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Fw Glm 5 1 vs Morph Glm53 744b

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

Fw Glm 5 1

Tool calling

Context window

203K

202,800 tokens · ~152K words

Model page
Z Ai

Model

Morph Glm53 744b

Tool calling

Context window

1.0M

1,048,576 tokens · ~786K 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 Glm 5 1203K
Morph Glm53 744b1.0M

Morph Glm53 744b has about 5.2× the context window of the other in this pair.

Morph Glm53 744b has 417% more context capacity (1048K vs 202K tokens). Morph Glm53 744b is 22% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Morph Glm53 744b. Its 1048K context fits entire documents without chunking (vs 202K).

  • RAG / high-volume retrieval

    Use Morph Glm53 744b. Input tokens are 22% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Morph Glm53 744b. Its 1048K 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.

SpecFw Glm 5 1Morph Glm53 744b
Context window202,800 tokens (202K)1,048,576 tokens (1048K)
Max output tokens131,072 tokens (131K)1,048,576 tokens (1048K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingYesYes
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 Glm 5 1 inFw Glm 5 1 outMorph Glm53 744b inMorph Glm53 744b out
Azure$1.54/M$4.84/M——
Morph——$1.19/M$6.00/M

Frequently asked questions

Morph Glm53 744b has a larger context window: 1048K 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