Context Window

Compare

Kimi Latest 8k vs Minimax Minimax M2 1

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.

Moonshot

Model

Kimi Latest 8k

Image inputTool calling

Context window

8K

8,192 tokens · ~6K words

Model page
Minimax

Model

Minimax Minimax M2 1

Tool calling

Context window

196K

196,000 tokens · ~147K 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.

Kimi Latest 8k8K
Minimax Minimax M2 1196K

Minimax Minimax M2 1 has about 23.9× the context window of the other in this pair.

Token calculator →Memory savings →

Minimax Minimax M2 1 has 2292% more context capacity (196K vs 8K tokens). Kimi Latest 8k is 44% 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 8K).

  • RAG / high-volume retrieval

    Use Kimi Latest 8k. Input tokens are 44% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecKimi Latest 8kMinimax Minimax M2 1
Context window8,192 tokens (8K)196,000 tokens (196K)
Max output tokens8,192 tokens (8K)8,192 tokens (8K)
Speed tierBalancedFast
VisionYesNo
Function callingYesYes
Extended thinkingNoNo
Prompt cachingYesNo
Batch APINoNo
Release dateN/AN/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.

ProviderKimi Latest 8k inKimi Latest 8k outMinimax Minimax M2 1 inMinimax Minimax M2 1 out
Aws Bedrock——$0.360/M$1.44/M
Moonshot$0.200/M$2.00/M——

Frequently asked questions

Minimax Minimax M2 1 has a larger context window: 196K tokens vs 8K. 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