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Llama 3.1 70B Euryale

Added Jan 1, 2024Open weights

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Use in the app

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 modelQwen SEA-LION v4 32BaisingaporeRemove
ProviderMetaaisingapore
Discovery tierOpen weightsOpen weights
AddedJan 1, 2024Oct 15, 2026
Limits
Context window20.48K131.07KBest
Max output16.38K—
Average output272 tokens—
Pricing, USD per 1M tokens
Input$0.31$0.30Best
Output$0.36Best$0.60
Performance
Request success (24h)21.2%—
Capabilities
ToolsNoYes
Reasoning effort——

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.

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

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