Context Window

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Amazon Titan Text Premier vs Openorca 7b

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
Microsoft

Model

Openorca 7b

Context window

33K

32,768 tokens · ~25K 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
Openorca 7b33K

Amazon Titan Text Premier has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Amazon Titan Text Premier has 28% more context capacity (42K vs 32K tokens). Openorca 7b 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 32K).

  • RAG / high-volume retrieval

    Use Openorca 7b. Input tokens are 60% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Openorca 7b. Its 32K 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 PremierOpenorca 7b
Context window42,000 tokens (42K)32,768 tokens (32K)
Max output tokens32,000 tokens (32K)32,768 tokens (32K)
Speed tierBalancedFast
VisionNoNo
Function callingNoNo
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 outOpenorca 7b inOpenorca 7b out
Aws Bedrock$0.500/M$1.50/M——
Fireworks——$0.200/M$0.200/M

Frequently asked questions

Amazon Titan Text Premier has a larger context window: 42K tokens vs 32K. 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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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