Top Model API Price Comparison: AI's "Hourly Wage" Is Surpassing Human Workers

By | Large Model Home

On October 24, Moonshot AI launched Kimi K3.1 Pro. As a flagship model with 3 trillion-level parameters and support for a 1 million Token context, its capabilities on tasks such as programming and long-horizon agents drew attention. But not long after the new model went live, the focus of user discussion shifted from how its performance was to complaining that the K3.1 Pro quota provided by Moonshot AI was too low.

In response to user feedback, in the early hours of October 26, Kimi publicly apologized, announcing that it would compensate all subscription members within their validity period with usage equivalent to 25% of the monthly total quota, while also adjusting configurations to reduce unnecessary sub-Agent calls and output length.

This also puts a new question before AI companies: models not only have to work hard to prove how smart they are, but also have to start studying how to make them do less work and save Tokens.

Especially when the API prices of some top-tier models are laid out, bosses discover that they originally wanted AI to do work for employees, but as a result, the usage cost of some AI can no longer be left out of the same accounting as employee salaries. Workers get paid for going to work; AI burns Tokens when it goes to work. In the past, bosses worried about employees slacking off; now bosses also have to keep an eye on AI's bills.

01 AI pricing is beginning to surpass the worth of workers

Large Model Home noticed that the API prices of some top-tier models at home and abroad are indeed far greater than the "hourly wage" of human employees.

Here, OpenAI's GPT-6 Astra, Anthropic's Claude Fable 5.1, and Moonshot AI's Kimi K3.1 Pro are selected for comparison. The three represent different vendors' pricing strategies for high-end models.

Taking GPT-6 Astra as an example, processing 1 million Tokens of input and generating 1 million Tokens of output, calculated at standard prices, costs 60 US dollars. Claude Fable 5.1 is also the same price. Kimi K3.1 Pro is priced in RMB; completing the same scale of input and output costs 81 yuan; if the high-speed version is used, it is as high as 162 yuan!

Of course, 1 million Tokens does not equal 1 million characters, nor does it mean that an ordinary conversation will spend that much money. But for Agents that need to process long documents, write code, and repeatedly call tools, the Tokens consumed by input, output, and intermediate reasoning may all accumulate.

So why exactly are these models so expensive?

First, these models sell not just an answer, but the ability to handle complex tasks. Simple Q&A can be handed to lower-priced models, but if a task involves large codebases, long-document analysis, multi-turn reasoning, tool calls, and continuous execution, enterprises usually care more about whether the model can get the job done than whether it can give a polished sentence.

Second, the reasoning process itself also incurs costs. A model answering more complexly does not necessarily mean it is more expensive; but in tasks that require long reasoning and repeated tool calls, Token consumption may increase significantly. Therefore, the more models are used for complex work, the more enterprises cannot look only at the listed price per million Tokens, but also at how many Tokens were actually used to complete the task.

Finally, model vendors also need to invest costs in training, inference infrastructure, and service capabilities. The pricing of top-tier models reflects both the capabilities they seek to provide and the vendors' business judgment about high-value tasks.

Judged by "salary," the "hourly wage" of top-tier AI has indeed become expensive enough to surpass many "workers." Data from the National Bureau of Statistics shows that in 2025 the average annual wage of employed persons in urban private units nationwide was 71,600 yuan, equivalent to about 34 yuan per hour; the average annual wage of professional and technical personnel in enterprises above designated size was only 155,500 yuan, equivalent to about 75 yuan per hour.

Calculated at the standard API prices of some top-tier models, processing 1 million Tokens of input and generating 1 million Tokens of output may cost tens of US dollars in a single instance, equivalent to the average wage of an ordinary employee for dozens of hours.

In the end, although enterprises are not afraid to pay for capability, what they fear is spending a lot of money and finally discovering that this job actually did not need such an expensive model.

If employees hold a meeting for too long, the boss can still tell them to "get to the point" to reduce time costs. But if AI does not have its task scope properly controlled, it may run through retrieval, reasoning, tool calls, and repeated revisions all in one go. Never mind how the final report turns out; the Token expense may directly Double.

As AI enters all walks of life, enterprises have for the first time the opportunity to break part of knowledge work into callable, measurable services. Previously, enterprises paid wages to buy employees' labor within a certain period of time; now, enterprises call models to buy specific pieces of capability.

It sounds very advanced, but when it comes down to enterprises, it is still the old cliché: how much money was spent, and how much was accomplished?

Especially for small and medium-sized enterprises, budgets are often not that ample. Using a high-end model occasionally may not hurt; but if it is connected into business processes such as customer service, programming, marketing, and research, with hundreds or thousands of calls every day, costs can no longer be judged by feel.

At this point, what the boss wants most may not be an AI that can do everything, but an AI that knows when to be serious and when there is no need to overthink. Don't deploy a fully loaded configuration for simple tasks, and don't ruin things just to save money on complex tasks. Otherwise, the boss originally wanted to hire a digital employee that does not need rest, but in the end finds that he has raised an extra team that never gets tired but charges a fee every time it starts work.

03 Enterprises need more "Big Fat Fish"

Recently, the busiest one in the AI circle may not be the CEO of any major model company, but a big blue fat fish (the personified image of DeepSeek).

With the image of "Whale Girl," it appears in memes, fan comics, and desktop pets; some people make animations for it, some keep it on their computer desktops, and some developers have even turned it into an interactive desktop pet. On Bilibili, this fish's presence is growing even stronger, and related fan creations have swum all the way from the AI circle into the ACG community.

What is even more interesting is that this fish is not only cute in appearance, but its persona is also quite down-to-earth: it loves eating plain rice, occasionally acts silly, and even while working, it still thinks about taking a break. It is not like an omnipotent superintelligence, but rather like an employee with decent ability, a somewhat odd temper, but not too expensive to keep. No wonder many AI enthusiasts are willing to draw pictures for it, write jokes about it, and even turn it into a pet on their desktops.

Why did this fish become popular? On the surface, it is because netizens played with the meme until they developed affection for it; looking deeper, it is because everyone has finally found a concrete personality for an abstract large model.

DeepSeek is not always the smartest or most stable model, and sometimes it may even seem a bit dumb. But it is cheap enough, and on many tasks it can indeed get the work done.

As a result, the model's limitations were turned into jokes by netizens, while its low-cost advantage was condensed into the three words "easy to feed." The big fat fish looks like it is acting cute, but in fact it expresses on users' behalf a very realistic preference: it does not have to be perfect at everything, but don't make it too expensive, and it still has to deliver when it matters.

This is also why the image of "Big Fat Fish" is easy to remember. If top models solve the question of "whether complex work can be done well," then another question that makes DeepSeek attractive to enterprises is: can enough work be done well at a lower cost?

Behind this image is actually a change in enterprises' procurement logic.

In the past, when enterprises chose large models, they often looked first at capability rankings. Whichever model was stronger was more likely to enter the candidate list. But as AI moves from trial use to production environments, enterprises have begun to care more about another set of questions: how many tokens does it take to complete a task? If it runs thousands of times a day, how much will it cost in a month? If the task volume expands tenfold, will costs also spiral out of control?

DeepSeek's low-price strategy happens to make these questions more concrete.

Taking DeepSeek V4.1 Flash as an example, public pricing shows that its off-peak input price is $0.15 per million tokens and output price is $0.60; during peak hours, they are $0.30 and $1.20, respectively. Compared with the standard prices of the several high-end models mentioned earlier, this creates an obvious price gap.

This does not mean that DeepSeek can replace top models on all tasks, nor does it mean that a low price necessarily brings lower actual costs. When different models complete the same task, token consumption, success rates, and the number of tool calls may all differ. But it at least provides a new option: enterprises do not have to pay top-model prices for every piece of work.

According to the latest OpenRouter data estimates, last week (October 19 to 25), the total global AI large-model call volume was 188 trillion tokens, up 14.63% week-on-week. Among the AI large models on the list, the weekly call volume of Chinese AI large models reached 93.54 trillion tokens, while the weekly call volume of US AI large models during the same period was 22.47 trillion tokens. The weekly call volume of Chinese large models has exceeded that of the United States for twenty-six consecutive weeks, firmly ranking first in the world.

Among them, the top five in global call volume are all Chinese AI large models. Among them, DeepSeek V4.1-Flash-1017 ranked first, with a weekly call volume of 33.6 trillion tokens, up 801% week-on-week.

This is also why the "DeepSeek kill line" holds: models with similar prices are not as powerful as it; other models with comparable performance are far more expensive than it.

When an option appears that is cheaper and close enough in capability, enterprises have to consider switching models.

The reason "Big Fat Fish" is widely favored is not because its performance is top-notch, but because it is cheap enough, can endure hardship and work hard, and does not need to use the most expensive capabilities every time. For enterprises, this kind of model may not win every leaderboard, but it may gain a large number of usage opportunities in real business.

04 AI's payroll ultimately still has to be paid by productivity

After all, when enterprises begin to seriously calculate model costs, sooner or later they will also seriously calculate labor costs.

AI is billed by tokens, and employees are paid monthly salaries, but ultimately both will be put into the enterprise's operating ledger. What the boss wants is not the smartest AI, but productivity that makes the money spent produce results. As for whether this work is ultimately handed to a model or an employee, the answer is not on the leaderboard, but in the comparison between cost and output.