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Llama 3.3 70B Instruct (free) vs Nemotron 3 Ultra (free)

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 Instruct (free)

Tool calling

Context window

66K

65,536 tokens · ~49K words

Model page
Nvidia

Model

Nemotron 3 Ultra (free)

Tool calling

Context window

1M

1,000,000 tokens · ~750K 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 Instruct (free)66K
Nemotron 3 Ultra (free)1M

Nemotron 3 Ultra (free) has about 15.3× the context window of the other in this pair.

Nemotron 3 Ultra (free) has 1425% more context capacity (1000K vs 65K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nemotron 3 Ultra (free). Its 1000K context fits entire documents without chunking (vs 65K).

Full specs

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

SpecLlama 3.3 70B Instruct (free)Nemotron 3 Ultra (free)
Context window65,536 tokens (65K)1,000,000 tokens (1000K)
Max output tokensN/A65,536 tokens (65K)
Speed tierDeepBalanced
VisionNoNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateDec 2024Jun 2026

Frequently asked questions

Nemotron 3 Ultra (free) has a larger context window: 1000K tokens vs 65K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

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.

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