Context Window

Compare

Jamba 1 5 vs Trinity Large Thinking (free)

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.

Ai21

Model

Jamba 1 5

Context window

256K

256,000 tokens · ~192K words

Model page
Arcee Ai

Model

Trinity Large Thinking (free)

Tool calling

Context window

262K

262,144 tokens · ~197K 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.

Jamba 1 5256K
Trinity Large Thinking (free)262K

Trinity Large Thinking (free) has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Trinity Large Thinking (free) has 2% more context capacity (262K vs 256K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Trinity Large Thinking (free). Its 262K context fits entire documents without chunking (vs 256K).

  • Long output (reports, code files)

    Use Jamba 1 5. 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.

SpecJamba 1 5Trinity Large Thinking (free)
Context window256,000 tokens (256K)262,144 tokens (262K)
Max output tokens256,000 tokens (256K)80,000 tokens (80K)
Speed tierBalancedDeep
VisionNoNo
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoNo
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.

ProviderJamba 1 5 inJamba 1 5 outTrinity Large Thinking (free) inTrinity Large Thinking (free) out
Ai21$0.200/M$0.400/M——
Google Vertex$0.200/M$0.400/M——

Frequently asked questions

Trinity Large Thinking (free) has a larger context window: 262K tokens vs 256K. 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