Context Window

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Hermes3 70b vs Llama 3 3 70b Versatile

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.

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Model

Hermes3 70b

Tool calling

Context window

131K

131,072 tokens · ~98K words

Model page
Meta

Model

Llama 3 3 70b Versatile

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.

Hermes3 70b131K
Llama 3 3 70b Versatile128K

Hermes3 70b has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Hermes3 70b has 2% more context capacity (131K vs 128K tokens). Hermes3 70b is 79% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Hermes3 70b. Its 131K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Hermes3 70b. Input tokens are 79% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Hermes3 70b. Its 131K 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.

SpecHermes3 70bLlama 3 3 70b Versatile
Context window131,072 tokens (131K)128,000 tokens (128K)
Max output tokens131,072 tokens (131K)32,768 tokens (32K)
Speed tierDeepDeep
VisionNoNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoNo
Release dateN/AN/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.

ProviderHermes3 70b inHermes3 70b outLlama 3 3 70b Versatile inLlama 3 3 70b Versatile out
Groq——$0.590/M$0.790/M
Lambda$0.120/M$0.300/M——

Frequently asked questions

Hermes3 70b 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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Use a smaller model.
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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