Context Window

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Minimax Minimax M2 1 vs Sonar Reasoning Pro

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 Minimax M2 1

Tool calling

Context window

196K

196,000 tokens · ~147K words

Model page
Perplexity

Model

Sonar Reasoning Pro

Image input

Context window

128K

128,000 tokens · ~96K 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 Minimax M2 1196K
Sonar Reasoning Pro128K

Minimax Minimax M2 1 has about 1.5× the context window of the other in this pair.

Token calculator →Memory savings →

Minimax Minimax M2 1 has 53% more context capacity (196K vs 128K tokens). Minimax Minimax M2 1 is 82% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Minimax Minimax M2 1. Its 196K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Minimax Minimax M2 1. Input tokens are 82% 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 Minimax M2 1Sonar Reasoning Pro
Context window196,000 tokens (196K)128,000 tokens (128K)
Max output tokens8,192 tokens (8K)N/A
Speed tierFastDeep
VisionNoYes
Function callingYesNo
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AMar 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.

ProviderMinimax Minimax M2 1 inMinimax Minimax M2 1 outSonar Reasoning Pro inSonar Reasoning Pro out
Aws Bedrock$0.360/M$1.44/M——
Perplexity——$2.00/M$8.00/M

Frequently asked questions

Minimax Minimax M2 1 has a larger context window: 196K 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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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