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Ai21 Jamba Instruct vs Anthropic Claude

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
Anthropic

Model

Anthropic Claude

Context window

100K

100,000 tokens · ~75K 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
Anthropic Claude100K

Anthropic Claude has about 1.4× the context window of the other in this pair.

Anthropic Claude has 42% more context capacity (100K vs 70K tokens). Ai21 Jamba Instruct is 93% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Anthropic Claude. Its 100K context fits entire documents without chunking (vs 70K).

  • RAG / high-volume retrieval

    Use Ai21 Jamba Instruct. Input tokens are 93% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Anthropic Claude. Its 8K 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 InstructAnthropic Claude
Context window70,000 tokens (70K)100,000 tokens (100K)
Max output tokens4,096 tokens (4K)8,191 tokens (8K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APINoYes
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 outAnthropic Claude inAnthropic Claude out
Aws Bedrock$0.500/M$0.700/M$8.00/M$24.00/M

Frequently asked questions

Anthropic Claude has a larger context window: 100K 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.

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