Which AI Token is cheaper? Before comparing, first understand what kind of usage you are using

Which AI Token is cheaper? There is no answer that is always the cheapest for everyone. What really needs to be compared is not just to see who has the lowest unit price among OpenAI, Claude or Gemini, but to first see which one your usage is, and then compare which one has the lowest total cost in your scenario. OpenAI officially positions GPT-5.4 nano as the cheapest GPT-5.4-level model, suitable for simple high-frequency tasks; Anthropic's official pricing page shows that Claude Haiku 4.5 is the lower-priced option in the Claude series; Google officially describes Gemini 2.5 Flash-Lite as the fastest and most budget-friendly model in the 2.5 family. These three companies actually have "cheap routes", but the premise of being cheap is based on different mission types.

The point of this article is not to directly tell you which one is the lowest, but to help you distinguish first: which one your usage belongs to, and then you will know how to compare the cheaper ones. People who do a lot of simple tasks, and people who write copy, generate long articles, and run automated processes every day, may have different answers in the end. This is why if you directly compare the pricing pages of three companies too early, you will often make a mistake. The official pricing pages of OpenAI, Anthropic, and Google do not list just one number, but split it into input, output, cache, batch or other function fees. This also means that the "cheap" thing has to be put back into actual usage.

Let’s start with the conclusion: There is no always the cheapest AI Token, only the most cost-effective choice for your current usage

If you are a user with high-frequency, simple, and large-volume tasks, you should usually look at the low-cost, high-speed models first, such as OpenAI’s GPT-5.4 nano, Anthropic’s Claude Haiku 4.5, and Google’s Gemini 2.5 Flash-Lite. OpenAI officially states that GPT-5.4 nano is the cheapest GPT-5.4 level model and is suitable for simple high-volume tasks; Anthropic’s pricing page shows that Haiku 4.5 input is US$1/MTok and output is US$5/MTok, which is significantly lower than the Sonnet series.

If you are a daily content worker, such as writing social copy, editing emails, organizing meeting content, and doing SEO outlines every day, then you are usually not looking for the "absolute lowest price", but looking for something that is cheap enough to be used for a long time, but not so cheap that you keep making mistakes. What should be compared at this time are balanced options, such as models such as GPT-5.4 mini and Gemini Flash, rather than just the cheapest small models. OpenAI’s API Pricing shows that GPT-5.4 mini input is $0.75/1M and output is $4.50/1M. It is clearly positioned as a stronger small model.

If you are doing high-quality long articles, complex analysis, reasoning or important tasks, then "which one is cheaper" cannot just look at the superficial price. Because the real cost of this kind of task is likely to come from re-runs, rework and manual corrections, rather than the API unit price itself. OpenAI officially positions GPT-5.4 as the strongest model for professional work, while Anthropic's Claude Opus 4.6 is in the higher price range on the official pricing page.

If you are a batch, systematic, and automated task user, the most important thing is usually not the single real-time price, but the overall structure of batch, cache, and limits. OpenAI’s official Batch API clearly states that input and output can save 50% each; Anthropic’s official pricing page also directly states that batch processing can save 50%. In this case, which one is cheaper often does not depend on the immediate single price, but whether you can use the right processing model.

Why do so many people make mistakes at the beginning? Because just looking at the unit price of the input is not enough

The most common mistake novices make is to just look at which company has the lowest input as soon as they open the pricing page. But in reality, AI API costs usually look at at least these few things together:

input price

output price

Are there any additional tool fees or search fees?

OpenAI's official pricing page lists input, cached input, and output separately; Anthropic lists base input, cache writes, cache hits, output, and prompt caching magnification separately; Google Gemini also separately handles context caching, storage, Grounding with Google Search, etc. This means that if you only compare the prices in a certain column, it is easy to choose a plan that is cheap on the surface but not economical in reality.

Why output is often more worth looking at than input

This is very important. In most cases, output is more expensive than input. OpenAI's GPT-5.4 nano input is $0.20/1M, output is 1.25; GPT-5.4 mini is 0.75 vs. 4.50. Anthropic's Claude Haiku 4.5 has an input of $1/MTok and an output of $5/MTok. This means that if your task itself is to generate long articles, multiple versions of answers, in-depth explanations or long reports, when you compare prices, you can’t just look at who has the lowest input, but rather whose output cost and output quality combined are more cost-effective.

The first usage: You are a high-frequency, simple, and large-volume task user

If your main needs are:

Then what you should compare most is not the flagship model, but the model that favors low cost and high speed.

The official price page of OpenAI shows that the input of GPT-5.4 nano is 0.20 US dollars / 1M, the cached input is 0.02, and the output is 1.25; OpenAI also directly writes that it is the cheapest GPT-5.4 level model, suitable for simple high-volume tasks. Anthropic's official price page shows that the base input of Claude Haiku 4.5 is $1/MTok, the cache hits are 0.10/MTok, and the output is 5/MTok. Google's official models page positions the Gemini 2.5 Flash-Lite as the fastest and most budget-friendly model in the 2.5 family. Looking at the bucket of "a large number of simple tasks" alone, OpenAI nano and Gemini Flash-Lite are usually worth comparing first.

If you fall into this category, the real question you should ask is not "which one is the strongest", but:

Which one is the cheapest for a large number of simple tasks that can tolerate a small amount of difference?

Which one doesn’t require you to rerun too many times?

At this time, many people will find that the cheapest ones are not necessarily high-end models, but small models.

Second usage: You are a daily content worker, focusing on stability, speed, and quality balance

If what you usually do most often is:

Doing SEO outlines

Then you are usually not looking for the "absolute lowest price", but something cheap enough to be used for a long time, but not so cheap that you keep making mistakes. The most important thing about this kind of usage is balance, not extreme price reduction.

OpenAI officially positions GPT-5.4 mini as the most powerful small model currently. The price is input $0.75/1M, cached input 0.075, and output 4.50. Anthropic's Claude Sonnet 4.5 is base input 3 / MTok, cache hits 0.30 / MTok, output 15 / MTok. Google Gemini's Flash route is obviously price-performance positioned, suitable for high-traffic and low-latency tasks. This means that if you are a daily content worker, OpenAI mini and Gemini Flash are usually easier to make the comparison list, while Claude Sonnet is often an option with more stable capabilities but a higher price.

In this case, "Which one is cheaper" really means:

Which one has a reasonable total cost if I use it every day?

Which company doesn’t have to sacrifice too much quality in order to save money?

Which company can help me repair less and run less frequently?

So if you are a daily content worker, don’t just compare the input unit price, but also the overall stability.

Third usage: You are doing high-quality long articles, complex analysis, reasoning or important tasks

The most common misunderstanding of this type of usage is: obviously the task is very important, but you insist on finding the cheapest model.

If your needs include:

high-risk coding

multi-step reasoning

Whether it's cheap or not, you can't just look at the superficial price. Because the real cost is likely to come from reruns, rework, and manual corrections.

OpenAI officially positions GPT-5.4 as the most powerful model. The price is input $2.50/1M, cached input 0.25, and output 15. Anthropic's Claude Opus 4.6 is input 5 / MTok, output 25 / MTok. The unit price of these models is obviously higher, but if your task inherently requires high quality, then the real comparison is not the cheapest, but the total cost of getting it right the first time.

This is also why many projects are not solved with "the cheapest one" in the end, but instead:

Cheap models for pre-processing

High-priced models for finalization

Because for high-value tasks, it is so cheap that it requires re-running many times, and it may not be really cheap.

Fourth usage: You are a batch, systematic, automated task user

SEO data pre-processing

Then what you should look at is usually not the single real-time price, but:

Is there a batch discount

Is there a cache hit price

Is there a high batch capacity

How about batch speed and limits

OpenAI official clearly states that the Batch API can reduce input and output by 50%. Anthropic's official pricing page also directly lists that Batch processing can save 50%, and prompt caching and batch discount can be stacked. This means that if you are a user of systematic tasks, which one is cheaper often does not depend on the real-time single price, but whether it can run cheaply when running a large number of tasks.

In this case, the real question is:

Does this company have batch?

How much cheaper is batch?

Is the cache value worth using?

Can I use a low-price model and add batch processes to reduce the overall cost?

Cheap, not only depends on the price, but also on the speed

Speed is also something that many people forget when comparing prices. OpenAI officially positions GPT-5.4 nano and mini towards cost and high-frequency tasks; Anthropic's Haiku 4.5 is obviously a low-price line; Google's Flash/Flash-Lite is also clearly oriented towards speed and large-scale use. These official materials actually remind you: if you are doing real-time chat, customer service, or interactive products, then a model that is too slow may not necessarily be considered cheap even if the unit price is low. Because time cost and experience cost will eventually come back to affect your overall CP value.

The most practical way for novices to judge: first ask yourself what kind of person you are

If you want to make a quick judgment, you can use this method first:

Run many simple tasks every day, focusing on cheapness and speed. At this time, you should first look at low-cost routes such as GPT-5.4 nano, Claude Haiku 4.5, and Gemini Flash-Lite.

I use it every day, but I don’t want it to be too unstable just because it’s cheap. At this time, it is more worthwhile than mid-level balanced options such as GPT-5.4 mini, Gemini Flash, and Claude Sonnet.

You are a high-quality task type

Your focus is not the cheapest, but the overall results should be stable and there should be less rework. At this time, the price comparison method cannot only look at the unit price, but also the first-time success rate. Such tasks usually rely on GPT-5.4, Claude Opus, and high-level Gemini lines.

You are a batch and system type

You need to look at batch, caching, and limits, not just the real-time price. At this time, OpenAI, Anthropic, and Google all need to look at the batch and cache structure together.

Which AI Token is the cheapest?

There is no single answer that works for everyone. For simple high-frequency tasks, OpenAI's GPT-5.4 nano, Anthropic's Claude Haiku 4.5, and Google's Gemini Flash-Lite are all low-cost routes worth looking at first, but which one is the most cost-effective still depends on the type of your task.

Can I only look at the input unit price?

Not recommended. The official pricing pages of OpenAI, Anthropic, and Google all list output prices separately, and in most cases output is more expensive than input, so it is easy to misjudge just by looking at input.

If I just write copy and abstracts every day, which one should I write first?

First compare models with low to medium price, fast speed, and sufficient stability, such as GPT-5.4 mini, Gemini Flash, Claude Haiku / Sonnet, etc., instead of directly comparing with flagship models. This is a practical judgment based on official model positioning.

Is batch task suitable for finding cheap API?

yes. OpenAI officially says that the Batch API can save 50%, and Anthropic also has a batch structure, so a large number of non-real-time tasks are usually more suitable to see which one is more cost-effective based on batch capabilities.

Is Claude necessarily more expensive?

It cannot be simplified like this. The Claude Sonnet / Opus does cost more than the Haiku, but if your tasks require higher quality output, the actual total cost is not necessarily less cost-effective. The official pricing page originally divides different models into different task levels.

Is Gemini cheap?

Some of Gemini's Flash / Flash-Lite routes are very competitive in price, but you need to look at output, caching, grounding and tier restrictions together, not just one input number.

Data source and credibility statement

This article is compiled and written based on the official models and pricing documents of OpenAI, Anthropic and Google, mainly referring to the following official information:

OpenAI｜API Pricing||OpenAI｜Introducing GPT-5.4 mini and nano

OpenAI｜GPT-5.4 nano model page

Anthropic｜PricingGoogle AI for Developers｜Gemini API pricing

Google AI for Developers｜Models

This article is organized in a three-layered manner of "official pricing × model positioning × usage stratification". The focus is not just to compare who has the lowest unit price, but to help readers separate common usages such as high-frequency simple tasks, daily content work, high-quality tasks, and batch system tasks, and establish a price comparison method that is closer to real usage scenarios. The article involves descriptions of GPT-5.4 nano/mini, Claude Haiku 4.5, Gemini Flash/Flash-Lite, etc., all of which are based on official public pricing and model positioning.

If you want to quickly understand the overall differences in price, speed, and usage of different AI models, you can first read this 2026 AI model comparison for lazy people: price, speed, and usage at once

If you want to start from a more complete teaching entrance, you can also go back to AI Token

This article belongs to the category of "AI Token Cost".

This category mainly organizes AI Token prices, AI Token fees, model pricing methods, platform differences, cost interpretation and price comparison logic to help novices, content creators, case recipients and enterprises when they come into contact with AI APIs, not only know how to look at the price list, but also know how to put different usage scenarios back together to determine which solution is more cost-effective.

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Which AI Token is cheaper? Before comparing, first understand what kind of usage you are using