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Glm 5p1 vs MiniMax-01

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.

Z Ai

Model

Glm 5p1

Context window

203K

202,800 tokens · ~152K words

Model page
Minimax

Model

MiniMax-01

Image input

Context window

1.0M

1,000,192 tokens · ~750K 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.

Glm 5p1203K
MiniMax-011.0M

MiniMax-01 has about 4.9× the context window of the other in this pair.

MiniMax-01 has 393% more context capacity (1000K vs 202K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use MiniMax-01. Its 1000K context fits entire documents without chunking (vs 202K).

  • Long output (reports, code files)

    Use MiniMax-01. Its 1000K max output lets you generate complete artifacts in one request.

Full specs

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

SpecGlm 5p1MiniMax-01
Context window202,800 tokens (202K)1,000,192 tokens (1000K)
Max output tokens202,800 tokens (202K)1,000,192 tokens (1000K)
Speed tierBalancedFast
VisionNoYes
Function callingNoNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateN/AJan 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.

ProviderGlm 5p1 inGlm 5p1 outMiniMax-01 inMiniMax-01 out
Fireworks$1.40/M$4.40/M

Frequently asked questions

MiniMax-01 has a larger context window: 1000K tokens vs 202K. 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.

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