Context Window

Compare

MiniMax M2.7 vs Qwen2 5 Vl 72b

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 M2.7

Tool calling

Context window

205K

204,800 tokens · ~154K words

Model page
Alibaba

Model

Qwen2 5 Vl 72b

Image inputTool calling

Context window

131K

131,072 tokens · ~98K 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 M2.7205K
Qwen2 5 Vl 72b131K

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

Token calculator →Memory savings →

MiniMax M2.7 has 56% more context capacity (204K vs 131K tokens). Qwen2 5 Vl 72b is 56% 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 131K).

  • RAG / high-volume retrieval

    Use Qwen2 5 Vl 72b. Input tokens are 56% 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 M2.7Qwen2 5 Vl 72b
Context window204,800 tokens (204K)131,072 tokens (131K)
Max output tokens131,072 tokens (131K)131,072 tokens (131K)
Speed tierFastDeep
VisionNoYes
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateMar 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 M2.7 inMiniMax M2.7 outQwen2 5 Vl 72b inQwen2 5 Vl 72b out
Nebius——$0.130/M$0.400/M
Novita——$0.800/M$0.800/M
Ovhcloud——$0.910/M$0.910/M
Sambanova$0.300/M$1.20/M——

Frequently asked questions

MiniMax M2.7 has a larger context window: 204K tokens vs 131K. 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