Context Window

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Llama3 3 70b 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

Llama3 3 70b

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.

Llama3 3 70b128K
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 53% 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 53% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecLlama3 3 70bMiniMax M2.7
Context window128,000 tokens (128K)204,800 tokens (204K)
Max output tokensN/A131,072 tokens (131K)
Speed tierDeepFast
VisionNoNo
Function callingNoYes
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.

ProviderLlama3 3 70b inLlama3 3 70b outMiniMax M2.7 inMiniMax M2.7 out
Gradient$0.650/M$0.650/M——
Sambanova——$0.300/M$1.20/M
Snowflake————

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.

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