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Meta · Open weights

Llama 3.1 8B (decentralized)

Added Jan 1, 2024Open weights

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Use in the app

Meta's Llama 3.1 8B model on an open permissionless network

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.02
Output
$0.03

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
16.4K

Side by side

Llama 3.1 8B (decentralized) compared

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

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Llama 3.1 8B (decentralized)MetaThis modelAmazon Nova Pro 1.0amazonRemove
ProviderMetaamazon
Discovery tierOpen weightsBalanced
AddedJan 1, 2024Dec 3, 2024
Limits
Context window128K300KBest
Max output16.38K32KBest
Average output—56 tokens
Pricing, USD per 1M tokens
Input$0.02Best$0.80
Output$0.03Best$3.20
Performance
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index6.97.0Best
Coding Index5.4—
Math Index4.37.0Best
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

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

  • Llama 3.1 8B (decentralized)
  • Amazon Nova Pro 1.0

Intelligence Index

  • Llama 3.1 8B (decentralized)6.9
  • Amazon Nova Pro 1.07.0

Llama 3.1 8B (decentralized): Better than 8% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama 3.1 8B (decentralized): 5.4
    • Amazon Nova Pro 1.0: —

  • Math Index

    Composite of the math evaluations

    • Llama 3.1 8B (decentralized): 4.3
    • Amazon Nova Pro 1.0: 7.0Top

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.1 8B (decentralized): 18.0%
    • Amazon Nova Pro 1.0: 21.0%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.1 8B (decentralized): 5.3%Top
    • Amazon Nova Pro 1.0: 3.2%

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.1 8B (decentralized): 25.9%
    • Amazon Nova Pro 1.0: 49.9%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 3.1 8B (decentralized): 47.6%
    • Amazon Nova Pro 1.0: 69.1%Top

  • IFBench

    Following precise output instructions

    • Llama 3.1 8B (decentralized): 28.6%
    • Amazon Nova Pro 1.0: 38.1%Top

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 3.1 8B (decentralized): 11.6%
    • Amazon Nova Pro 1.0: 23.3%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 3.1 8B (decentralized): 4.3%
    • Amazon Nova Pro 1.0: 7.0%Top

  • AIME

    Competition mathematics

    • Llama 3.1 8B (decentralized): 7.7%
    • Amazon Nova Pro 1.0: 10.7%Top

  • MATH-500

    Competition math problems

    • Llama 3.1 8B (decentralized): 51.9%
    • Amazon Nova Pro 1.0: 78.6%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 3.1 8B (decentralized): 0.8%
    • Amazon Nova Pro 1.0: 6.1%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 3.1 8B (decentralized): 16.4%Top
    • Amazon Nova Pro 1.0: 14.0%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.1 8B (decentralized): 18.0%
    • 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.

Prompt cache hits
0.0%
Request success (24h)
100.0%
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.

Hourly request success · last 72 hoursLowest hour 100.0%
  • 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": "Meta-Llama-3-1-8B-Instruct-FP8",
    "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="Meta-Llama-3-1-8B-Instruct-FP8",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.