Context Window

Compare

Ai21 Jamba Instruct vs Jamba 1 5

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 1 5

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 1 5256K

Jamba 1 5 has about 3.7× the context window of the other in this pair.

Token calculator →Memory savings →

Jamba 1 5 has 265% more context capacity (256K vs 70K tokens). Jamba 1 5 is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Jamba 1 5. Its 256K context fits entire documents without chunking (vs 70K).

  • RAG / high-volume retrieval

    Use Jamba 1 5. Input tokens are 60% cheaper — critical when sending large retrieved contexts.

  • 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.

SpecAi21 Jamba InstructJamba 1 5
Context window70,000 tokens (70K)256,000 tokens (256K)
Max output tokens4,096 tokens (4K)256,000 tokens (256K)
Speed tierBalancedBalanced
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 Instruct inAi21 Jamba Instruct outJamba 1 5 inJamba 1 5 out
Ai21——$0.200/M$0.400/M
Aws Bedrock$0.500/M$0.700/M——
Google Vertex——$0.200/M$0.400/M

Frequently asked questions

Jamba 1 5 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.
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.

Calculate your savingsLearn about Mem0

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