LLM Reference

Jamba-Instruct

Released
2024-05-02
Last refreshed
2026-05-16
Status
Researched 244d ago
Open sourceCommercial use: permittedLong context

Jamba-Instruct is worth evaluating for long context when its provider route and context window match the workload.

Use it for

  • Teams evaluating long context
  • Workloads that can use a 256k context window
  • Buyers comparing 2 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads
  • Strict JSON or tool-calling flows
Specifications
Family
Jamba
Released
2024-05-02
Context
256k
Parameters
52B (12B active)
Architecture
Mixture of Experts
Knowledge cutoff
2024-03
Specialization
general
Openness
Open source
License
Apache 2.0OSI-approvedCommercial use: permitted
Weights
Unknown
Code
Unknown
Training
Fine-tuned
Created by

Developing AI for natural language understanding.

Tel Aviv, Israel
Founded 2017
Website
Pricing
Output / 1M
$0.700
Input / 1M
$0.500

Cheapest of 2 routes · AI21 Studio

About

Jamba-Instruct, developed by AI21 Labs, is a cutting-edge large language model tailored for enterprise applications. It boasts a remarkable 256,000-token context window, enabling it to process vast amounts of data, equivalent to an 800-page novel, making it ideal for tasks like summarization, question answering, and document analysis. Utilizing a hybrid architecture that blends Structured State Space (SSM) technology with traditional Transformer layers, Jamba-Instruct is designed for optimal performance and efficiency in managing long-context scenarios.

Top use-case fit

Long context

Included by capability and metadata signals in the decision map.

Capabilities

No model capability flags are currently sourced.

Benchmark peer barsfor Long context

No task-mapped benchmark peers are available for this model yet.

Migration checks

No linked migration route is available for this model yet.