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Llama 3.1 70B Euryale

Added Jan 1, 2024Open weights

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A 70B parameter model from SAO10K based on Llama 3.1 70B, offering high-quality text generation.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.31
Output
$0.36

Reference prices as published by the model provider. API example

Specifications

Context window
20.5K
Max output
16.4K
Average output (7 days)
272 tokens
Longer than 28% of 468 models

Side by side

Llama 3.1 70B Euryale 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 70B EuryaleMetaThis modelMuse Spark OpenMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsFrontier
AddedJan 1, 2024Oct 15, 2026
Limits
Context window20.48K1.05MBest
Max output16.38K—
Average output272 tokens1.3K tokens
Pricing, USD per 1M tokens
Input$0.31Best$1.25
Output$0.36Best$4.25
Cache read—$0.15
Performance
Request success (24h)21.2%—
Benchmark indexes
Intelligence Index—38.9
Capabilities
ReasoningNoYes
ToolsNoYes
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 70B Euryale
  • Muse Spark Open

Intelligence Index

  • Llama 3.1 70B Euryale—
  • Muse Spark Open38.9
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 49.7%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 20.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 1,279 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 1,401 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 18.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 78.3%

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 7.8%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 43.9%

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 89.0%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 8.1%

  • SciCode

    Scientific computing in Python

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 58.4%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.1 70B Euryale: —
    • Muse Spark Open: 78.3%

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.

Prompt cache hits
0.0%
Request success (24h)
21.2%

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.

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": "Sao10K/L3.1-70B-Euryale-v2.2",
    "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="Sao10K/L3.1-70B-Euryale-v2.2",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.