elo
  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Nvidia Nemotron 3 Nano 30B: —
    • Nvidia Nemotron 3.5 Lightning Thinking: 638 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Nvidia Nemotron 3 Nano 30B: —
    • Nvidia Nemotron 3.5 Lightning Thinking: 3.0%
  • AA-LCRv1.1

    Reasoning across long documents

    • Nvidia Nemotron 3 Nano 30B: 10.7%
    • Nvidia Nemotron 3.5 Lightning Thinking: 60.3%Top
  • MLCR-AA

    Long-context reasoning over medical records

    • Nvidia Nemotron 3 Nano 30B: —
    • Nvidia Nemotron 3.5 Lightning Thinking: 2.2%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Nvidia Nemotron 3 Nano 30B: 4.6%
    • Nvidia Nemotron 3.5 Lightning Thinking: 10.6%Top
  • GPQA Diamond

    Graduate-level science questions

    • Nvidia Nemotron 3 Nano 30B: 39.9%
    • Nvidia Nemotron 3.5 Lightning Thinking: 74.3%Top
  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Nvidia Nemotron 3 Nano 30B: 57.9%
    • Nvidia Nemotron 3.5 Lightning Thinking: —
  • IFBench

    Following precise output instructions

    • Nvidia Nemotron 3 Nano 30B: 37.5%
    • Nvidia Nemotron 3.5 Lightning Thinking: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Nvidia Nemotron 3 Nano 30B: —
    • Nvidia Nemotron 3.5 Lightning Thinking: 0.5%
  • SciCode

    Scientific computing in Python

    • Nvidia Nemotron 3 Nano 30B: —
    • Nvidia Nemotron 3.5 Lightning Thinking: 32.1%
  • LiveCodeBench

    Recent competitive programming problems

    • Nvidia Nemotron 3 Nano 30B: 36.0%
    • Nvidia Nemotron 3.5 Lightning Thinking: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Nvidia Nemotron 3 Nano 30B: 13.3%
    • Nvidia Nemotron 3.5 Lightning Thinking: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Nvidia Nemotron 3 Nano 30B: 12.1%
    • Nvidia Nemotron 3.5 Lightning Thinking: —
  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Nvidia Nemotron 3 Nano 30B: 25.4%
    • Nvidia Nemotron 3.5 Lightning Thinking: —
  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Nvidia Nemotron 3 Nano 30B: 10.7%
    • Nvidia Nemotron 3.5 Lightning Thinking: 60.3%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.

Request success (24h)
100.0%
Reliable
Hourly request success · last 72 hoursLowest hour 94.7%
  • 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-nano-30b-a3b",
    "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-nano-30b-a3b",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

nvidia

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