Activefastllm Proprietary

Jamba-Instruct

by AI21 Labs· Released March 2024

Jamba-Instruct is a hybrid SSM-Transformer model by AI21 Labs, combining Mamba structured state space with transformer attention for efficient long-context processing. It offers a 256K token context window and is optimized for instruction-following tasks, making it suitable for enterprise applications requiring high throughput and low latency.

Official Site API Docs 📄 Research Paper

Input cost

$0.50 per 1M tokens

Output cost

$0.70 per 1M tokens

Context window

256K tokens

Max output

—

Modalities

text

License

proprietary

Capabilities

Instruction FollowingLong Context ProcessingFunction CallingStreamingJSON ModeCode GenerationReasoning

Best For

Enterprise applications requiring efficient long-context understanding and instruction following with low latency.

Strengths

256K token context window
Hybrid SSM-Transformer architecture for efficiency
Low latency and high throughput
Strong instruction following
Cost-effective pricing

Limitations

No vision or multimodal capabilities
Smaller parameter count compared to frontier models
Less known for creative writing
Limited community adoption vs. open-source models

Use Cases

Document analysis and summarization

Long-form content generation

Customer support chatbots

Code generation and debugging

Data extraction from large texts

Enterprise knowledge base querying

Legal document review

Improvements Over Previous Model

First model in the Jamba family, no direct predecessor
Introduces hybrid SSM-Transformer architecture
256K token context window
Optimized for instruction following
Efficient inference with lower memory footprint

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