Context Window

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Glm 5p3 Flash Us vs MiniMax M3 (batch)

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 5p3 Flash Us

Image inputTool calling

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Minimax

Model

MiniMax M3 (batch)

Image inputTool calling

Context window

524K

524,288 tokens · ~393K 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 5p3 Flash Us1.0M
MiniMax M3 (batch)524K

Glm 5p3 Flash Us has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 5p3 Flash Us has 100% more context capacity (1048K vs 524K tokens). Glm 5p3 Flash Us is 24% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 5p3 Flash Us. Its 1048K context fits entire documents without chunking (vs 524K).

  • RAG / high-volume retrieval

    Use Glm 5p3 Flash Us. Input tokens are 24% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGlm 5p3 Flash UsMiniMax M3 (batch)
Context window1,048,576 tokens (1048K)524,288 tokens (524K)
Max output tokensN/A471,859 tokens (471K)
Speed tierFastFast
VisionYesYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/AMay 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.

ProviderGlm 5p3 Flash Us inGlm 5p3 Flash Us outMiniMax M3 (batch) inMiniMax M3 (batch) out
Fireworks$0.225/M$0.750/M——
Openrouter——$0.300/M$1.20/M

Frequently asked questions

Glm 5p3 Flash Us has a larger context window: 1048K tokens vs 524K. 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