Context Window

Compare

Amazon Titan Text Lite vs Glm 4 5 Air 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.

Amazon

Model

Amazon Titan Text Lite

Context window

42K

42,000 tokens · ~32K words

Model page
Z Ai

Model

Glm 4 5 Air Fp8

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 Lite42K
Glm 4 5 Air Fp8128K

Glm 4 5 Air Fp8 has about 3× the context window of the other in this pair.

Token calculator →Memory savings →

Glm 4 5 Air Fp8 has 204% more context capacity (128K vs 42K tokens). Glm 4 5 Air Fp8 is 33% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Glm 4 5 Air Fp8. Its 128K context fits entire documents without chunking (vs 42K).

  • RAG / high-volume retrieval

    Use Glm 4 5 Air Fp8. Input tokens are 33% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecAmazon Titan Text LiteGlm 4 5 Air Fp8
Context window42,000 tokens (42K)128,000 tokens (128K)
Max output tokens4,000 tokens (4K)N/A
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
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 Lite inAmazon Titan Text Lite outGlm 4 5 Air Fp8 inGlm 4 5 Air Fp8 out
Aws Bedrock$0.300/M$0.400/M——
Together Ai——$0.200/M$1.10/M

Frequently asked questions

Glm 4 5 Air Fp8 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.

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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