Activefrontierllm Proprietary

Jamba 1.5 Large

by AI21 Labs· Released August 2024· Cutoff April 2024

Jamba 1.5 Large is a state-of-the-art large language model from AI21 Labs, built on a hybrid SSM-Transformer architecture that combines Mamba structured state space models with traditional transformer layers. It offers a 256K token context window and excels in long-context tasks, multilingual support, and structured output generation.

Official Site API Docs

Input cost

$5.00 per 1M tokens

Output cost

$15.00 per 1M tokens

Context window

256K tokens

Max output

4096 tokens

Modalities

text

License

proprietary

Capabilities

Function CallingStructured Output (JSON Mode)Long Context UnderstandingMultilingual SupportCode GenerationStreamingTool Use

Best For

Enterprise applications requiring long-context understanding, multilingual support, and reliable structured output generation.

Strengths

Industry-leading 256K token context window
Strong multilingual performance across 30+ languages
High accuracy in structured output and function calling
Efficient inference due to hybrid architecture

Limitations

No native vision or image input support
Limited to text-only modalities
Smaller parameter count compared to some frontier models (e.g., GPT-4)
Pricing is higher than some open-source alternatives

Use Cases

Long-document analysis and summarization

Multilingual customer support chatbots

Code generation and debugging across multiple languages

Data extraction and structured output from large texts

Complex reasoning tasks with tool use

Enterprise knowledge management and retrieval-augmented generation

Legal and financial document processing

Improvements Over Previous Model

Context window increased from 200K to 256K tokens compared to Jamba 1.5 Mini
Improved multilingual capabilities with support for over 30 languages
Enhanced structured output and function calling accuracy
Better performance on long-context benchmarks (e.g., RULER, LongBench)
Lower latency due to optimized hybrid architecture

Back to all models

Use Cases

Long-document analysis and summarization

Multilingual customer support chatbots

Code generation and debugging across multiple languages

Data extraction and structured output from large texts

Complex reasoning tasks with tool use

Enterprise knowledge management and retrieval-augmented generation

Legal and financial document processing

Improvements Over Previous Model

Context window increased from 200K to 256K tokens compared to Jamba 1.5 Mini

Improved multilingual capabilities with support for over 30 languages

Enhanced structured output and function calling accuracy

Better performance on long-context benchmarks (e.g., RULER, LongBench)

Lower latency due to optimized hybrid architecture