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Amazon Titan Text Lite vs Minimax M1 80k

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
Minimax

Model

Minimax M1 80k

Context window

4K

4,096 tokens · ~3K 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
Minimax M1 80k4K

Amazon Titan Text Lite has about 10.3× the context window of the other in this pair.

Amazon Titan Text Lite has 925% more context capacity (42K vs 4K tokens). Minimax M1 80k is 66% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Amazon Titan Text Lite. Its 42K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Minimax M1 80k. Input tokens are 66% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Minimax M1 80k. Its 4K 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 LiteMinimax M1 80k
Context window42,000 tokens (42K)4,096 tokens (4K)
Max output tokens4,000 tokens (4K)4,096 tokens (4K)
Speed tierBalancedFast
VisionNoNo
Function callingNoNo
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 Lite inAmazon Titan Text Lite outMinimax M1 80k inMinimax M1 80k out
Aws Bedrock$0.300/M$0.400/M
Fireworks$0.100/M$0.100/M
Novita$0.550/M$2.20/M

Frequently asked questions

Amazon Titan Text Lite has a larger context window: 42K tokens vs 4K. 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