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Nvidia Nemotron Super 3 120b vs Openai Gpt 4o

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

Nvidia Nemotron Super 3 120b

Tool calling

Context window

256K

256,000 tokens · ~192K words

Model page
Openai

Model

Openai Gpt 4o

Context window

128K

128,000 tokens · ~96K 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.

Nvidia Nemotron Super 3 120b256K
Openai Gpt 4o128K

Nvidia Nemotron Super 3 120b has about 2× the context window of the other in this pair.

Nvidia Nemotron Super 3 120b has 100% more context capacity (256K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nvidia Nemotron Super 3 120b. Its 256K context fits entire documents without chunking (vs 128K).

Full specs

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

SpecNvidia Nemotron Super 3 120bOpenai Gpt 4o
Context window256,000 tokens (256K)128,000 tokens (128K)
Max output tokens32,768 tokens (32K)N/A
Speed tierBalancedBalanced
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoYes
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.

ProviderNvidia Nemotron Super 3 120b inNvidia Nemotron Super 3 120b outOpenai Gpt 4o inOpenai Gpt 4o out
Aws Bedrock$0.150/M$0.650/M
Gradient

Frequently asked questions

Nvidia Nemotron Super 3 120b 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.

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