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Llama 3.3 70B Euryale

Added Jan 1, 2024Open weights

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A 70B parameter model from SAO10K based on Llama 3.3 70B, offering high-quality text generation.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.49
Output
$0.49

Reference prices as published by the model provider. API example

Specifications

Context window
20.5K
Max output
16.4K
Average output (7 days)
173 tokens
Longer than 17% of 468 models

Side by side

Llama 3.3 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 3.3 70B EuryaleMetaThis modelMuse Spark 1.3MetaRemove
ProviderMetaMeta
Discovery tierOpen weightsFrontier
AddedJan 1, 2024Sep 2, 2026
Limits
Context window20.48K1.05MBest
Max output16.38K—
Average output173 tokens1.2K tokens
Pricing, USD per 1M tokens
Input$0.49Best$1.25
Output$0.49Best$4.25
Cache read—$0.15
Performance
Throughput—256.8 tok/s
Time to first token—3.0 s
Request success (24h)15.8%100.0%Best
Benchmark indexes
Intelligence Index—48.1
Coding Index—75.8
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
PDF inputNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—minimal, low, medium, high, xhigh

Public evaluations

Benchmarks

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

  • Llama 3.3 70B Euryale
  • Muse Spark 1.3

Intelligence Index

  • Llama 3.3 70B Euryale—
  • Muse Spark 1.348.1
3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 75.8

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 57.9%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 29.8%

  • Harvey LAB-AAv1.1

    Legal tasks passed in full, with no material hallucination

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 8.9%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 1,581 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 1,681 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 26.6%

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 83.0%

  • MLCR-AA

    Long-context reasoning over medical records

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 13.9%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 48.7%

  • GPQA Diamond

    Graduate-level science questions

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 93.5%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 33.3%

  • SciCode

    Scientific computing in Python

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 58.8%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 3.3 70B Euryale: —
    • Muse Spark 1.3: 83.0%

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)
15.8%

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/L3.3-70B-Euryale-v2.3",
    "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/L3.3-70B-Euryale-v2.3",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

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