> For the complete documentation index, see [llms.txt](https://docs.qolaba.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.qolaba.ai/chatbot/model-selection/model-information-panel.md).

# 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