Context Window

Compare

Gemma 3 4b It Gguf vs Nemotron 3 Nano Omni (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.

Google

Model

Gemma 3 4b It Gguf

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Nvidia

Model

Nemotron 3 Nano Omni (free)

Image inputTool calling

Context window

256K

256,000 tokens · ~192K 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.

Gemma 3 4b It Gguf128K
Nemotron 3 Nano Omni (free)256K

Nemotron 3 Nano Omni (free) has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Nemotron 3 Nano Omni (free) has 100% more context capacity (256K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nemotron 3 Nano Omni (free). Its 256K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Nemotron 3 Nano Omni (free). Its 65K 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.

SpecGemma 3 4b It GgufNemotron 3 Nano Omni (free)
Context window128,000 tokens (128K)256,000 tokens (256K)
Max output tokens8,192 tokens (8K)65,536 tokens (65K)
Speed tierBalancedFast
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AApr 2026

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.

ProviderGemma 3 4b It Gguf inGemma 3 4b It Gguf outNemotron 3 Nano Omni (free) inNemotron 3 Nano Omni (free) out
Lemonade————

Frequently asked questions

Nemotron 3 Nano Omni (free) has a larger context window: 256K 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