Jamba 1.5 MinivsJamba 1.5 Large

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 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.