Sao10K: Llama 3.1 Euryale 70B v2.2

Public pricingIntelligence 79/100Medium memory

Sao10K: Llama 3.1 Euryale 70B v2.2 ist ein Text-Modell für Allgemeiner Chat, Analyse und Produktionseinsatz. Es verbindet stabile Allzweckleistung, einen Kontext von 131K tokens und ein ausgewogen-Profil für zuverlässige Arbeit über general chat, analysis, and production workloads.

Input

$0.85/1M

Output

$0.85/1M

Cached

$0.07/1M

Batch

$0.33/1M

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Technical specifications

Llama 3.1 Euryale 70B v2.2 at a glance.

Memory

131,072

tokens

Max reply

16,384

tokens

Memory tier

Medium

a long report or a codebase file

Tokenizer

llama3

Released

Aug 2024

Training cutoff

Dec 2023

Availability

Public pricing

Status

active

What it can do

Capabilities & limits.

  • Understands images
  • Deep step-by-step thinking
  • Uses tools / calls functions
  • Strict JSON output
  • Streams replies
  • Fine-tunable on your data

When to pick Llama 3.1 Euryale 70B v2.2

  • High-volume workloads where unit cost matters.

When to look elsewhere

  • Your workload involves images — pick a vision-capable model instead.
  • You need tool-use / function calling for agent workflows.

Alternatives

Similar models to compare.

FAQ

Llama 3.1 Euryale 70B v2.2 — the questions we see most.

Pricing, capabilities, alternatives — generated from the same data that powers the calculator above.

Get instant answers from our AI agent

At a typical workload of 50,000 conversations a month with 1,500-token prompts and 800-token replies, Llama 3.1 Euryale 70B v2.2 costs roughly $98 per month. Input is $0.85 /1M tokens and output is $0.85 /1M tokens.
Llama 3.1 Euryale 70B v2.2 has a 131,072-token context window (medium memory — a long report or a codebase file). That means you can fit about 24,576 words of input and history in a single call.
Llama 3.1 Euryale 70B v2.2 was released in August 2024, with training data cut off around December 2023.
Models in a similar class include Llama 3.3 Euryale 70B, Llama 3 Euryale 70B v2.1, Llama 3 8B Lunaris. The "Similar models" section below this FAQ links into each.

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