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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 modelMuse Spark OpenMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsFrontier
AddedJan 1, 2024Oct 15, 2026
Limits
Context window131.07K1.05MBest
Max output16.38K—
Average output57 tokens1.3K tokens
Pricing, USD per 1M tokens
Input$0.02Best$1.25
Output$0.04Best$4.25
Cache read—$0.15
Performance
Throughput45.2 tok/s—
Time to first token1.8 s—
Request success (24h)100.0%—
Benchmark indexes
Intelligence Index6.938.9Best
Coding Index5.4—
Math Index4.3—
Capabilities
ReasoningNoYes
ToolsYesYes
VisionNoYes
Structured outputNoYes
Reasoning effort—low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Llama 3.1 8b Instruct
  • Muse Spark Open

Intelligence Index

  • Llama 3.1 8b Instruct6.9
  • Muse Spark Open38.9

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
    • Muse Spark Open: —

  • Math Index

    Composite of the math evaluations

    • Llama 3.1 8b Instruct: 4.3
    • Muse Spark Open: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3.1 8b Instruct: —
    • Muse Spark Open: 49.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3.1 8b Instruct: —
    • Muse Spark Open: 20.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3.1 8b Instruct: —
    • Muse Spark Open: 1,279 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3.1 8b Instruct: —
    • Muse Spark Open: 1,401 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3.1 8b Instruct: —
    • Muse Spark Open: 18.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.1 8b Instruct: 18.0%
    • Muse Spark Open: 78.3%Top

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 3.1 8b Instruct: —
    • Muse Spark Open: 7.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.1 8b Instruct: 5.3%
    • Muse Spark Open: 43.9%Top

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.1 8b Instruct: 25.9%
    • Muse Spark Open: 89.0%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 3.1 8b Instruct: 47.6%
    • Muse Spark Open: —

  • IFBench

    Following precise output instructions

    • Llama 3.1 8b Instruct: 28.6%
    • Muse Spark Open: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3.1 8b Instruct: —
    • Muse Spark Open: 8.1%

  • SciCode

    Scientific computing in Python

    • Llama 3.1 8b Instruct: —
    • Muse Spark Open: 58.4%

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 3.1 8b Instruct: 11.6%
    • Muse Spark Open: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 3.1 8b Instruct: 4.3%
    • Muse Spark Open: —

  • AIME

    Competition mathematics

    • Llama 3.1 8b Instruct: 7.7%
    • Muse Spark Open: —

  • MATH-500

    Competition math problems

    • Llama 3.1 8b Instruct: 51.9%
    • Muse Spark Open: —

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 3.1 8b Instruct: 0.8%
    • Muse Spark Open: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 3.1 8b Instruct: 16.4%
    • Muse Spark Open: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.1 8b Instruct: 18.0%
    • Muse Spark Open: 78.3%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.

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.