Jamba 1.5 MinivsJamba 1.5 Mini (SSM)

Jamba 1.5 Mini, developed by AI21 Labs, is a compact hybrid SSM-Transformer model with 52 billion total parameters (12 billion active) and a 256K token context window. The model applies AI21 Labs' hybrid Mamba-Transformer architecture in a smaller, more efficient package designed for cost-effective long-context processing. It handles summarization, document analysis, and RAG tasks with the efficiency advantages of state-space models on long sequences. Priced at $0.20 per million input tokens and $0.40 per million output tokens, it offers affordable access to long-context processing. As an open-source model, Jamba 1.5 Mini can be deployed on more modest hardware compared to its larger sibling. The model targets production applications where processing long documents efficiently at low cost matters more than achieving maximum benchmark scores.

Jamba 1.5 Mini SSM, developed by AI21 Labs, is a variant of the Jamba 1.5 Mini model with 52 billion total parameters (12 billion active) and a 256K token context window. The model emphasizes the state-space model components of AI21 Labs' hybrid architecture, optimizing for throughput on long-context workloads. It processes lengthy documents, transcripts, and data files efficiently with linear-time complexity rather than the quadratic scaling of standard Transformer attention. Priced at $0.20 per million input tokens and $0.40 per million output tokens. As an open-source model, it supports self-hosted deployment for organizations requiring maximum control over their inference infrastructure. The SSM-focused design makes it particularly efficient for batch processing of long documents where throughput optimization provides measurable cost savings.