Context Window

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MiniMax M3 vs Nvidia Nemotron 3 5 Lightning

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.

Minimax

Model

MiniMax M3

Image inputTool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Nvidia

Model

Nvidia Nemotron 3 5 Lightning

Tool calling

Context window

262K

262,144 tokens · ~197K 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.

MiniMax M31.0M
Nvidia Nemotron 3 5 Lightning262K

MiniMax M3 has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

MiniMax M3 has 300% more context capacity (1048K vs 262K tokens). Nvidia Nemotron 3 5 Lightning is 83% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use MiniMax M3. Its 1048K context fits entire documents without chunking (vs 262K).

  • RAG / high-volume retrieval

    Use Nvidia Nemotron 3 5 Lightning. Input tokens are 83% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecMiniMax M3Nvidia Nemotron 3 5 Lightning
Context window1,048,576 tokens (1048K)262,144 tokens (262K)
Max output tokens131,072 tokens (131K)N/A
Speed tierFastBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesNo
Batch APINoNo
Release dateMay 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.

ProviderMiniMax M3 inMiniMax M3 outNvidia Nemotron 3 5 Lightning inNvidia Nemotron 3 5 Lightning out
Deepinfra——$0.050/M$0.200/M
Fireworks$0.300/M$1.20/M——
Minimax$0.300/M$1.20/M——

Frequently asked questions

MiniMax M3 has a larger context window: 1048K tokens vs 262K. 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