What is Jamba?
Jamba is a family of open-weight language models from AI21 Labs, an Israeli company. Its distinctive feature is the architecture: a hybrid that combines Mamba-style state-space layers with Transformer layers, which makes long-context inference more memory-efficient than a pure Transformer.
That efficiency translates into a large context window at lower cost, which suits long-document and high-throughput work. You use Jamba through AI21's API and Studio, through cloud marketplaces, or by self-hosting the published open weights.
Jamba is worth evaluating when long context and efficiency are the priority, and when you want open weights from a Western lab that you can deploy in your own environment.
What it's best for
- Long-context tasks: processing large documents efficiently thanks to the hybrid architecture.
- High-throughput workloads where memory efficiency lowers the cost per request.
- Self-hosting open weights from a Western lab for data control.
- Retrieval-augmented generation and enterprise workflows through AI21's tooling.
- Teams that want an alternative architecture to evaluate alongside standard Transformers.
Where it falls short
- A consumer chatbot experience: AI21 targets developers and enterprises.
- Native image, audio, or video generation; Jamba is a text model family.
- Topping general leaderboards on every task; its edge is long-context efficiency rather than being the single strongest model everywhere.
Ways in
Developers start in AI21 Studio: get a key and call the Jamba models by API. The models are also available through major cloud marketplaces for deployment near your data.
For full control, download the open weights and self-host them under common runtimes.
Using the long context well
Pass long inputs directly rather than pre-chunking when you can; the architecture is designed to keep large contexts in memory efficiently.
For grounded answers, supply the retrieved documents and ask the model to answer only from them and cite sources.
What Jamba costs
Approximate, in USD, as of January 2026. Prices change often. Confirm on the official site before you rely on them.
Open weights
$0 (self-host)
Download and run the Jamba open-weight models; you pay only your own compute.
AI21 Studio API
Usage-based
Priced per token by model; free credits for evaluation.
Enterprise / cloud
Custom
Deployment through cloud marketplaces and enterprise agreements.
Example prompts
Copy these into Jamba as starting points, then adapt them to your task.
Using the full document below, answer these questions one by one. For each answer, quote the sentence it is based on. If the document does not answer a question, say so.
Summarize this long transcript into a one-page brief: key decisions, open questions, and action items with owners. Keep it factual and do not add anything not stated.
Answer the question using only the retrieved passages. Cite the source passage for each claim and flag any gaps the passages do not cover.
We process very long inputs at high volume. Explain how Jamba's hybrid Mamba-Transformer design affects memory and cost compared with a standard Transformer of similar quality.
Jamba
common questions.
Direct answers to the questions we get asked the most. If yours isn't covered, write to the team.