Context Window

Compare

Fw Minimax M3 vs Glm 5 1 Fp8

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

Fw Minimax M3

Image inputTool calling

Context window

512K

512,000 tokens · ~384K words

Model page
Z Ai

Model

Glm 5 1 Fp8

Context window

203K

202,752 tokens · ~152K 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.

Fw Minimax M3512K
Glm 5 1 Fp8203K

Fw Minimax M3 has about 2.5× the context window of the other in this pair.

Token calculator →Memory savings →

Fw Minimax M3 has 152% more context capacity (512K vs 202K tokens). Fw Minimax M3 is 76% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Fw Minimax M3. Its 512K context fits entire documents without chunking (vs 202K).

  • RAG / high-volume retrieval

    Use Fw Minimax M3. Input tokens are 76% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecFw Minimax M3Glm 5 1 Fp8
Context window512,000 tokens (512K)202,752 tokens (202K)
Max output tokens512,000 tokens (512K)N/A
Speed tierFastBalanced
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesYes
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.

ProviderFw Minimax M3 inFw Minimax M3 outGlm 5 1 Fp8 inGlm 5 1 Fp8 out
Azure$0.330/M$1.32/M——
Gmi——$1.40/M$4.40/M

Frequently asked questions

Fw Minimax M3 has a larger context window: 512K 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.

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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