Jamba 1.5 Mini (SSM)vsJamba 1.5 Large

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.

Jamba 1.5 Large, developed by AI21 Labs, is a hybrid model combining the Mamba state-space architecture with traditional Transformer layers, featuring 398 billion total parameters (94 billion active) and a 256K token context window. The novel SSM-Transformer design enables efficient processing of very long sequences while maintaining strong performance on reasoning and generation tasks. The architecture offers better throughput than pure Transformer models at equivalent quality, reducing inference costs for long-context workloads. Priced at $2.00 per million input tokens and $8.00 per million output tokens. As an open-source model, it can be self-hosted for enterprise deployments. Jamba 1.5 Large demonstrates that architectural diversity beyond the dominant Transformer paradigm can yield practical advantages, particularly for applications requiring processing of lengthy legal, scientific, or financial documents.