# Model Information Panel

#### 5.2.1 Context Length

* Token capacity
* Impact on performance

#### 5.2.2 Feature Indicators

* Text capability
* Vision capability
* Advanced reasoning capability

#### 5.2.3 Credit Usage Transparency

* Input credits per 1K tokens
* Output credits per 1K tokens
* How credits are calculated

***

## Model Information Breakdown

### 5.2.1 Context Length

This section explains how much information a model can handle in a single request.

#### • Token Capacity

* A **token** is a small unit of text (roughly 3–4 characters in English).
* Token capacity = maximum number of tokens the model can process at once.
* Includes:
  * User input
  * System instructions
  * Uploaded content
  * Model output

**Example:**If a model supports 128K tokens, it can handle long documents, multi-step conversations, or large prompts in one go.

***

#### • Impact on Performance

Context length directly affects:

* **Memory** – Larger context = better understanding of long conversations.
* **Document handling** – Can process large PDFs, codebases, reports.
* **Cost** – Larger context models often cost more per request.
* **Latency** – Bigger context windows may slightly increase response time.

**In short:**More context = more capability, but potentially higher cost and slower responses.

***

### 5.2.2 Feature Indicators

This section visually highlights what the model is capable of.

#### • Text Capability

Indicates the model can:

* Generate content
* Summarize
* Translate
* Code
* Answer questions

Almost all LLMs support text capability.

***

#### • Vision Capability

Indicates the model can:

* Analyze images
* Extract text from images
* Describe visuals
* Interpret charts or diagrams

If enabled, the model is multimodal (text + image).

***

#### • Advanced Reasoning Capability

Indicates stronger:

* Logical reasoning
* Multi-step problem solving
* Math and coding accuracy
* Complex decision-making

These models are typically:

* Slower than lightweight models
* More expensive
* Higher accuracy for complex tasks

***

### 5.2.3 Credit Usage Transparency

This section explains how model usage is billed.

#### • Input Credits per 1K Tokens

Cost charged for:

* Prompt text
* Uploaded files
* System instructions

Calculated per 1,000 tokens of input.

***

#### • Output Credits per 1K Tokens

Cost charged for:

* The model’s generated response

Also calculated per 1,000 tokens.

## Why This Structure Matters

* Compare models quickly
* Choose based on performance vs cost
* Understand technical limits before usage
* Avoid unexpected credit consumption


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.qolaba.ai/chatbot/model-selection/model-information-panel.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.