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Nvidia Nemotron 70b

Added Apr 15, 2025Open weights

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Use in the app

Nvidia's latest Llama fine-tune optimized for instruction following. Early results hints that it might outperform models such as GPT-4o and Claude 3.5 Sonnet.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.36
Output
$0.41

Reference prices as published by the model provider. API example

Specifications

Context window
16.4K
Max output
8.2K

Side by side

Nvidia Nemotron 70b compared

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

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Nvidia Nemotron 70bnvidiaThis modelAmazon Nova Pro 1.0amazonRemove
Providernvidiaamazon
Discovery tierOpen weightsBalanced
AddedApr 15, 2025Dec 3, 2024
Limits
Context window16.38K300KBest
Max output8.19K32KBest
Average output—56 tokens
Pricing, USD per 1M tokens
Input$0.36Best$0.80
Output$0.41Best$3.20
Performance
Request success (24h)22.9%—
Benchmark indexes
Intelligence Index6.97.0Best
Math Index11.0Best7.0
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

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

  • Nvidia Nemotron 70b
  • Amazon Nova Pro 1.0

Intelligence Index

  • Nvidia Nemotron 70b6.9
  • Amazon Nova Pro 1.07.0

Nvidia Nemotron 70b: Better than 8% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Math Index

    Composite of the math evaluations

    • Nvidia Nemotron 70b: 11.0Top
    • Amazon Nova Pro 1.0: 7.0

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Nvidia Nemotron 70b: 8.3%
    • Amazon Nova Pro 1.0: 21.0%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Nvidia Nemotron 70b: 4.2%Top
    • Amazon Nova Pro 1.0: 3.2%

  • GPQA Diamond

    Graduate-level science questions

    • Nvidia Nemotron 70b: 46.5%
    • Amazon Nova Pro 1.0: 49.9%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Nvidia Nemotron 70b: 69.0%
    • Amazon Nova Pro 1.0: 69.1%Top

  • IFBench

    Following precise output instructions

    • Nvidia Nemotron 70b: 30.7%
    • Amazon Nova Pro 1.0: 38.1%Top

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Nvidia Nemotron 70b: 16.9%
    • Amazon Nova Pro 1.0: 23.3%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Nvidia Nemotron 70b: 11.0%Top
    • Amazon Nova Pro 1.0: 7.0%

  • AIME

    Competition mathematics

    • Nvidia Nemotron 70b: 24.7%Top
    • Amazon Nova Pro 1.0: 10.7%

  • MATH-500

    Competition math problems

    • Nvidia Nemotron 70b: 73.3%
    • Amazon Nova Pro 1.0: 78.6%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Nvidia Nemotron 70b: 4.5%
    • Amazon Nova Pro 1.0: 6.1%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Nvidia Nemotron 70b: 23.1%Top
    • Amazon Nova Pro 1.0: 14.0%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Nvidia Nemotron 70b: 8.3%
    • Amazon Nova Pro 1.0: 21.0%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)
22.9%

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-3.1-Nemotron-70B-Instruct-HF",
    "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-3.1-Nemotron-70B-Instruct-HF",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

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