Context Window

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Nemotron Nano 12B 2 VL (free) vs Phi 4

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.

Nvidia

Model

Nemotron Nano 12B 2 VL (free)

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Microsoft

Model

Phi 4

Tool calling

Context window

16K

16,384 tokens · ~12K 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.

Nemotron Nano 12B 2 VL (free)128K
Phi 416K

Nemotron Nano 12B 2 VL (free) has about 7.8× the context window of the other in this pair.

Token calculator →Memory savings →

Nemotron Nano 12B 2 VL (free) has 681% more context capacity (128K vs 16K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nemotron Nano 12B 2 VL (free). Its 128K context fits entire documents without chunking (vs 16K).

  • Long output (reports, code files)

    Use Nemotron Nano 12B 2 VL (free). Its 128K 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.

SpecNemotron Nano 12B 2 VL (free)Phi 4
Context window128,000 tokens (128K)16,384 tokens (16K)
Max output tokens128,000 tokens (128K)16,384 tokens (16K)
Speed tierFastBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoNo
Release dateOct 2025Jan 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.

ProviderNemotron Nano 12B 2 VL (free) inNemotron Nano 12B 2 VL (free) outPhi 4 inPhi 4 out
Azure——$0.125/M$0.500/M
Deepinfra——$0.070/M$0.140/M

Frequently asked questions

Nemotron Nano 12B 2 VL (free) has a larger context window: 128K tokens vs 16K. 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