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Meta · Open weights

Llama 3.1 8b Instruct

Added Jan 1, 2024Open weights

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Use in the app

Fast and efficient for simple purposes.

  • Tools
Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.02
Output
$0.04

Reference prices as published by the model provider. API example

Specifications

Context window
131.1K
Max output
16.4K
Average output (7 days)
57 tokens
Longer than 5% of 465 models

Side by side

Llama 3.1 8b Instruct 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 InstructMetaThis modelNvidia Nemotron 70bnvidiaRemove
ProviderMetanvidia
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Apr 15, 2025
Limits
Context window131.07KBest16.38K
Max output16.38KBest8.19K
Average output57 tokens—
Pricing, USD per 1M tokens
Input$0.02Best$0.36
Output$0.04Best$0.41
Performance
Throughput45.2 tok/s—
Time to first token1.8 s—
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index6.96.9
Coding Index5.4—
Math Index4.311.0Best
Capabilities
ToolsYesNo
Reasoning effort——

Public evaluations

Benchmarks

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

  • Llama 3.1 8b Instruct
  • Nvidia Nemotron 70b

Intelligence Index

  • Llama 3.1 8b Instruct6.9
  • Nvidia Nemotron 70b6.9

Llama 3.1 8b Instruct: 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 Instruct: 5.4
    • Nvidia Nemotron 70b: —

  • Math Index

    Composite of the math evaluations

    • Llama 3.1 8b Instruct: 4.3
    • Nvidia Nemotron 70b: 11.0Top

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.1 8b Instruct: 18.0%Top
    • Nvidia Nemotron 70b: 8.3%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.1 8b Instruct: 5.3%Top
    • Nvidia Nemotron 70b: 4.2%

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.1 8b Instruct: 25.9%
    • Nvidia Nemotron 70b: 46.5%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 3.1 8b Instruct: 47.6%
    • Nvidia Nemotron 70b: 69.0%Top

  • IFBench

    Following precise output instructions

    • Llama 3.1 8b Instruct: 28.6%
    • Nvidia Nemotron 70b: 30.7%Top

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 3.1 8b Instruct: 11.6%
    • Nvidia Nemotron 70b: 16.9%Top

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 3.1 8b Instruct: 4.3%
    • Nvidia Nemotron 70b: 11.0%Top

  • AIME

    Competition mathematics

    • Llama 3.1 8b Instruct: 7.7%
    • Nvidia Nemotron 70b: 24.7%Top

  • MATH-500

    Competition math problems

    • Llama 3.1 8b Instruct: 51.9%
    • Nvidia Nemotron 70b: 73.3%Top

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 3.1 8b Instruct: 0.8%
    • Nvidia Nemotron 70b: 4.5%Top

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 3.1 8b Instruct: 16.4%
    • Nvidia Nemotron 70b: 23.1%Top

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.1 8b Instruct: 18.0%Top
    • Nvidia Nemotron 70b: 8.3%

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.

Throughput
45.2 tok/s
Faster than 33% of 251 models
Time to first token
1.8 s
Quicker than 80% of 251 models
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/llama-3.1-8b-instruct",
    "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/llama-3.1-8b-instruct",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.