Compare

Ai21 Jamba 1 5 Mini vs Jamba Large 1 6

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 1 5 Mini

Context window

256K

256,000 tokens · ~192K words

Model page
Ai21

Model

Jamba Large 1 6

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 1 5 Mini256K
Jamba Large 1 6256K

Same context window size for both models.

Ai21 Jamba 1 5 Mini and Jamba Large 1 6 have identical context windows (256K tokens). Ai21 Jamba 1 5 Mini is 90% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Ai21 Jamba 1 5 Mini. Input tokens are 90% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecAi21 Jamba 1 5 MiniJamba Large 1 6
Context window256,000 tokens (256K)256,000 tokens (256K)
Max output tokens256,000 tokens (256K)256,000 tokens (256K)
Speed tierFastDeep
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.

ProviderAi21 Jamba 1 5 Mini inAi21 Jamba 1 5 Mini outJamba Large 1 6 inJamba Large 1 6 out
Ai21$2.00/M$8.00/M
Aws Bedrock$0.200/M$0.400/M

Frequently asked questions

Jamba Large 1 6 has a larger context window: 256K 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.

Powered by Mem0

Use a smaller model.
Get better results.

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.

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