What Are AI Model 'Parameters' and 'Tokens'? The Numbers Behind AI Performance

What Are AI Model ‘Parameters’ and ‘Tokens’? Understanding AI Performance Through Numbers

What you will learn from this guide

• What parameters are, and why bigger numbers cost more

• What tokens are, and why pasting a long document at once causes errors

• The real reason GPT-3.5, GPT-4 and GPT-4o are priced differently

• Practical prompt-writing tips to save tokens while getting better results

Step 1. Understanding “Parameters” in Everyday Work Terms

What Is a Parameter?

Think of an AI model as a human brain. The brain has about 100 billion nerve cells (neurons), and knowledge is stored in the connections between them. An AI model’s **parameters** correspond to exactly those connections.

• 1 parameter = the smallest unit of knowledge the AI has learned (a single number)

• More parameters = it can remember more complex patterns and generate more refined answers

• Fewer parameters = lighter and faster, but weaker on complex questions

Parameter Counts of Major Models

💡 Practical tip: GPT-3.5 (175 billion parameters) is enough for simple jobs like drafting a sales email. Reserve GPT-4 for work that needs precision, such as analyzing complex contracts or multilingual translation, and you can cut costs by up to 20x.

Try It Yourself (Hands-On)

Step 1: Go to chat.openai.com in your browser.

Step 2: Click the model selector dropdown at the top of the screen. You will see options such as “GPT-4o,” “GPT-4o mini,” and “GPT-3.5.”

Step 3: Select GPT-4o mini first and enter the prompt below.

Please translate the following sentence into English, Japanese, and Vietnamese, and also provide a version of each translation revised in a business email tone: "I would like to schedule a product demo meeting next Tuesday at 3 PM. I would appreciate it if you could let me know whether you are available to attend."

Step 4: Enter the same prompt again with GPT-4o.

Before (GPT-4o mini): The translation works, but the business nuance can feel somewhat literal, and the level of Japanese honorifics is sometimes inconsistent.

After (GPT-4o): It uses expressions that fit each language’s business conventions, and the Japanese version includes natural business honorifics such as “お忙しいところ恐れ入りますが”.

This difference comes from the difference in parameter count. More parameters = more language patterns learned = more natural output.

Step 2. Understanding “Tokens” in Everyday Work Terms

What Is a Token?

A token is the smallest unit in which an AI processes text. People read by letters or words, but AI reads by tokens.

In English:

• "Hello" → 1 token

• "I don't know" → 4 tokens

• 1 English word ≈ 1.3 tokens on average

In Korean:

• “Hello” → 2–3 tokens

• “Product demo meeting” → 4–5 tokens

• 1 Korean character ≈ 2–3 tokens on average (Korean uses more tokens than English!)

💡 Pro tip: The same content uses about 1.5–2x more tokens in Korean than in English. If you work with the API, you can cut token costs by writing your prompt instructions in English and adding "Please answer in Korean."

Count the Tokens Yourself (Hands-On)

Step 1: In your browser, go to platform.openai.com/tokenizer.

Step 2: Paste the sentence below into the text box on the left.

Hello, I'm Minsu Kim, a sales representative at ABC Solutions. I'm reaching out to make a proposal that could help improve the efficiency of your logistics system.

Step 3: Check the Token count shown on the right. The sentence above measures about 55–65 tokens.

Step 4: Now enter an English sentence with the same meaning.

Hello, I'm Minsu Kim, a sales representative at ABC Solutions. I'm reaching out to share a proposal that could help improve your logistics system efficiency.

Comparison of results:

You can see for yourself that Korean has fewer characters but uses nearly 2x as many tokens.

Step 3. How Token Limits (Context Window) Affect Real-World Work

What Is a Context Window?

The maximum number of tokens an AI model can process in a single conversation is called the **context window**. It includes both your question and the AI's answer.

Token Limit Problems You'll Run Into at Work

Scenario 1: What happens if you paste a 30-page RFP (request for proposal) into GPT-4 (8K)?

→ Only the first part of the document is read and the rest is cut off. Even if you ask it to "summarize," the summary won't reflect the whole document.

Scenario 2: What if you want to analyze a sales meeting transcript (2 hours long, about 15,000 words)?

→ Roughly 40,000–50,000 tokens in Korean. You would need GPT-4 Turbo or Claude 3.5.

💡 Pro tip: When processing a long document, don’t feed it in all at once. Split it into sections, summarize each, then combine the summaries at the end in a chain approach. This also works well with models that have smaller token limits.

Hands-on: Feel the token limit

Step 1: In ChatGPT, select GPT-4o.

Step 2: Enter the prompt below.

Tell me the size of your current context window and roughly how many tokens have been used in this conversation so far. Then estimate how many more tokens I can use.

Step 3: The AI’s response gives you an intuitive sense of the current model’s limits.

Step 4. Understanding in numbers why GPT-4 is expensive

API price comparison (as of 2024, per 1 million tokens)

Cost calculation for a real work scenario

Scenario: A B2B sales rep uses AI to write 20 cold emails a day.

• Prompt (input) per email: about 300 tokens

• Result (output) per email: about 500 tokens

• At 20 emails a day: 6,000 input tokens + 10,000 output tokens

💡 Pro tip: Repetitive tasks like drafting emails work well enough with GPT-4o mini. Reserve GPT-4o for complex analysis (competitor comparisons, contract reviews, strategy planning) to get the most value for your money. This strategy alone can cut your annual API costs by over 90%.

Step 5. How to write prompts that save tokens and get better results

Before/After comparison: token-wasting prompt vs. efficient prompt

❌ Before (inefficient prompt — about 150 tokens):

Hello ChatGPT! I work in sales at a B2B SaaS company, and I have a cold email I need to send today. The recipient is a logistics company, and our product is an inventory management solution. Could you write a really good email for me? Not too long, but not too short either, in a business tone, please. Oh, and please include a CTA!

✅ After (efficient prompt — about 80 tokens):

Role: B2B SaaS sales email copywriter

Goal: Write a cold email

Target: Operations manager at a logistics company

Product: Inventory management solution (real-time stock tracking, automated ordering)

Requirements: Under 150 words, business tone, include a CTA

CTA: Propose a 15-minute demo call

Comparison of results:

💡 Practical tip: Structuring your prompt (separating role, goal, and conditions) can cut token use by 50% while actually improving output quality. Spelling out your conditions gives the AI far clearer instructions than a vague request like “write it well.”

Hands-on: Token-saving prompt template

Copy the template below and adapt it to your own situation.

Step 1: Open ChatGPT and enter the structured prompt below.

Role: [the expert role you want]

Task: [the specific task]

Input: [the necessary background, kept brief]

Output format: [table, bullet points, email format, etc.]

Constraints: [length, tone, what to include or exclude]

Step 2: Review the result and fill in only what’s missing with a short follow-up prompt.

Make the first sentence of the email above more impactful, and add a limited-time promotion line in the PS.

This saves 70% or more of the tokens compared with rewriting everything from scratch.

Step 6. Model selection decision guide

Recommended model by task type

Hands-on: Find the model that fits you

Step 1: List five tasks you asked an AI to do over the past week.

Step 2: Compare each task against the table above and match it to a suitable model.

Step 3: Use the prompt below in ChatGPT to analyze your own usage patterns.

I'm a B2B salesperson, and I mainly use AI for the following:

1. Writing cold emails (10 per day)

2. Writing follow-up emails after meetings (5 per day)

3. Drafting the weekly sales report (once a week)

4. Client industry analysis (twice a week)

5. Drafting proposals (3 per month)

Recommend the most cost-effective OpenAI model for each task, and calculate my estimated monthly token usage and cost.

💡 Pro tip: Don’t stick with a single model. Just by getting into the habit of switching models based on task difficulty, you can hand AI 2–3 times more work on the same budget.

Step 7. Key Concepts — 1-Minute Review Card

Wrap-Up Checklist

If you can check off every item below, you’ve fully grasped the core of this guide.

• I checked the difference in token counts between Korean and English in the OpenAI tokenizer myself

• I entered the same prompt into GPT-4o mini and GPT-4o and compared the results

• I wrote an email using a structured prompt template (role / task / conditions)

• I matched the right model to each of my 5 main tasks

• I calculated token costs and estimated my monthly AI spending

💡 Final tip: Remember today’s lesson in one sentence — “Parameters are the size of AI’s brain; tokens are AI’s billing unit. Pick the right model for the task and structure your prompts, and costs go down while quality goes up.”