100 篇 SEO 文章需要多少 AI Token 预算？ The content marketing team first grasps this

If your team is doing content marketing automation, the most common question encountered is usually not which model is the strongest, but the more direct sentence: To produce 100 SEO articles every month, how much AI Token budget should be prepared?

If you want to understand the most basic pricing logic first, you can also read the AI Token price first. Newbies should first understand where the fees come from.

Let me make it clear first: What is this article counting?

What this article counts is not the total budget of the entire content department, nor the manpower, images, CMS, SEO tools or outsourced editing fees. It is about how many tokens the model itself will consume when using an AI model to produce 100 SEO articles per month, and how much budget it will eventually cost.

What items are included in the AI Token budget here

It can be mainly divided into two parts. The first is the input cost, which is the brief, keywords, structure, revision requirements, and format requirements you throw to the model. The second is the output cost, which is the outline, text, FAQ, Meta field, paragraph rewrite and title version returned to you by the model.

What this article does not count

This article does not count team manpower, image tools, on-site submission costs, keyword research tools or external consultant fees. You can understand it as: first capture the text cost of the AI model itself, and then add other operating costs.

Why the budget gap can be huge for the same 100 articles

When many people estimate the AI Token budget for the first time, they will directly multiply 100 articles by the unit price of a certain model, but this algorithm is usually too rough. Because what really affects the monthly budget is not just the number of articles, but how deep your content workflow is.

If the process is very shallow, the cost will be relatively low

If your process is just to give a question, ask the model to produce a first draft, and then revise it a little and then put it on the website, then the token usage is usually not too high. In this case, although the number of articles is large, the model consumption per article is actually relatively simple.

If the process is very deep, the cost will be magnified

But if your process is to first organize the search intent, regenerate the title, then make an outline, then write the main text, then add FAQ, then Meta Title, Meta Description, Slug, Tags, and finally do one or even two rounds of revision, then the Token usage of the same article may be doubled or even tripled.

What really widens the budget gap is the workflow, not the number of articles

So what you should really estimate is not just the number of 100 articles, but "how many times will the model be called for an article from title to final draft?" Many content teams end up losing not because the models are too expensive, but because the process is too fragmented, there is too much heavy work, and tokens are consumed repeatedly at every step.

First grab the most practical benchmark: how many tokens will be used for an SEO article

If you are using AI to make SEO articles, you can actually use ranges to capture an article first, and there is no need to pursue very precise results from the beginning. For the content team, it is usually more important to grasp the general direction first than to calculate to the decimal point first.

This type is more suitable for teams with a fixed format, few revisions, and medium-depth articles. Common processes include title brief, simple outline, first draft of text, FAQ and Meta fields. For this type of process, one article can first capture 8,000 to 12,000 tokens.

This is closer to most teams that are really doing SEO content. You not only need the main text, but also search intent sorting, title generation, outline planning, FAQ, Meta fields, a round of revision and initial reinforcement. In this type of process, an article can usually capture 15,000 to 20,000 tokens first.

If you are producing long articles, high-standard content, high brand tone requirements, or multiple rounds of workflows and more automated connections, the Token consumption of each article will usually be higher. For this type of process, one article can first capture 25,000 to 40,000 tokens.

100 articles per month, you can first grasp these three budget scenarios

If you are fixed to produce 100 SEO articles every month, the simplest way is to first multiply the number of articles by the range of a single workflow.

About 10,000 tokens per article. 100 articles per month is approximately 1,000,000 tokens.

About 18,000 tokens per article. 100 articles per month is approximately 1,800,000 tokens.

About 30,000 tokens per article. 100 articles per month is approximately 3,000,000 tokens.

If you just want to grab a practical scope that can be used for budget meeting discussions, you can first remember this sentence: 100 SEO articles per month, the monthly usage can mostly be 1 million to 3 million Tokens, and the middle value usually falls around 1.8 million.

Let’s look at the money: How much is the official price?

To truly estimate the monthly cost, you need to incorporate the current Token usage into the official price. Because model prices will change, the official price page shall prevail here.

The approximate price range of OpenAI

OpenAI official API Pricing shows that the price of GPT-5.4 mini is US$0.75 per 1 million tokens for input and US$4.50 per 1 million tokens for output; GPT-5.4 nano is US$0.20 for input and US$1.25 for output. Officials also state that the Batch API can save another 50% of input and output costs.

The approximate price range of Google Gemini

The Google Gemini API price page shows that the input price of Gemini 2.5 Flash-Lite is US$0.10 per 1 million tokens, and the output price is US$0.40 per 1 million tokens; if you use grounding with Google Search, there will be additional fees after exceeding the free limit.

Anthropic’s approximate price range

Anthropic’s official price page shows that the price of Claude Haiku 3.5 is $0.80 per 1 million tokens for input and $4 for output per 1 million tokens; Claude Haiku 3 is $0.25 for input and $1.25 for output.

Using the standard process to do a trial calculation for you to see

We use a middle value that is more similar to the real situation of the content team to calculate. Assuming that each article inputs an average of 8,000 tokens and outputs 10,000 tokens, then 100 articles per month means an input of 800,000 tokens and an output of 1,000,000 tokens.

If you enter 800,000 tokens with GPT-5.4 mini

around $0.60. Outputs 1,000,000 tokens, which is approximately $4.50. That adds up to about $5.10/month.

If you enter 800,000 tokens with GPT-5.4 nano

approximately $0.16 USD. Outputs 1,000,000 tokens, which is approximately $1.25. That adds up to about $1.41/month.

If using Gemini 2.5 Flash-Lite

Enter 800,000 tokens, approximately $0.08 USD. Outputs 1,000,000 tokens, which is approximately $0.40. That adds up to about $0.48/month.

If you enter 800,000 tokens with Claude Haiku 3.5

approximately $0.64 USD. Outputs 1,000,000 tokens, which is approximately $4.00. That adds up to about $4.64/month.

Seeing this, you may feel that the model fee is lower than expected

This is the reaction that many people have when they do a trial calculation with the official price for the first time. Because if you only count pure text tokens and the process is well controlled, the model text cost of 100 SEO articles per month may indeed be lower than you think.

Why many people still think it is expensive

The reason is usually not the text model fee itself, but the complexity of the subsequent process. When you add search, external tools, workflow orchestration, multiple rounds of revisions, failed reruns, model division of labor, and manual proofreading, the overall cost begins to amplify.

What is really expensive in content marketing is often not the text itself

So if you only ask "How much does the model cost for 100 articles", the answer may not be high; but if you ask "100 articles must be stable online, comply with SEO, manageable, and deliverable", then you cannot just look at the pure text token price.

What you really need to be careful about is not the main text, but the additional steps

The most easily distorted part of content marketing automation is that many teams only count the main text and not the process.

The search function may be billed separately

If each of your articles requires additional searching, data sorting, and context addition, then the model fee will not just be simple input and output, but also tool costs or additional token consumption.

Batch and cache may help you save a lot

If you are producing high-frequency mass-produced content, some suppliers’ batch and cache rules may actually reduce the overall cost. This is also something worth looking at together when the content team is making monthly budgets.

Multiple rounds of revisions will enlarge the Token

If you complete an article not in one go, but in three rounds of revisions, rewriting the introduction, filling in FAQs, and rearranging paragraphs, then the total tokens will soon not be more than 10,000 for a single article, but will reach 20,000 or even more than 30,000.

The most suitable thing for the content marketing team is not to find the cheapest, but to divide the labor among models

If you really want to do 100 articles every month, usually the most recommended idea is not to use the strongest model for all, nor to use the cheapest model for all, but to divide the labor.

Cheap models do high-frequency, standardizable work

Such as title variations, FAQs, Meta fields, paragraph editing, first drafts, and repetitive formatting work, which are usually suitable for handing over to lower-cost models.

Higher-order models do high-value work

For example, key introductions, fine-tuning of brand tone, business page paragraphs, difficult topics and content that require higher-precision reasoning are more worthy of being handled by higher-order models.

What are the benefits of doing this

The benefit is very straightforward: you don’t have to make 100 articles eat the same cost from beginning to end, but leave the higher cost model to where it is really valuable.

For the content team, how should the budget be grasped so that it is not easily distorted

The most practical way is not to directly ask "how much does 100 articles cost", but to first define the standard process for an article.

First define the standard workflow for a single article

For example, you can first fix: an article must include a title, brief, outline, body, FAQ, Meta field and one revision. In this way, you will have a consistent standard when evaluating Tokens.

First run 5 to 10 articles, and then multiply by 100

Then run 5 to 10 articles first, record the average Token usage, and then multiply by 100. The monthly budget you get out of this way will be much more accurate than the formulas commonly discussed on the Internet.

The best budget algorithm is not to guess the number of articles, but to measure one article first

Because what you really want to manage is not the number 100, but the complete workflow of a piece of content.

Which type of team is most likely to underestimate the AI Token budget

The first type: only counting the text, not the process

This kind of team most often misses FAQ, Meta, rewriting, supplementary paragraphs, title variations and format adjustments.

The second type: rely on high-order models for every step

It’s not that it can’t be done, but the ROI is often not pretty, because many SEO article processes can actually be completed with cheap models first.

The third type: There are search, tools, and workflows, but no tracking

This kind of team is most likely to wonder why the bill keeps getting longer even though the unit price is not high, because what really drives up the cost is the entire process, not a single model.

If you want to produce 100 SEO articles every month, the pure text token budget is usually not as high as you think. For cheap or lightweight models, the monthly cost may even be as low as a few dollars if only the input and output tokens are counted; but once your process includes search, tool calling, multiple rounds of revisions, and the use of higher-end models, the overall budget will go up significantly.

What content marketing automation really needs to control is not a single article, but the entire content production process. When your goal becomes 100 SEO articles per month, AI Token is not just a technical fee, but a fixed operating cost that can be estimated, tracked, and optimized by the content department.

The first thing you should do is not to rush to chase the strongest model, but to dismantle the process first: which steps can be templated, which steps can be handed over to cheap models, which steps are worthy of being handed over to higher-order models, and which steps should not be redone at all. As long as the process is done correctly, the AI Token budget is usually easier to control than you think.

100 SEO articles per month, is AI Token necessarily expensive?

Not necessarily. When only counting pure text input and output, the official price of the lightweight model is actually not high. What really makes the budget bigger is usually a mix of search, tool invocation, multiple rounds of revisions, and high-end models.

What is most likely to be missed when estimating the AI Token budget?

The most commonly missed ones are the workflow steps. Many people only count the main text, but do not include the brief, outline, FAQ, Meta field, revision and search.

Which model strategy is best suited for content marketing teams?

Usually not all use the same model, but division of labor. Work with a large amount of repetition and clear rules can be handed over to cheaper models; high-value paragraphs and difficult topics can be handed over to higher-level models.

Why are the budgets of different teams so different for the same 100 articles?

Because the depth of the process of each article is different. Some people only generate it once, while others will do the title, outline, text, FAQ, Meta, revision and search. The Token usage is naturally very different.

Should the AI Token budget be grasped all at once, or adjusted as you go?

A better approach is to capture the monthly range first, and then use the first 5 to 10 articles to make corrections. This is closer to reality than trying to stick to a single number in the first place.

What is the difference between this article and the general AI Token fee article?

This article is not a general discussion of fees, prices or cost formulas, but is locked in the application scenario of "content marketing automation/100 SEO articles per month". The focus is to help the content team grasp the monthly budget.

Data source and credibility statement

This article focuses on the content marketing automation scenario of "producing 100 SEO articles per month", collates the practical budget estimation method of AI Token, and refers to the official pricing information disclosed by mainstream model suppliers, including OpenAI API Pricing, Gemini API Pricing and Anthropic Pricing. The focus of the article is not to simply compare who is the cheapest, but to break down the common workflow of content teams so that you can understand more easily: when SEO articles change from single article production to fixed monthly output, how to grasp the AI Token budget is closer to the actual usage.

If you want to understand the basic pricing logic of AI Token first, you can also go back to the AI Token price first. Newbies should first understand how the fees come from, and first clarify how the fees are composed.

If you want to understand model comparison, cost differences and usage methods from a more complete perspective, you can also go back to the AI Token summary page first.

This article belongs to the category "AI Token Fees"

This category mainly organizes the prices, fees, cost estimates and budget planning of AI Tokens. It is suitable for readers who want to understand how models are priced, how different usages affect costs, and how individuals or teams can grasp the AI usage budget. If your question is no longer "What is AI Token?" but "How much does it cost to use it like this?", then this category is the most suitable place to read further.

What’s the price of AI Token? Newbies should first understand where the fees come from

How to calculate the cost of AI Token? It can be seen most clearly from the separation of input and output

How to estimate the cost of AI Token? The most practical method for individual users

AI Token budget
SEO article generation
Content team AI cost

AI Token organizes the basic concepts, calculation methods, API fees and model comparisons of AI Token (word elements), and covers common models such as ChatGPT, Gemini, Claude, etc. to help you establish clear understanding and judgment faster.

100 篇 SEO 文章需要多少 AI Token 预算？ The content marketing team first grasps this