Context Window

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Llama 3 3 70b Versatile vs Nemotron 3 Nano 30B A3B

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.

Meta

Model

Llama 3 3 70b Versatile

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Nvidia

Model

Nemotron 3 Nano 30B A3B

Tool calling

Context window

262K

262,144 tokens · ~197K 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 3 70b Versatile128K
Nemotron 3 Nano 30B A3B262K

Nemotron 3 Nano 30B A3B has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Nemotron 3 Nano 30B A3B has 104% more context capacity (262K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nemotron 3 Nano 30B A3B. Its 262K context fits entire documents without chunking (vs 128K).

Full specs

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

SpecLlama 3 3 70b VersatileNemotron 3 Nano 30B A3B
Context window128,000 tokens (128K)262,144 tokens (262K)
Max output tokens32,768 tokens (32K)N/A
Speed tierDeepFast
VisionNoNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/ADec 2025

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 3 70b Versatile inLlama 3 3 70b Versatile outNemotron 3 Nano 30B A3B inNemotron 3 Nano 30B A3B out
Groq$0.590/M$0.790/M——

Frequently asked questions

Nemotron 3 Nano 30B A3B has a larger context window: 262K 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