Context Window

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Gemini Gemma 2 27b It vs Nvidia Nemotron 3 5 Super Vl 120b A12b

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

Gemini Gemma 2 27b It

Image inputTool calling

Context window

8K

8,192 tokens · ~6K words

Model page
Nvidia

Model

Nvidia Nemotron 3 5 Super Vl 120b A12b

Image inputTool calling

Context window

1.0M

1,048,576 tokens · ~786K 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.

Gemini Gemma 2 27b It8K
Nvidia Nemotron 3 5 Super Vl 120b A12b1.0M

Nvidia Nemotron 3 5 Super Vl 120b A12b has about 128× the context window of the other in this pair.

Token calculator →Memory savings →

Nvidia Nemotron 3 5 Super Vl 120b A12b has 12700% more context capacity (1048K vs 8K tokens). Nvidia Nemotron 3 5 Super Vl 120b A12b is 42% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nvidia Nemotron 3 5 Super Vl 120b A12b. Its 1048K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use Nvidia Nemotron 3 5 Super Vl 120b A12b. Input tokens are 42% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Nvidia Nemotron 3 5 Super Vl 120b A12b. Its 1048K 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.

SpecGemini Gemma 2 27b ItNvidia Nemotron 3 5 Super Vl 120b A12b
Context window8,192 tokens (8K)1,048,576 tokens (1048K)
Max output tokens8,192 tokens (8K)1,048,576 tokens (1048K)
Speed tierFastBalanced
VisionYesYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AN/A

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.

ProviderGemini Gemma 2 27b It inGemini Gemma 2 27b It outNvidia Nemotron 3 5 Super Vl 120b A12b inNvidia Nemotron 3 5 Super Vl 120b A12b out
Baseten——$0.200/M$0.500/M
Google$0.350/M$1.05/M——

Frequently asked questions

Nvidia Nemotron 3 5 Super Vl 120b A12b has a larger context window: 1048K tokens vs 8K. 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