Context Window

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Amazon Titan Text Premier vs Granite 3 3 8b

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 Premier

Context window

42K

42,000 tokens · ~32K words

Model page
Ibm

Model

Granite 3 3 8b

Tool calling

Context window

8K

8,192 tokens · ~6K 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 Premier42K
Granite 3 3 8b8K

Amazon Titan Text Premier has about 5.1× the context window of the other in this pair.

Token calculator →Memory savings →

Amazon Titan Text Premier has 412% more context capacity (42K vs 8K tokens). Granite 3 3 8b is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Amazon Titan Text Premier. Its 42K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use Granite 3 3 8b. Input tokens are 60% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecAmazon Titan Text PremierGranite 3 3 8b
Context window42,000 tokens (42K)8,192 tokens (8K)
Max output tokens32,000 tokens (32K)N/A
Speed tierBalancedFast
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
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 Premier inAmazon Titan Text Premier outGranite 3 3 8b inGranite 3 3 8b out
Aws Bedrock$0.500/M$1.50/M——
Ibm Watsonx——$0.200/M$0.200/M
Replicate——$0.030/M$0.250/M

Frequently asked questions

Amazon Titan Text Premier has a larger context window: 42K tokens vs 8K. 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.
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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