AI Model Size Comparison: GPT-4, Claude, Gemini, Llama… What's the Difference and Which Should You Use?

AI Model Size Comparison: GPT-4, Claude, Gemini, Llama — A Practical Selection Guide

Dozens of AI models are flooding the market, yet few people know exactly **"which model to use for my work."** This guide walks B2B sales reps and office workers through 5 steps to experience the differences between models firsthand and choose the best one for each situation.

Step 1. Get the Major AI Models at a Glance

First, understand the key specs of the models you are comparing. Save the table below and refer to it often.

1-1. Key Comparison Table by Model

1-2. What "Size" Actually Means

A larger parameter count does not automatically mean a better model.

• Parameter count: the number of "brain cells" a model learned with. GPT-4 is estimated at about 1.8T; Llama 3.1 has 405B

• Context window: how much text the model can read at once. Feeding in a full sales proposal (about 15,000 characters) takes at least 32K tokens

• Inference speed: larger models can be slower, but optimization (such as quantization) can offset this

💡 Pro tip: Choose a model based on **“the context length your task needs” and “output quality”**, not model size. A 1M-token model is overkill for writing a three-line email.

Step 2. Access Each Model Directly (Creating an Account to Your First Chat)

2-1. Accessing GPT-4o

1. Enter chat.openai.com in your browser

2. Click “Sign up” at the top right → sign up with your email or Google account

3. The free plan includes GPT-4o mini. For the full GPT-4o experience, go to your profile at the bottom left → “Upgrade to Plus” → pay ($20/month)

4. Select “GPT-4o” from the model dropdown at the top of the chat screen

5. Type your prompt in the input box at the bottom, then press Enter or click the send icon

2-2. Accessing Claude

1. Go to claude.ai → click “Sign up”

2. Verify your email, then log in

3. The free plan offers limited use of Claude 3.5 Sonnet. For the Pro plan, click “Upgrade” in the left menu → pay $20/month

4. Type your prompt in the input box at the bottom of the chat screen

5. Attach files: click the 📎 icon on the left of the input box → upload PDF, TXT, CSV, and more (up to 5 files)

2-3. Accessing Gemini

1. Go to gemini.google.com → log in with your Google account

2. The free version runs Gemini 1.5 Flash. For Gemini Advanced (1.5 Pro), click “Try Gemini Advanced” at the top right → subscribe to Google One AI Premium ($19.99/month)

3. Type your prompt in the input box. To upload images or files, click the + icon on the left of the input box

2-4. Accessing Llama (Beginner-Friendly Route)

Instead of installing it yourself, use a free hosting service.

1. Go to huggingface.co/chat → create an account

2. Open the model dropdown at the top left → select “Meta Llama 3.1 405B Instruct”

3. Type your prompt in the input box

💡 Pro tip: Keep all four models open in browser tabs and copy-paste the same prompt into each to compare results instantly.

Step 3. Compare All Four Models with the Same Prompt

This step is the heart of the guide. Enter the same three work scenarios into each model and compare the results.

3-1. Scenario A: Writing a B2B Cold Email

Sample prompt 1

“I’m a B2B sales rep selling cloud security solutions. Write a first cold email to the IT manager at a mid-sized Korean manufacturer. Requirements: ① include a subject line ② keep it under 200 characters ③ mention the recipient’s current concern (rising ransomware attacks) ④ close with a meeting request”

Sample comparison results (Before/After)

💡 Pro tip: For Korean B2B emails, Claude 3.5 Sonnet and GPT-4o are the most reliable. Claude in particular is good at striking a tone that isn’t too salesy.

3-2. Scenario B: Summarizing a 30-Page Proposal

Sample prompt 2

“The attached PDF is a 30-page IT infrastructure proposal. Please turn it into: ① a one-page executive summary (3 key points) ② a table of cost items ③ the 3 main expected risks.”

Key comparison points

• Claude 3.5 Sonnet: With a 200K context window, it can take a 30-page PDF in a single upload and analyze it. Its summaries are well structured

• Gemini 1.5 Pro: With 1M tokens, it can handle 100+ pages. Even more convenient with Google Drive integration

• GPT-4o: Its 128K context handles 30 pages with room to spare. Table output is clean

• Llama 3.1: File uploads are limited in HuggingFace Chat. Not suited to analyzing long documents

3-3. Scenario C: Competitor Analysis & Market Research

Prompt example 3

“Please compare UiPath, Automation Anywhere, and Samsung SDS Brity RPA in the Korean RPA (robotic process automation) market. Requirements: ① strengths/weaknesses of each solution ② pricing structure ③ Korean references ④ the recommended solution for our company (a manufacturer with 500 employees) and your reasoning”

Comparison results

• GPT-4o: Training data through April 2024, plus web browsing for up-to-date information. High accuracy on pricing

• Gemini 1.5 Pro: Google Search integration can pull in real-time prices and news. However, Korean-language reference information is somewhat thin

• Claude 3.5 Sonnet: Excellent analytical structure and logical flow. However, real-time web search is limited, so pricing needs to be verified yourself

• Llama 3.1: Can only respond from its training data. Weak on current market information

💡 Practical tip: Draft with GPT-4o (web browsing ON) or Gemini, then give that draft to Claude to refine the logical structure. This two-step workflow is the most effective.

Step 4. Best-Model Selection Matrix by Situation

Print the matrix below or save it in Notion, and refer to it whenever a work situation comes up.

4-1. Recommended Model by Task Type

4-2. Recommended combinations by budget

💡 Practical tip: The best value combination is two subscriptions, Claude Pro + GPT-4o Plus ($40/month). A user survey (2024, Superhuman) found that office workers can save an average of 15–20 hours of work per month.

Step 5. Real-world workflow — the “model relay” technique

Rather than relying on a single model, chaining several models like a relay raises output quality significantly.

5-1. Sales proposal workflow

Before (single model)

• Draft the proposal with GPT-4o alone

• Result: solid structure, but the tone is stiff and the client-specific wording is thin

• Time required: 2 hours (including revisions)

After (model relay)

Step 1 — Research (Gemini 1.5 Pro)

Prompt

“Summarize the size and growth rate of the 2024 Korean smart factory market and its three main trends, based on the latest sources. Include the sources too.”

• Gemini’s Google Search integration provides up-to-date statistics

• Time required: 3 minutes

Step 2 — Drafting (GPT-4o)

Prompt

“Using the market research data below, write a proposal for a smart factory MES solution. Audience: an auto parts manufacturer with KRW 300 billion (≈ US$224,450,100) in annual revenue. Structure: ① Market background ② Client challenges ③ Solution overview ④ Expected impact (with figures) ⑤ Investment cost ⑥ Timeline. [Paste the Step 1 Gemini output below]”

• Leverages GPT-4o’s strength in generating structured documents

• Time required: 5 minutes

Step 3 — Polishing tone & persuasiveness (Claude 3.5 Sonnet)

Prompt

“Review the proposal draft below. ① Revise the parts that lack empathy for the client’s perspective. ② Convert overly technical wording into business language executives can understand. ③ Replace the first sentence of each section with a hooking sentence. [Paste the Step 2 GPT-4o output below]”

• Claude’s nuance control raises final quality

• Time required: 5 minutes

Results compared

5-2. A checklist you can apply to your daily work

Skim this checklist each morning and pick the model that fits today’s tasks.

• 5+ emails to write today? → Open a Claude tab

• Preparing for a client meeting? → Give GPT-4o the client’s details and have it draft likely questions

• Have a long document to read? → Upload it to Claude (up to 30 pages) or Gemini (over 30 pages)

• Need market data? → Gemini or GPT-4o with web browsing

• Sensitive internal data? → Llama (local), or check each service’s API option that excludes your data from training

Common mistakes & how to fix them

❌ Mistake 1: “The most expensive model must be the best”

• Reality: GPT-4o isn’t number one at every task. On coding benchmarks (SWE-bench), Claude 3.5 Sonnet often beats GPT-4o.

• Fix: Use the matrix above and choose a different model for each type of task.

❌ Mistake 2: “Putting all the context into one model”

• Reality: When a single conversation runs to 100 messages, the model starts to “forget” the early context.

• Fix: Once a conversation passes 20 turns, start a new one and paste a summary of only the key context into the first message.

❌ Mistake 3: “Copy-pasting the output as is”

• Reality: AI output can contain **factual errors (hallucinations)**, especially in figures, statistics, and legal information.

• Fix: Always cross-check key figures against the original source. Gemini’s “Double-check response” button (the G icon at the bottom of a response) makes this easy.

Key takeaways — a 30-second summary

💡 Final practical tip: Right now, open all four model tabs at once, then copy and paste the three sample prompts above. The 15 minutes you spend comparing them yourself is worth 10 times more than the time you spent reading this article. The "best model for you" is something you can only find by using it on your own work.