Jamba 1.5 Large is AI21 Labs' hybrid model combining Mamba SSM (State Space Model) blocks with standard transformer attention layers, resulting in an architecture that handles very long contexts efficiently without the memory explosion typical of pure-attention models. It supports a 256K token context window, making it one of the longest-context models available from a major AI lab.
The hybrid design gives Jamba 1.5 Large a favorable quality-throughput tradeoff: it processes long documents and extended conversations faster and with less memory than comparably capable transformer-only models. AI21 positions it for enterprise workflows involving large document corpora — legal review, financial analysis, lengthy research synthesis — where fitting everything in context at once is valuable.
Key Features
256K token context window for processing very long documents in a single pass
Hybrid Mamba SSM + Transformer architecture for memory-efficient long-context inference
High throughput on extended sequences compared to pure-attention models
Strong document understanding, summarization, and question answering
Enterprise-ready with AI21's API and safety filtering layer
Effective at multi-document reasoning where full context matters
Ideal Use Cases
Legal document review and clause extraction across lengthy contracts
Financial report analysis requiring ingestion of full annual filings
Research synthesis across multiple long academic papers in a single context
RAG-free long-document QA where chunking would lose critical cross-references
Enterprise knowledge base querying with large internal document sets
Example Prompts for Jamba 1.5 Large
Technical Specifications
| Provider | AI21 |
| Category | Text |
| Modality | Text -> Text |
| Context Window | 256K tokens |
Frequently Asked Questions
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