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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

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Llama 3.3 70B Euryale compared

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Llama 3.3 70B EuryaleMetaThis modelMuse Spark 1.3 ContributorMetaRemove
ProviderMetaMeta
Discovery tierOpen weightsEfficient
AddedJan 1, 2024Sep 2, 2026
Limits
Context window20.48K1.05MBest
Max output16.38K—
Average output173 tokens1.2K tokens
Pricing, USD per 1M tokens
Input$0.49$0.10Best
Output$0.49$0.20Best
Cache read—$0.002
Performance
Throughput—264.8 tok/s
Time to first token—3.7 s
Request success (24h)15.8%99.2%Best
Capabilities
ReasoningNoYes
ToolsNoYes
VisionNoYes
Structured outputNoYes
PDF inputNoYes
Audio inputNoYes
Video inputNoYes
Reasoning effort—minimal, low, medium, high, xhigh

Public evaluations

Benchmarks

There are no public benchmark results for these models yet.

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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