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Gemma 3n 4B 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.
Model
Nemotron 3 Ultra (free)
Context window
1M
1,000,000 tokens · ~750K words
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.
Nemotron 3 Ultra (free) has about 30.5× the context window of the other in this pair.
Nemotron 3 Ultra (free) has 2951% more context capacity (1000K vs 32K 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 32K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | Gemma 3n 4B | Nemotron 3 Ultra (free) |
|---|---|---|
| Context window | 32,768 tokens (32K) | 1,000,000 tokens (1000K) |
| Max output tokens | N/A | 65,536 tokens (65K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | No | Yes |
| Extended thinking | No | Yes |
| Prompt caching | No | No |
| Batch API | No | No |
| Release date | May 2025 | Jun 2026 |
Frequently asked questions
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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.
Example: a multi-turn chat session
80% less to send — works with any model