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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 modelGLM 4.5VZ.aiRemove
ProvidernvidiaZ.ai
Discovery tierOpen weightsOpen weights
AddedMar 1, 2026Nov 22, 2025
Limits
Context window262.14KBest65.54K
Max output—16.38K
Average output564 tokens732 tokens
Pricing, USD per 1M tokens
Input$0.17Best$0.60
Output$0.68Best$1.80
Performance
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index6.8Best6.7
Math Index13.315.3Best
Capabilities
ReasoningNoYes
VisionNoYes
Reasoning effort——

Public evaluations

Benchmarks

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

  • Nvidia Nemotron 3 Nano 30B
  • GLM 4.5V

Intelligence Index

  • Nvidia Nemotron 3 Nano 30B6.8
  • GLM 4.5V6.7

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

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Nvidia Nemotron 3 Nano 30B: 13.3
    • GLM 4.5V: 15.3Top

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Nvidia Nemotron 3 Nano 30B: 10.7%Top
    • GLM 4.5V: 0.0%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Nvidia Nemotron 3 Nano 30B: 4.6%Top
    • GLM 4.5V: 3.5%

  • GPQA Diamond

    Graduate-level science questions

    • Nvidia Nemotron 3 Nano 30B: 39.9%
    • GLM 4.5V: 57.3%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Nvidia Nemotron 3 Nano 30B: 57.9%
    • GLM 4.5V: 75.1%Top

  • IFBench

    Following precise output instructions

    • Nvidia Nemotron 3 Nano 30B: 37.5%Top
    • GLM 4.5V: 28.6%

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Nvidia Nemotron 3 Nano 30B: 36.0%Top
    • GLM 4.5V: 35.2%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Nvidia Nemotron 3 Nano 30B: 13.3%
    • GLM 4.5V: 15.3%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Nvidia Nemotron 3 Nano 30B: 12.1%Top
    • GLM 4.5V: 6.8%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Nvidia Nemotron 3 Nano 30B: 25.4%Top
    • GLM 4.5V: 19.6%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Nvidia Nemotron 3 Nano 30B: 10.7%Top
    • GLM 4.5V: 0.0%

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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