Context Window

Compare

Ai21 Jamba Instruct vs Jamba Large 1.7

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

Ai21 Jamba Instruct

Context window

70K

70,000 tokens · ~53K words

Model page
Ai21

Model

Jamba Large 1.7

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.

Ai21 Jamba Instruct70K
Jamba Large 1.7256K

Jamba Large 1.7 has about 3.7× the context window of the other in this pair.

Token calculator →Memory savings →

Jamba Large 1.7 has 265% more context capacity (256K vs 70K tokens). Ai21 Jamba Instruct is 75% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Jamba Large 1.7. Its 256K context fits entire documents without chunking (vs 70K).

  • RAG / high-volume retrieval

    Use Ai21 Jamba Instruct. Input tokens are 75% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Jamba Large 1.7. 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.

SpecAi21 Jamba InstructJamba Large 1.7
Context window70,000 tokens (70K)256,000 tokens (256K)
Max output tokens4,096 tokens (4K)256,000 tokens (256K)
Speed tierBalancedDeep
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
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.

ProviderAi21 Jamba Instruct inAi21 Jamba Instruct outJamba Large 1.7 inJamba Large 1.7 out
Ai21——$2.00/M$8.00/M
Aws Bedrock$0.500/M$0.700/M——

Frequently asked questions

Jamba Large 1.7 has a larger context window: 256K tokens vs 70K. 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