AI21's large hybrid SSM-Transformer model with a 256K context window. Uses a novel Jamba architecture combining Mamba SSM layers with Transformer attention for efficient long-context processing.
Use our main calculator for more detailed estimates including input/output combinations.
Key Features
Hybrid SSM-Transformer (Jamba) architecture
256K token context window
94B active parameters (398B total MoE)
Efficient long-context processing
Strong performance on reasoning and coding
Common Use Cases
Long-document analysis and summarization
Enterprise knowledge retrieval
Multi-document reasoning
Coding assistance
Complex content generation
Ratings & Feedback
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Frequently Asked Questions
What is a token in the context of Large Language Models (LLMs)?
In Large Language Models (LLMs), a 'token' is the smallest unit of text the model processes. Tokens can be entire words, subwords, or even individual characters, depending on the language and tokenization method. Understanding tokens is essential for optimizing your content and managing costs, as models operate within specific token limits.
Why is understanding token count important for using LLMs?
Knowing the token count of your input is crucial because LLMs have maximum token limits per request. Accurate token counting ensures your inputs and outputs stay within these limits, preventing errors and optimizing performance. Additionally, token usage directly impacts the cost of using these models, making it vital for budget management.
What factors affect LLM pricing?
LLM pricing typically depends on: (1) Model capability - more powerful models cost more, (2) Token type - input vs. output tokens are often priced differently, (3) Volume - some providers offer discounts for high volume, (4) Features - specialized capabilities may incur additional costs, and (5) Deployment type - cloud API vs. dedicated deployments have different pricing structures.
What is a context window in LLMs?
The context window refers to the maximum number of tokens an LLM can process in a single request. It represents the "memory" of the model during a conversation or analysis. Larger context windows allow the model to consider more information when generating responses, but may cost more to use.
How can I optimize my prompts to use fewer tokens?
To optimize prompts: (1) Be concise and direct, (2) Remove unnecessary context and redundant information, (3) Use efficient formatting, (4) Avoid repetitive instructions, (5) For complex tasks, consider breaking them into smaller, focused prompts, and (6) Use our TokenCalculator.com tool to measure and refine your prompt efficiency.