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Amazon Titan Text Express vs DeepSeek V4 Pro

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
Deepseek

Model

DeepSeek V4 Pro

Tool calling

Context window

1.0M

1,048,576 tokens · ~786K 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
DeepSeek V4 Pro1.0M

DeepSeek V4 Pro has about 25× the context window of the other in this pair.

DeepSeek V4 Pro has 2396% more context capacity (1048K vs 42K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use DeepSeek V4 Pro. Its 1048K context fits entire documents without chunking (vs 42K).

  • Long output (reports, code files)

    Use DeepSeek V4 Pro. Its 384K 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 ExpressDeepSeek V4 Pro
Context window42,000 tokens (42K)1,048,576 tokens (1048K)
Max output tokens8,000 tokens (8K)384,000 tokens (384K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AApr 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 Express inAmazon Titan Text Express outDeepSeek V4 Pro inDeepSeek V4 Pro out
Aws Bedrock$1.30/M$1.70/M

Frequently asked questions

DeepSeek V4 Pro has a larger context window: 1048K 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