OpenAI offers a variety of AI models, such as o1, GPT-4o, GPT-4o Mini, and GPT-4 Turbo. Each model differs in performance, speed, and cost, and you can choose the right model based on your project’s requirements.
Make.com is a powerful platform that makes it easy to implement automation using the OpenAI API. However, one thing to keep in mind when using the API is that different models have different token costs. In particular, the Chat GPT module on Make.com does notallow you to set input tokens, butyou can set output max tokens, so optimizing this can be an effective way to reduce costs.
Taking the GPT-4o and GPT-4o Mini models as an example, the input cost differs by a factor of 33 and the output cost differs by a factor of 25, and token consumption can vary depending on the structure of the Korean and English prompts. by understanding these differences, you can choose the best model for your project’s language and task characteristics.
in this article, we’ll compare the performance, suitability, and token cost of GPT o1, GPT-4o, GPT-4o Mini, and GPT-4 Turbo, and show you how to use them efficiently. Whether you are planning to use the OpenAI API for a long timeor are just getting started with automation, this information will help you understand the differences between the models and choose the best one for your project .


talknizer OPEN AI Token Calculator: “https://platform.openai.com/tokenizer”
GPT 01, GPT-4o, GPT-4o mini, GPT-4 turbo model feature comparison
model | performance | token cost (input/output, per million tokens) | speed | intended Use |
---|---|---|---|---|
o1 | models with advanced inference capabilities, optimized for solving complex problems. | 15.00 / $60.00 | slow | tasks that require a high degree of accuracy and reasoning skills, such as science, coding, and math. |
GPT-4o | versatile, high-performance model that can handle text, images, and audio. | 5.00 / $15.00 | moderate | general purpose model for a wide range of tasks. |
GPT-4o mini | lightweight model that is fast, cost-effective and includes vision capabilities. | 0.15 / $0.60 | fast | projects where simple tasks, real-time processing, and cost-effectiveness are important. |
GPT-4 turbo | 2x faster than GPT-4o and half the cost. | 10.00 / $30.00 | very fast | generate large responses, repeat tasks, use APIs, and more. |
1️⃣ GPT o1
- features: Provides the most precise inference capabilities, performs best on highly complex tasks
- good for: Writing scientific papers, designing complex algorithms, drafting legal documents.
- summary: Best for projects that require precision and accuracy.
GPT o1 is a model with GPT advanced reasoning capabilities, optimized for solving complex problems. as of January 25, 2025, it is the highest performing GPT model available today and is ideal for tasks that require a high degree of accuracy.
it is often used to handle challenging tasks such as analyzing complex data, writing research papers, and designing coding algorithms. it is useful for working on scientific papers, solving complex algorithmic problems, and drafting legal documents. API calls are relatively slow and can be expensive, so they’re used in projects where accuracy and quality are a priority.
The GPT o1 API costs $15.00 (input)/$60.00 (output) per million tokens and is slow. .
. 2️⃣ GPT-4o
- features: A versatile, high-performance model that provides balanced performance for a variety of tasks.
- good for: blog writing, translation, marketing content creation, medium-difficulty analytics tasks.
- summary: A general-purpose model that can be utilized for many tasks.
The GPT-4o model is a versatile, high-performance model that can handle text, images, and audio. it is suitable for a wide range of tasks and can be used for medium-difficulty tasks with good performance.
GPT-4o is typically used for a variety of tasks, including text generation, image description, translation, and marketing content creation such as blogs, YouTube transcripts, email drafting, and ad copy generation. The call rate when using the API is currently moderate and is used when you need to balance high-quality work with economic cost.
The GPT-4o API costs $5.00 (input)/$15.00 (output) per million tokens, with moderate speeds.
3️⃣ GPT-4o mini
- features: Lightweight model optimized for simple tasks, fast and low cost.
- ideal for: Real-time FAQ bots, social media captioning, and simple response processing.
- summary: An affordable choice for simple tasks and real-time processing.
The GPT 4o-mini model is a lightweight version of the 4o, optimized for simple tasks, offering fast response times and cost-effectiveness.
ideal for tasks that require fast response and low cost. for quick turnaround on simple tasks such as live chat responses, simple FAQ bots, and social media captioning. It is often used for large-scale projects or real-time services because it is very fast and cost-effective to make API calls.
GPT-4o mini costs $0.15 (input)/$0.60 (output) per 1 million tokens and is very fast.
4️⃣ GPT- turbo
- features: Optimized for high-volume tasks with fast processing speed and good performance.
- best for: Large data processing, repetitive tasks, real-time response systems.
- summary: Provides a balance of performance and cost-effectiveness for speed-critical jobs.
The GPT- turbo modelis twice as fast as the GPT-4o and half the cost, making it efficient for high-speed, high-volume jobs where real-time processing is critical.
ideal for jobs that require large amounts of data processing, real-time services, and fast response times. Used for repetitive tasks or large-scale user response systems (API-based) because it is the fastest and most cost-effective way to make API calls. for example, e-commerce customer support systems or large-scale data synchronization tasks,
GPT- turbo costs only $10.00 (input) / $30.00 (output) per million tokens, which is on the fast side,
Comparison ofGPT o1, GPT-4o, GPT-4o Mini, GPT-4 Turbo based on performance
1️⃣ Precision and Inference
- o1 > GPT-4o > GPT-4-Turbo > GPT-4o Mini
- description:
the o1 offers the most precise inference power and excels at complex tasks. GPT-4o is a versatile, high-performance model that performs adequately on a wide range of tasks. The GPT-4-Turbo delivers compliant performance with fast processing speeds, while the GPT-4o Mini is optimized for simple tasks.
2️⃣ Cost-effectiveness
- GPT-4o Mini > GPT-4o > GPT-4-Turbo > o1
- description:
The GPT-4o Mini is ideal for handling simple jobs at the lowest cost. The GPT-4o offers a medium balance of cost and performance. The GPT-4-Turbo has a slightly higher cost in proportion to its faster processing speed, and the o1 requires the highest cost with the highest performance.
3️⃣ Speed
- GPT-4o Mini ≥ GPT-4-Turbo > GPT-4o > o1
- explanation:
GPT-4o Mini and GPT-4-Turbo are fast and suitable for real-time processing jobs. The GPT-4o offers standard speeds, while the o1 has relatively slow speeds with high precision.
model selection guide
choose the GPT-4o: 1️⃣ for complex problem solving and tasks requiring high accuracy
- choose the O1
- examples of use: writing scientific papers, advanced data analysis, drafting legal documents.
- why: When precision and reasoning skills are paramount.
2️⃣ A general-purpose model that can perform a variety of tasks
- GPT-4o is a good fit
- example uses: creating blog content, describing images, translating, drafting emails.
- why: Versatile and performs well on a variety of tasks.
3️⃣ Simple tasks where cost-effectiveness and fast response are important
- Consider the GPT-4o Mini
- example uses: Running FAQ bots, generating social media captions, live chat.
- why: Effectively handle simple tasks at low cost and fast turnaround.
4️⃣ For larger jobs and real-time processing
- Choose GPT-4o Turbo.
- use cases: bulk data processing, real-time API responses, recurring jobs.
- why: Fast and affordable, perfect for large-scale jobs.
Chat gpt (open ai )api token
In OpenAI’s token system, Korean and English arehandled differently. the number of tokens depends on the structure of the language and the length of the words, so even sentences of the same length use different numbers of tokens depending on the language. below, we’ll explain the differences in the token system between Korean and English. First, we’ll start with the concept of tokens, which is how OPEN AI’s API reads letters, and then we’ll discuss token costs and use cases, as well as tips for reducing costs when using the API.
1️⃣ What is a token?
- In OpenAI’s model, a tokenis the smallest unit of processing text data.
- one token consists of about one word or a few letters
- example: “ChatGPT is great!” → 6 tokens.
- example: “Hi, this is GPT.” → about 9 to 11 tokens.
2️⃣ Difference between tokens in Korean and English
1) English (English)
- english has spaces between words and a simple grammatical structure, so the number of tokens used in a sentence is relatively small.
- example
- sentence: “Hello, how are you doing?”
- tokens: 7
- “Hello”, “,”, “how”, “are”, “you”, “doing”, “?”
2) Korean (Korean)
- korean tends to be a language with a lot of investigations (e.g., “은”, “은은”, “을”) and endings changes (e.g., “합니다”, “해요”), which makes words longer.
- when processing Korean, the model also chops up words and tokenizes them, consuming more tokens than an English translation of the same sentence.
- example
- sentence: “Hello, it’s a beautiful day.”
- tokens: 13-15
- “Hi”, “do”, “,”, “today”, “weather”, “go”, “true”, “good”, “yes”, “yo”, “.”
3️⃣ Comparing the number of tokens in Korean and English
- in Korean, it is not uncommon for a sentence with the same content to consume 1.5 to 2 times as many tokens as in English, due to investigation, end inflection, word compounding, etc.
- example comparison
- english: “I love learning AI.” → 5 tokens.
- korean: “I like learning AI.” → about 12-14 tokens.
4️⃣ Differences in calculating token costs
- The OpenAI API is priced based on the number of tokens, so a Korean task may cost more than an English task.
- for example, for GPT-4-Turbo
- input cost: $0.0015/1K tokens
- output cost: $0.002/1K tokens
- generating long sentences in Korean is likely to be more expensivethan in English.
5️⃣ Real-world use cases
- english: Efficient for generating short sentences, writing technical documentation, and optimizing API responses
- example: “Write a summary of this report.” → about 6 tokens.
- korean: Used for customer service responses, translations, and user interface text generation
- example: “Please write a summary of this report.” → about 12-15 tokens.
6️⃣ Optimization tips for using the Korean and English APIs
- make concise requests: In Korean, longer requests consume more tokens, so write concise and clear requests
- example: “Write a summary of the report” (O) → “Please write a detailed and thorough summary based on this report.” (X)
- limit output length: Explicitly limit the number of output tokens in your request
- example: “Please summarize in 50 characters or less.”
- when working with translations: save tokens by making a request in English and receiving only the translated result in Korean.
conclusion
OpenAI offers a wide range of choices for users with a variety of AI models. the o1, GPT-4o, GPT-4o Mini, and GPT-4 Turbocan be utilized for specific projects based on their respective features and strengths.
the o1is best suited for jobs that require the most precise inference capabilities and high levels of complexity, and is ideal if you can tolerate its high cost and relatively slow speed. the GPT-4o, on the other hand, is a general-purpose model that offers balanced performance on a wide range of tasks, and the GPT-4o Miniis a low-cost model optimized for simple tasks and real-time response. The GPT-4 Turbooffers fast processing speeds and excellent efficiency for large-scale jobs.
model selection and maximum output token settingsare key to cost savings, especially when automating with Make.com and the OpenAI API. for example, GPT-4o and GPT-4o Mini have a 25x difference in output cost, so it is important to choose the right model based on the nature of the project and the difficulty of the task. for example, for SNS publication, we use GPT-4o Mini to reduce the number of text, and for blog publication, we use GPT-4o’s model to deliver accurate and in-depth information.
in addition, the difference in token processingbetween Korean and English is an important factor in the cost-effectiveness of our work. korean tends to consume about 1.5 to 2 times as many tokens as English, so we can optimize costs by making concise requests and limiting output length.