Context Window

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Labs Leanstral 1 5 1 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.

Mistral

Model

Labs Leanstral 1 5 1

Tool calling

Context window

262K

262,144 tokens · ~197K 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.

Labs Leanstral 1 5 1262K
MiniMax M2.7205K

Labs Leanstral 1 5 1 has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Labs Leanstral 1 5 1 has 28% more context capacity (262K vs 204K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Labs Leanstral 1 5 1. Its 262K context fits entire documents without chunking (vs 204K).

Full specs

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

SpecLabs Leanstral 1 5 1MiniMax M2.7
Context window262,144 tokens (262K)204,800 tokens (204K)
Max output tokens131,072 tokens (131K)131,072 tokens (131K)
Speed tierBalancedFast
VisionNoNo
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.

ProviderLabs Leanstral 1 5 1 inLabs Leanstral 1 5 1 outMiniMax M2.7 inMiniMax M2.7 out
Deepinfra——$0.250/M$1.00/M
Mistral————
Novita——$0.300/M$1.20/M
Openrouter——$0.300/M$1.20/M
Sambanova——$0.600/M$2.40/M
Together Ai——$0.300/M$1.20/M

Frequently asked questions

Labs Leanstral 1 5 1 has a larger context window: 262K tokens vs 204K. 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
Full history
Repeated info
Old context
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Current turn

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