Context Window

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Meta Llama3 2 11b Instruct vs MiniMax M2.7

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 11b Instruct

Image inputTool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Minimax

Model

MiniMax M2.7

Tool calling

Context window

205K

204,800 tokens · ~154K 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 11b Instruct128K
MiniMax M2.7205K

MiniMax M2.7 has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

MiniMax M2.7 has 60% more context capacity (204K vs 128K tokens). MiniMax M2.7 is 14% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use MiniMax M2.7. Its 204K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use MiniMax M2.7. Input tokens are 14% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use MiniMax M2.7. Its 131K 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 11b InstructMiniMax M2.7
Context window128,000 tokens (128K)204,800 tokens (204K)
Max output tokens4,096 tokens (4K)131,072 tokens (131K)
Speed tierFastFast
VisionYesNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AMar 2026

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 11b Instruct inMeta Llama3 2 11b Instruct outMiniMax M2.7 inMiniMax M2.7 out
Aws Bedrock$0.350/M$0.350/M——
Sambanova——$0.300/M$1.20/M

Frequently asked questions

MiniMax M2.7 has a larger context window: 204K 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.

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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.

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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