Context Window

Compare

Amazon Titan Text Express vs Glm 4p5

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.

Amazon

Model

Amazon Titan Text Express

Context window

42K

42,000 tokens · ~32K words

Model page
Z Ai

Model

Glm 4p5

Tool calling

Context window

128K

128,000 tokens · ~96K 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.

Amazon Titan Text Express42K
Glm 4p5128K

Glm 4p5 has about 3× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 4p5 has 204% more context capacity (128K vs 42K tokens). Glm 4p5 is 57% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 4p5. Its 128K context fits entire documents without chunking (vs 42K).

  • RAG / high-volume retrieval

    Use Glm 4p5. Input tokens are 57% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Glm 4p5. Its 96K 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.

SpecAmazon Titan Text ExpressGlm 4p5
Context window42,000 tokens (42K)128,000 tokens (128K)
Max output tokens8,000 tokens (8K)96,000 tokens (96K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoNo
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.

ProviderAmazon Titan Text Express inAmazon Titan Text Express outGlm 4p5 inGlm 4p5 out
Aws Bedrock$1.30/M$1.70/M——
Fireworks——$0.550/M$2.19/M

Frequently asked questions

Glm 4p5 has a larger context window: 128K tokens vs 42K. 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.

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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