Context Window

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Llama 3.1 Euryale 70B v2.2 vs Llama 3 1 8b Instant

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Sao10K

Model

Llama 3.1 Euryale 70B v2.2

Tool calling

Context window

131K

131,072 tokens · ~98K words

Model page
Meta

Model

Llama 3 1 8b Instant

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Llama 3.1 Euryale 70B v2.2131K
Llama 3 1 8b Instant128K

Llama 3.1 Euryale 70B v2.2 has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Llama 3.1 Euryale 70B v2.2 has 2% more context capacity (131K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Llama 3.1 Euryale 70B v2.2. Its 131K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Llama 3.1 Euryale 70B v2.2. Its 16K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecLlama 3.1 Euryale 70B v2.2Llama 3 1 8b Instant
Context window131,072 tokens (131K)128,000 tokens (128K)
Max output tokens16,384 tokens (16K)8,192 tokens (8K)
Speed tierDeepFast
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateAug 2024N/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderLlama 3.1 Euryale 70B v2.2 inLlama 3.1 Euryale 70B v2.2 outLlama 3 1 8b Instant inLlama 3 1 8b Instant out
Groq——$0.050/M$0.080/M

Frequently asked questions

Llama 3.1 Euryale 70B v2.2 has a larger context window: 131K tokens vs 128K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model