Compare

Amazon Titan Text Lite vs MiniMax M2-her

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 M2-her

Context window

66K

65,536 tokens · ~49K 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 M2-her66K

MiniMax M2-her has about 1.6× the context window of the other in this pair.

MiniMax M2-her has 56% more context capacity (65K vs 42K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use MiniMax M2-her. Its 65K context fits entire documents without chunking (vs 42K).

  • Long output (reports, code files)

    Use Amazon Titan Text Lite. 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 M2-her
Context window42,000 tokens (42K)65,536 tokens (65K)
Max output tokens4,000 tokens (4K)2,048 tokens (2K)
Speed tierBalancedFast
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoNo
Release dateN/AJan 2026

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 M2-her inMiniMax M2-her out
Aws Bedrock$0.300/M$0.400/M

Frequently asked questions

MiniMax M2-her has a larger context window: 65K 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.

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