![[2025 Edition] A thorough comparison of AI APIs for developers | Summary of features and prices for major LLMs such as ChatGPT, Claude, Gemini, and Llama.](https://hack-lab-256.com/wp-content/uploads/2025/07/hack-lab-256-samnail-22.jpg)
From 2024 to 2025, the development of AI-powered apps and services has become increasingly popular.
Large-scale language models (LLM) such as ChatGPT and Claude go beyond just chatbots and are used in a variety of use cases, including code generation, summarization, search, and document processing
And now, many developers are concerned about
👉 " Which AI model should I use? "
👉 " How do the cost, accuracy, and ease of use of APIs differ? "
In this article, we will thoroughly compare the features, prices and suitable uses of each of the major AI models (LLMs) that developers can use via APIs
What is LLM?
The term "LLM" is a common word recently. This Large Language Model refers to AI that can understand and output human language, such as text generation, summarizing, translation, and question answering
Simply put, the very brains are LLMs.
To put it in a little more detail...
LLM is trained to learn a vast amount of text (articles, books, codes, etc. on the Internet) and understand the "relationship between words" and "natural sentence flow."
For example, you could do this:
- Answer questions in natural writing (Q&A)
- Automatically generate blog posts and code
- Text summary, structure, classification
- Read long sentences and understand the context
- Natural translation of English ⇔ Japanese
- And so on
Why is the "LLM API" important?
Most of today's LLMs are now available as APIs provided in the cloud.
In other words, you can incorporate AI functions by sending requests from your app or service to "brains" such as ChatGPT and Claude and receiving responses.
There are a large number of AI services created in this way, and many of the current generation AIs are actually structured to "carry" LLM APIs behind the scenes.
This allows engineers to use powerful AI easily and at a low cost without having to create AI from scratch.
Why is choosing an AI API important for developers?
When developing products that utilize AI, not only ideas and UI, but also the choice of which AI (LLM) to use will directly lead to the performance, cost, and future prospects of the service .
In particular, there are multiple LLMs that can be used as APIs, each with different
areas of expertise, accuracy, fees, and restrictions In other words, "Which to choose" will create the following differences:
There are differences between features and limitations
- ChatGPT is strong in file processing and code interpretation
- Claude offers a consistent way to process long sentences and think
- Gemini makes it easy to connect with images and audio
- Llama and Mistral are lightweight and suitable for custom models
→ Depending on the content of the service you create, there are many different options and inappropriate options.
API usage costs vary (differences in unit price)
- GPT-4 Turbo: One time can cost around 10 yen
- Claude Sonnet: Same processing is less than half price
- Llama series: It can be used for free and self-operated
→ Depending on how you use the API and the size, the difference between tens of thousands and hundreds of thousands of yen per month can vary.
Even if there are commercial use or usage restrictions
- Some models have restrictions on commercial use
- Open sources (such as Llama/Mistral) have a high degree of freedom, but also heavy responsibility.
→ Important from the perspective of risk management in product development
There are also differences in documentation and support systems
- OpenAI has a rich ecosystem (libraries and many SDKs)
- Documentation has been developed for Claude and Gemini, but there is little Japanese information.
→ It also affects development speed and team proficiency.
Summary: Not only is the use of AI, but the ability to choose is important
Nowadays, it is not difficult to develop using AI in itself.
The important thing is to "select the LLM API that suits your needs and use it effectively."
Because AI performance is evolving with the ever-advanced progress, annual model comparisons and reviews of selections are a weapon for developers.
In this article, we will compare representative LLM APIs from the next chapter, and organize them in an easy-to-understand manner which developers should choose which models
List of AI models to be compared
Here we introduce the major large-scale language models (LLMs) that developers can use through their APIs as of 2025.
All of these AI models that can be used for commercial purposes , and are all used behind the scenes of many AI services.
ChatGPT (OpenAI)
OpenAI's ChatGPT is something that can be said to be the spark of the LLM boom.
The API mainly uses gpt-3.5-turbo and gpt-4-turbo attractive features are
high accuracy, functionality and comprehensive development tools It's understandable that it has more features than other models, such as file processing, Code Interpreter, and Function Calling, and is highly supported by developers.
| Model name | Input price (1K token) | Output price (1K token) | Maximum context length | Japanese language support | Main features | Ease of use |
|---|---|---|---|---|---|---|
| GPT‑4.1 | $0.0020 | $0.0080 | 128K (estimated) | ◎ | Code generation, contextual understanding, and summary | ◎ Includes information and SDK |
| GPT‑4.1 mini | $0.0004 | $0.0016 | 128K (estimated) | ◎ | Lightweight and cost-effective | ◎ |
| GPT‑4.1 nano | $0.0001 | $0.0004 | 64K or more? (In the early hours) | ○〜◎ | For small-scale bots and lightweight processing | ◎ |
| o3 | $0.0020 | $0.0080 | 128K-256K? | ◎ | Complex reasoning and visual understanding | △ Fewer documents |
| o4‑mini | $0.0011 | $0.0044 | 128K (assumed) | ◎ | Lightweight reasoning based on multimodal premise | △ |
⇒Click here for the latest prices
Claude (Anthropic)
Claude is an LLM made by Anthropic, which appeared in a way that competes with OpenAI. Currently, the Claude 3 series (Opus/Sonnet/Haiku) is available.
its strength in logic, consistency, and long-form processing , and its Japanese language compatibility is also extremely accurate. Sonnet has a particularly and is particularly noteworthy because it is cheaper to use than the GPT-4 Turbo
| Model name | Input price (1K token) | Output price (1K token) | Maximum context length | Japanese language support | Main features and features | Ease of use |
|---|---|---|---|---|---|---|
| Claude Opus 4 | $0.015 | $0.075 | Approximately 200K | ◎ Very natural | Top model. Perfect for complex reasoning, advanced long-form reading comprehension and specialized tasks. | ○ Documentation included, English-focused |
| Claude Sonnet 4 | $0.003 | $0.015 | Approximately 200K | ◎ Very natural | A good balance of high accuracy, cost and speed. It is also ideal for code and design support. | ○Comprehensive documentation (English) |
| Claude Haiku 3.5 | $0.0008 | $0.004 | Approximately 200K | ◎ Practical quality | The fastest and lightest model. Suitable for chat bots, real-time use, low-cost operation | ◎ Lightweight and easy to install |
⇒
Gemini (Google)
Gemini is a multimodal-enabled next-generation LLM provided by Google.
The main API is Gemini 1.5 Pro, and can support not only text but also images, audio and video . Because it is integrated with Google Cloud, it is a great ally for developments linked to GCP users and Google Workspace.
| Model name | Input price (1K token) | Output price (1K token) | Maximum context length | Japanese language support | Main features and features | Ease of use |
|---|---|---|---|---|---|---|
| Gemini 2.5 Pro | $0.00125 (≤200,000 tok) $0.0025 (>200,000 tok) | $0.01 (≤200,000 tok) $0.015 (>200,000 tok) | Up to approximately 1M to 2M tokens | ◎ | Suitable for high-precision inference and coding. Multimodal support, GCP integration | △ (GCP registration/English document) |
| Gemini 2.5 Flash | $0.0003 (text etc.) | $0.0025 | Approximately 1M token | ◎ | Lightweight and high-speed model. Images and audio input are also possible. | △ (Slightly complicated setting) |
| Gemini 2.5 Flash-Lite | $0.0001 (text etc.) | $0.0004 | Approximately 1M token | ◎ | The lowest priced general purpose model. Perfect for chat bots and lightweight services | ○ (If you're focusing on price, ◎) |
⇒Click here for the latest prices!
Llama 3 (Meta)
An open source LLM developed and published by Meta (formerly Facebook). Currently, the main focus is the Llama 3 (8B/70B/405B) can also be used for commercial purposes .
The model itself is free to use and can also be used in the cloud through multiple API providers such as Hugging Face, Groq, Replicate, Together.AI, and Fireworks.ai
Even with the same model, differences in fees, latency, and performance (e.g., Turbo or Lite versions) , so it is important to select the product according to the purpose.
Additionally, for developers who are interested in in-house operation, weight reduction, and local inference, the high degree of freedom of the model and flexibility in adoption are extremely attractive. Another advantage that other commercial LLMs don't have is that you can choose between cloud and local.
| Model name | Input price (1K token) | Output price (1K token) | Maximum context length | Japanese language support | Main features and features | Ease of use |
|---|---|---|---|---|---|---|
| Llama 3.1 8B (AWS) | $0.00022 | $0.00022 | ? | ○ | Basic inference and lightweight tasks | ◎ |
| Llama 3.1 8B (Together.AI) | $0.00018 | $0.00018 | ? | ○ | Developer hosting and UI development | ◎ |
| Llama 3.1 70B (AWS) | $0.00099 | $0.00099 | ? | ◎ | High-precision inference, large-scale tasks | ◎ |
| Llama 3.1 70B (Together.AI) | $0.00088 | $0.00088 | ? | ◎ | Good value for money and easy to implement | ◎ |
| Llama 3.1 405B (Fireworks.ai) | $0.00300 | ? | ? | ◎ | Large-scale LLM with a focus on cost performance | ◎ |
Other Featured Models
Mistral (Mistral.ai)
High-performance models that make use of the Mixture of Experts architecture , such as the Mixtral 8x7B, It is possible to infer low cost and fast, and is popular for edge inference and research purposes.
Cohere Command R+
Cohere offers LLMs specialized for search and RAG (Retrieval-Augmented Generation) applications. It is suitable for large-scale document processing and building internal search bots, and is also well supported in Japanese.
Comparing the features, prices and features of each model thoroughly.
Here we will compare key models for the key LLMs that developers can use via APIs.
| Model name | Input price (1K token) | Output price (1K token) | Maximum context length | Japanese language support | Main features and features | Ease of use |
|---|---|---|---|---|---|---|
| GPT‑4.1 (OpenAI) | $0.0020 | $0.0080 | 128K | ◎ | Highly accurate chat, code generation, excellent stability | ◎ (Official and SDK-rich) |
| Claude Sonnet 4 (Anthropic) | $0.0030 | $0.0150 | Approximately 200K | ◎ | Long-form understanding, design intent explanation, natural and consistent output | ◎ (Comprehensive documentation) |
| Gemini 2.5 Pro (Google) | $0.00125〜2.50 | $0.005〜10.00 | Up to 2 million | ◎ | Google integration, multimodal support, search grounding support | ○ (Slightly complicated via GCP) |
| Llama 3.1 70B (Together.AI) | $0.00088 | $0.00088 | Approximately 128K (estimated) | ○〜◎ | OSS-based, highly flexible and inexpensive. It varies by provider | ◎ (Simple API, commercial use OK) |
Recommended models for different uses
There are many different situations where developers use LLM. Here we will introduce carefully selected "the best models for each purpose."
We choose based on API cost, accuracy, number of supported tokens, and whether commercial use is possible.
General chat and question answering → GPT-4.1 (OpenAI)
A classic model that can handle a wide range of general-purpose tasks, including natural dialogue, chatting, Q&A.
In addition to its high accuracy and stability, the OpenAI ecosystem (SDKs, documentation, tutorials) is also extremely comprehensive, making it a great place to choose from
Long text summary and document processing → Claude Sonnet 4 (Anthropic)
Claude is good at "contextual understanding" and "consistent summaries."
The Sonnet 4 has a good balance of cost and performance, making it ideal for processing minutes, contracts, reports, and more. A major advantage is that it can also be used to input long sentences, nearly 200K tokens.
Explanation of design intent and code review → Claude Sonnet 4
The appeal of Claude is that it not only explains the code's behavior, but also goes into the "why I wrote it this way."
a logical and easy to explain AI that can be used to support beginners' learning and also assist with PR reviews .
Ultra-long context processing → Gemini 2.5 Pro (Google)
It can accommodate up to 2 million tokens. You can load and process large amounts of PDFs, multiple code files, long-term project history, and more at once.
As this system is used via GCP, the implementation hurdles are a bit high, but ideal for work with a lot of information .
Coding use: AI Pair Pro → GPT-4.1 or Claude Sonnet 4
GPT-4.1 is extremely resistant to code generation and contributes to improving coding speed.
On the other hand, Claude Sonnet 4 is a model that is good at interpreting code and explaining intentions, interact with pair programming .
It's a good idea to choose which one to prioritize: generation or understanding.
High-speed bot development with a focus on cost performance → Llama 3.1 70B (Together.AI)
Although it is an OSS model, it can be used for high precision and commercial purposes, and is cheaper than major models.
Perfect for MVP development, simple internal bots, and automatic FAQ responses. It can be easily used via APIs from Together.AI, Fireworks, etc.
Local inference and in-house operation → Llama 3.1 (8B/70B)
Meta's Llama is an OSS and can be used on-premises or in a local environment.
Recommended for internal systems that emphasize security or for applications that want to reduce cloud costs. There are lightweight 8B and high-performance 70B, so you can choose according to your environment.
Summary: List of recommended models by purpose
Finally, we'll summarize it in a list, so
please use this as a reference to select the best model that suits your purpose, budget, and technical requirements.
If you're unsure, we recommend trying out GPT-4.1 or Claude Sonnet 4 first.
| Usage Category | Recommended models | Reasons and characteristics |
|---|---|---|
| General chat and question response | GPT‑4.1 (OpenAI) | Excellent accuracy, stability and language operation. If you're unsure, this is it. It has a wealth of APIs and SDKs, making it easy to install. |
| Long text summary and document processing | Claude Sonnet 4 (Anthropic) | It is characterized by strength to long sentences and consistent answers. Suitable for reading comprehension, summaries, minutes summary, etc. |
| Explanation of design intent and code review | Claude Sonnet 4 (Anthropic) | He is good at logical answers and is extremely resistant to explanations of intentions such as "Why did you do that?" |
| Ultra-long context processing | Gemini 2.5 Pro (Google) | Up to 2 million tokens. It is useful when you need large amounts of documents and inferences that span multiple files. |
| Coding use/AI pair pro | GPT‑4.1 (OpenAI) or Claude Sonnet 4 (Anthropic) | Claude is strong in supporting design intent, while GPT is good at code generation. Opus is recommended for high-precision applications. |
| High-speed Bot development with a focus on cost performance | Llama 3.1 70B (Together.AI) | It can be used for commercial purposes, is inexpensive, and is high-performance. Because it is OSS, it is highly flexible and is extremely easy to use for MVPs and PoCs. |
| Local reasoning and in-house operation | Llama 3.1 (8B/70B) (Meta OSS) | Free operation with OSS. If you have a GPU, you can use it on-pre or locally. |
Summary and how to choose a recommended one
So far, we have compared the features, prices, features, and functions of major large-scale language models (LLMs) by their use.
in an age where high-precision, multifunctional models can be easily used via APIs, mainly OpenAI, Anthropic, Google, and Meta .
It is smooth to decide which model to choose from based on the following points:
| Perspective | What to check |
|---|---|
| Uses | chat? summary? Code generation? Document processing? |
| Accuracy or cost | Accuracy is the top priority, GPT-4.1 / Claude Opus, and Sonnet / Llama |
| Supported token amount | If you want to handle long-form processing and multiple files, you can use Claude or Gemini's super long context. |
| Commercial use is not possible | Check the OSS, paid API, and terms of use (Llama, especially provider dependent) |
| Ease of development | SDK and document richness, API stability, and error handling |
Recommended for beginners and individual developers
- First you want to try → GPT-4.1 or Claude Sonnet 4
→ Highly accurate, easy to use, and has a lot of proven track record - Want to keep costs down → Llama 3.1 (Together.AI, etc.)
→ Good value for money based on OSS. Perfect for MVPs and internal bots
Once you try it, you can experience each of your "personalities" and strengths and weaknesses.
Using LLM is a powerful weapon even during the prototyping and PoC stages , so be sure to choose one that suits your needs.
