Context Window

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Minimax M2p1 vs Nvidia Nemotron 3 5 Lightning 30b A3b

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 M2p1

Tool calling

Context window

205K

204,800 tokens · ~154K words

Model page
Nvidia

Model

Nvidia Nemotron 3 5 Lightning 30b A3b

Context window

66K

65,536 tokens · ~49K 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 M2p1205K
Nvidia Nemotron 3 5 Lightning 30b A3b66K

Minimax M2p1 has about 3.1× the context window of the other in this pair.

Token calculator →Memory savings →

Minimax M2p1 has 212% more context capacity (204K vs 65K tokens). Nvidia Nemotron 3 5 Lightning 30b A3b is 50% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Minimax M2p1. Its 204K context fits entire documents without chunking (vs 65K).

  • RAG / high-volume retrieval

    Use Nvidia Nemotron 3 5 Lightning 30b A3b. Input tokens are 50% 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 M2p1Nvidia Nemotron 3 5 Lightning 30b A3b
Context window204,800 tokens (204K)65,536 tokens (65K)
Max output tokens204,800 tokens (204K)N/A
Speed tierFastFast
VisionNoNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingYesYes
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.

ProviderMinimax M2p1 inMinimax M2p1 outNvidia Nemotron 3 5 Lightning 30b A3b inNvidia Nemotron 3 5 Lightning 30b A3b out
Fireworks$0.300/M$1.20/M——
Gmi——$0.150/M$1.00/M

Frequently asked questions

Minimax M2p1 has a larger context window: 204K tokens vs 65K. 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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Example: a multi-turn chat session

Without Mem0~128K tokens sent
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Repeated info
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