Model Information Panel

Understanding Model Information Panel in Qolaba — covering context length, capability indicators, and credit usage transparency.

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