A 1T parameter open-weight Mixture-of-Experts (MoE) model with 32B active parameters. This is the unaligned, pre-trained base model, suitable for further fine-tuning.
Use our main calculator for more detailed estimates including input/output combinations.
Key Features
1T total parameters (32B active)
Mixture-of-Experts (MoE) architecture
128K token context window
Open-weight
Strong multilingual (Chinese/English) and coding capabilities
Common Use Cases
Further fine-tuning for custom tasks
Research on large-scale models
Creative content generation
Complex reasoning tasks
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.