Context Window

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Meta Llama3 2 90b Instruct vs MiniMax M1

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.

Meta

Model

Meta Llama3 2 90b Instruct

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Minimax

Model

MiniMax M1

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.

Meta Llama3 2 90b Instruct128K
MiniMax M11M

MiniMax M1 has about 7.8× the context window of the other in this pair.

Token calculator →Memory savings →

MiniMax M1 has 681% more context capacity (1000K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use MiniMax M1. Its 1000K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use MiniMax M1. Its 40K 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.

SpecMeta Llama3 2 90b InstructMiniMax M1
Context window128,000 tokens (128K)1,000,000 tokens (1000K)
Max output tokens4,096 tokens (4K)40,000 tokens (40K)
Speed tierBalancedFast
VisionYesNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AJun 2025

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.

ProviderMeta Llama3 2 90b Instruct inMeta Llama3 2 90b Instruct outMiniMax M1 inMiniMax M1 out
Aws Bedrock$2.00/M$2.00/M——

Frequently asked questions

MiniMax M1 has a larger context window: 1000K 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.

Calculate your savingsLearn about Mem0

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