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Nemotron 3 Ultra (batch) vs Openai Gpt 5 Mini

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 3 Ultra (batch)

Tool calling

Context window

512K

512,288 tokens · ~384K words

Model page
Openai

Model

Openai Gpt 5 Mini

Tool calling

Context window

1M

1,000,000 tokens · ~750K 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 3 Ultra (batch)512K
Openai Gpt 5 Mini1M

Openai Gpt 5 Mini has about 2× the context window of the other in this pair.

Openai Gpt 5 Mini has 95% more context capacity (1000K vs 512K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Openai Gpt 5 Mini. Its 1000K context fits entire documents without chunking (vs 512K).

Full specs

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

SpecNemotron 3 Ultra (batch)Openai Gpt 5 Mini
Context window512,288 tokens (512K)1,000,000 tokens (1000K)
Max output tokensN/A16,384 tokens (16K)
Speed tierBalancedFast
VisionNoNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateJun 2026N/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.

ProviderNemotron 3 Ultra (batch) inNemotron 3 Ultra (batch) outOpenai Gpt 5 Mini inOpenai Gpt 5 Mini out
Snowflake$0.300/M$1.20/M

Frequently asked questions

Openai Gpt 5 Mini has a larger context window: 1000K tokens vs 512K. 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.

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
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