> ## Documentation Index
> Fetch the complete documentation index at: https://docs.galtea.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Model
> A representation of a LLM Model with cost information to calculate cost estimations
## What is a Model?
A model in Galtea represents a Large Language Model (LLM) configuration with associated cost information. This allows the platform to track, estimate, and report on the costs of using different LLMs across your products and evaluations.
Models are organization-wide and can be referenced across multiple products to ensure consistent cost tracking.
You can **create**, view and manage your models on the [Galtea dashboard](https://platform.galtea.ai/) or programmatically via the [SDK](/sdk/api/model/service).
## Use Cases for Models
Models in Galtea are primarily used for:
Monitor spending on AI models across different products and versions
Project future costs based on token usage patterns
Compare costs between different model providers and configurations
Generate accurate reports on LLM usage expenses
## Models vs. Evaluator Models
It's important to distinguish between a **Model** and an **Evaluator Model** in Galtea:
* **Model (This Page):** Represents the AI model your product is using. Its primary purpose in Galtea is for **cost estimation and tracking**. You link a Version to a Model to calculate the cost of its inference results.
* **Evaluator Model:** This is an LLM used by Galtea to perform "LLM-as-a-judge" evaluations for non-deterministic [Metrics](/concepts/metric). You select an Evaluator Model when creating a custom metric with a `judge_prompt`.
While both are LLMs, their roles within the Galtea platform are distinct: one is being tested, and the other is doing the testing.
## Model Properties
When creating a model in Galtea, you'll need to provide the following information:
The name of the model. This should be descriptive and indicate the provider and version.
**Example**: "GPT-4 Turbo" or "Claude 3 Haiku"
The cost in dollars per input token. This is the rate charged by the provider for tokens in your prompts.
**Example**: 0.00001 (representing \$0.00001 per token)
The cost in dollars per output token. This is the rate charged by the provider for tokens in the model's responses.
**Example**: 0.00003 (representing \$0.00003 per token)
The cost in dollars per cached input token. Some providers offer reduced rates for cached requests.
**Example**: 0.000005 (representing \$0.000005 per token)
The cost in dollars per input token when creating a cache entry.
**Example**: 0.000005
The cost in dollars per input token for contexts larger than 128k tokens, if applicable.
**Example**: 0.00002
The cost in dollars per output token for contexts larger than 128k tokens, if applicable.
**Example**: 0.00006
The provider of the tokenizer used by the model. This is important for accurate token counting and cost estimation.
Right now, the only supported tokenizers are:
**Examples**:
* "OpenAI" (uses [tiktoken](https://github.com/openai/tiktoken))
* "Anthropic" (uses [anthropic-tokenizer](https://lunary.ai/anthropic-tokenizer))
The provider or source of the pricing model. This can be a URL to the model's pricing page or documentation.
For instance: [https://openai.com/api/pricing/](https://openai.com/api/pricing/)
## Related
How Galtea's concepts connect — diagram + per-entity quick reference.
Manage models using the Python SDK
Link a model to a version for cost tracking.
Select evaluator models when creating custom metrics.