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Amazon Titan Text Express vs Nemotron Nano 12B 2 VL

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
Nvidia

Model

Nemotron Nano 12B 2 VL

Image input

Context window

131K

131,072 tokens · ~98K 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
Nemotron Nano 12B 2 VL131K

Nemotron Nano 12B 2 VL has about 3.1× the context window of the other in this pair.

Nemotron Nano 12B 2 VL has 212% more context capacity (131K vs 42K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Nemotron Nano 12B 2 VL. Its 131K context fits entire documents without chunking (vs 42K).

Full specs

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

SpecAmazon Titan Text ExpressNemotron Nano 12B 2 VL
Context window42,000 tokens (42K)131,072 tokens (131K)
Max output tokens8,000 tokens (8K)N/A
Speed tierBalancedFast
VisionNoYes
Function callingNoNo
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AOct 2025

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 outNemotron Nano 12B 2 VL inNemotron Nano 12B 2 VL out
Aws Bedrock$1.30/M$1.70/M

Frequently asked questions

Nemotron Nano 12B 2 VL has a larger context window: 131K 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