Context Window

Compare

Amazon Titan Text Express vs Codestral 2508

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
Mistral

Model

Codestral 2508

Tool calling

Context window

256K

256,000 tokens · ~192K 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
Codestral 2508256K

Codestral 2508 has about 6.1× the context window of the other in this pair.

Token calculator →Memory savings →

Codestral 2508 has 509% more context capacity (256K vs 42K tokens). Codestral 2508 is 76% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Codestral 2508. Its 256K context fits entire documents without chunking (vs 42K).

  • RAG / high-volume retrieval

    Use Codestral 2508. Input tokens are 76% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Codestral 2508. Its 256K 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 ExpressCodestral 2508
Context window42,000 tokens (42K)256,000 tokens (256K)
Max output tokens8,000 tokens (8K)256,000 tokens (256K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoNo
Release dateN/AAug 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 outCodestral 2508 inCodestral 2508 out
Aws Bedrock$1.30/M$1.70/M——
Mistral——$0.300/M$0.900/M

Frequently asked questions

Codestral 2508 has a larger context window: 256K 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