Quantitative Assessment of AI Cost Efficiency

May 9, 2026

Quantitative Assessment of AI Cost Efficiency

Your boss asks: “How much will it cost to equip employees with AI assistants, and is it worth it?” You can’t answer, and you feel uneasy. This article discusses how to calculate this account clearly.

Background

In recent years, various AI programming assistants like Claude Code and GitHub Copilot have flooded in like a tide. As a technical person, you’ve probably already started using them and feel they indeed improve efficiency significantly, like someone handing you a ladder.

But when you really need to discuss ROI with your boss or clients, you often get stuck—how do you quantify the subjective feeling of “efficiency improvement”? I actually understand this feeling. It’s like when you like someone and others ask you “what do you like about her,” you hem and haw for a long time and only say “I just like them.” This is understandable, but your boss wants numbers, not your feelings.

And this isn’t the only problem:

ROI: Is the cost of equipping the team with AI tools worth it?

Efficiency Quantification: How do you translate “efficiency improvements” across different positions and usage levels into measurable metrics?

Risk Assessment: If competitors massively adopt AI, how much will our competitiveness be affected?

Traditional ROI calculations often overlook two key factors:

Enterprise Total Cost Perspective: Only considering salary, ignoring city differences, social insurance, housing fund, and other additional costs
Token Economic Model: Lack of a calculation framework that links AI usage (Tokens) to actual output

Both factors are indispensable. Let me give you a real example: For the same 300k annual salary, the actual cost difference for enterprises between Beijing and Wuhan may exceed 30%. And this doesn’t even include the cost of AI usage itself. Cost is like an iceberg in the sea—you only ever see one corner…

About HagiCode

The solution shared in this article comes from our practical experience in the HagiCode project.

Speaking of which, HagiCode is just an AI code assistant project. It’s just that during development, we确实 need to accurately assess the cost-effectiveness of different AI models—after all, money doesn’t grow on trees. To this end, we built a complete calculation framework and open-sourced the HagiCode Cost assessment tool.

If you’re also thinking about AI cost issues, this solution might give you some reference. Or maybe not, I can’t guarantee that, just giving it a try.

Core Calculation Framework

A complete AI cost-benefit assessment requires establishing a three-layer model:

Input Layer
├── Annual Salary Data
├── City Tier Coefficient
├── AI Model Selection
├── Efficiency Multiplier Estimate
└── Daily Token Usage

Calculation Layer
├── Enterprise Total Cost Accounting
├── AI Annual Cost Calculation
├── Cost Ratio Analysis
├── ROI Calculation
└── Equivalent Headcount Calculation

Output Layer
├── AI Cost Ratio
├── Efficiency Gain
├── ROI
├── Equivalent Headcount
└── Disruption Risk Assessment

This framework looks quite complex, like it’s going to confuse people. Actually, the core logic is simple: calculate the enterprise’s real labor cost clearly, calculate the AI’s annual cost clearly, and then look at the ROI and equivalent headcount. After all, simplifying complexity is the right way.

Calculation of Key Metrics

Enterprise Annual Total Labor Cost

First, let’s talk about enterprise total cost. This isn’t simply annual salary multiplied by 12 months. The real cost needs to consider two factors:

City Coefficient: The additional costs in first-tier cities (Beijing, Shanghai, Guangzhou, Shenzhen) are about 30% higher than other cities. This includes social insurance, housing fund, various benefits, and the living cost premium of first-tier cities—after all, the cost of living in Beijing is indeed different from Wuhan.

Additional Employment Costs: Roughly equivalent to 1 month’s salary, used to cover year-end bonuses, various subsidies, office equipment amortization, etc. These amounts may seem small, but they add up.

So the formula is:

Enterprise Annual Total Labor Cost = Annual Salary × (1 + City Coefficient) + Annual Salary/12

City coefficients can refer to this standard:

First-tier cities (Beijing, Shanghai, Guangzhou, Shenzhen): 0.4
New first-tier (Hangzhou, Chengdu, Suzhou, Nanjing): 0.3
Second-tier cities (Wuhan, Xi’an, Tianjin, Zhengzhou): 0.2
Other cities: 0.1

AI Annual Cost

AI cost calculation is a bit more convoluted because AI models charge by Token. And input and output prices are different—output is usually 5-10x more expensive than input. This isn’t surprising, after all output is AI “working,” input is just you “talking.”

In code scenarios, the input-output ratio is about 3:1, so we can calculate a composite unit price:

// Composite unit price (based on 3:1 input-output ratio)
Composite Unit Price = (3 × Input Price + Output Price) / 4

// Daily cost
Daily AI Cost = Daily Token Usage(M) × Composite Unit Price

// Annual cost (based on 264 working days)
Annual AI Cost = Daily AI Cost × 264

For example, GPT-5.4’s input price is 2.5 USD/1M Token, output price is 15 USD/1M Token. Then the composite unit price is:

Composite Unit Price = (3 × 2.5 + 15) / 4 = 5.625 USD/1M Token

Converted to RMB (assuming exchange rate 1 USD = 7.25 CNY):

Composite Unit Price = 5.625 × 7.25 = 40.78 CNY/1M Token

This exchange rate will change, it’s just fixed for calculation convenience.

Core Benefit Metrics

With the two costs above, we can calculate core metrics:

// AI cost ratio
AI Cost Ratio = Annual AI Cost / Enterprise Annual Total Labor Cost

// Efficiency gain
Efficiency Gain = Efficiency Multiplier - 1

// AI ROI
AI ROI = Efficiency Gain / AI Cost Ratio

// Affordable workflow copies
Affordable Copies = Enterprise Annual Total Labor Cost / Annual AI Cost

// Equivalent headcount
Equivalent Headcount = 1 + (Efficiency Multiplier - 1) × min(Affordable Copies, 1)

The meaning of these metrics:

AI Cost Ratio: The percentage of enterprise labor cost required to maintain Agent workflows. The lower this number, the more “cost-effective” the AI usage. Everyone likes saving money, right?

ROI: Efficiency Gain ÷ AI Cost Ratio. Less than 1 means “somewhat wasteful,” greater than 2 means “very cost-effective.” This is actually quite understandable—like you spending money to buy time, calculate it and you’ll know if it’s worth it.

Equivalent Headcount: There’s a point here that’s easily misunderstood. It’s not directly accepting the efficiency multiplier, but looking at whether the enterprise can afford this AI workflow. If affordable copies is less than 1, then equivalent headcount won’t reach your expected efficiency multiplier. After all, you can’t make bricks without straw…

Practical Calculation Example

Let’s do a practical calculation example. Assume a backend developer in a first-tier city:

Annual salary: 300k
Using GPT-5.4, efficiency multiplier: 2.5x
Daily Token usage: 12 M

Step 1: Calculate enterprise total cost

Enterprise Total Cost = 30 × (1 + 0.4) + 30/12 = 44.5万

Step 2: Calculate AI annual cost

Composite Unit Price = 40.78 CNY/1M Token
Daily Cost = 12 × 40.78 = 489.36 CNY
Annual Cost = 489.36 × 264 = 129,191 CNY ≈ 12.9万

Step 3: Calculate benefit metrics

AI Cost Ratio = 12.9 / 44.5 = 29%
Efficiency Gain = 2.5 - 1 = 150%
ROI = 1.5 / 0.29 = 5.17x

Step 4: Calculate equivalent headcount

Affordable Copies = 44.5 / 12.9 = 3.45
Equivalent Headcount = 1 + (2.5 - 1) × 1 = 2.5 people

What’s the conclusion? The ROI of this AI usage exceeds 5, falling in the “very cost-effective” range. If the entire team uses it, forming a capacity advantage of about 2.5 people, it will be very competitive in the market.

This is actually quite reasonable—after all, the money you spend on AI is far less than your additional output. This deal is worth it.

Impact of Multi-Agent

HagiCode discovered an interesting phenomenon in actual use: there’s an upper limit to single Agent efficiency improvement.

This is actually quite natural—like no matter how capable a person is, they can only do one thing at the same time. After all, you’re not an octopus.

Traditional single Agent usage patterns have several bottlenecks:

Serial Limitation: Proposal→Implementation→Review→Fix, must wait in sequence. No matter how fast an Agent is, it can only do one thing at a time. This is like cooking—washing vegetables, cutting vegetables, stir-frying, can only be done step by step.

Quota Waste: Monthly quota limits cannot be fully utilized. If this month’s quota isn’t used up, it can’t be carried over to next month. This isn’t surprising, just a bit regrettable.

Context Switching: Different tasks need to repeatedly establish context, each time having to restate background information. Like when you talk about the same thing with different people, you have to start from the beginning each time, quite tiring.

HagiCode’s multi-Agent architecture solves these problems through parallel sessions:

Parallel 10x+: Multiple Agents drive multiple instances simultaneously, truly achieving parallel work
Throughput Improvement: Proposal, implementation, fixes can proceed in parallel without waiting for each other
Token Utilization Improvement: OpenSpec process reduces rework, amortizing equivalent consumption

The change this brings is huge. Using the previous example, if using HagiCode multi-Agent architecture:

Parallel sessions: 4
Token utilization improvement: 1.5x

Calculation after amplification:

Amplified Efficiency = 2.5 × 4 = 10x
Optimized Daily Token = (12 × 4) / 1.5 = 32 M
Optimized Annual Cost = 32 × 40.78 × 264 = 34.4万

New benefit metrics:

New AI Cost Ratio = 34.4 / 44.5 = 77%
New ROI = 9 / 0.77 = 11.68x
New Equivalent Headcount = 1 + (10 - 1) × 1 = 10 people

Although AI cost ratio rose from 29% to 77%, ROI improved from 5.17x to 11.68x, and equivalent headcount went from 2.5 people to 10 people.

This is the power of multi-Agent parallelism. One Agent is one person, ten Agents are a team…this difference is not just a little bit.

Practical Considerations

Don’t Get City Coefficient Wrong

Employment cost differences across different cities are significant—first-tier cities’ additional costs are about 30% higher than other cities. When calculating, be sure to use the correct city tier. This number being off by a little can make the final result differ significantly. After all, a small error can lead to a large deviation…this is an old saying, but it still makes sense.

Input-Output Ratio Is Not Fixed

Code scenarios default to a 3:1 input-output ratio, which matches the actual prompt-to-code generation ratio in programming. But if you’re doing other types of work—like writing copy, doing data analysis—this ratio might be completely different.

This is actually normal—different work, different methods.

Efficiency Multiplier Is Subjective

Efficiency multiplier is a subjective estimate,建议 combining actual observation:

1.5-2x: Familiar with basic functions, occasional use
2-3x: Proficient, daily high-frequency use
3x+: Deep integration, forming专属 workflows

Don’t estimate too high from the start—observe for a while before adjusting. After all, expectations too high lead to greater disappointment.

How to Calculate Token Usage

If you don’t know your daily Token usage, you can estimate it this way:

Check platform usage statistics (Claude, OpenAI both have them)
Record Token consumption for several typical conversations, take an average
Multiply by your daily conversation count

Or directly use HagiCode Cost to calculate—there are reference values for common scenarios. This is also convenient, saving you from blind折腾.

Impact of Exchange Rate Fluctuations

USD models need exchange rate conversion, but exchange rates change. Calculators generally use a fixed rate (like 1 USD = 7.25 CNY), and actual costs may vary due to exchange rate fluctuations. This error is usually not large, but keep it in mind.

After all, everything has an approximation—precise to several decimal places isn’t really necessary…

Technical Implementation Points

If you want to implement this calculation logic yourself, there are several technical details worth noting:

Multi-Currency Support

function convertCnyAmountToCurrency(
  amountCny: number,
  targetCurrency: "USD" | "CNY"
): number {
  if (targetCurrency === "CNY") return amountCny
  return amountCny / EXCHANGE_RATE_USD_TO_CNY
}

This code isn’t really much to say about, just simple currency conversion.

Multi-Language Localization

function getLocalizedModelCopy(
  model: ModelPricing,
  language: SupportedLanguage
): LocalizedModelMeta {
  return {
    description: language === "zh-CN"
      ? model.description
      : model.descriptionEn,
    pricingContext: language === "zh-CN"
      ? model.pricingContext
      : model.pricingContextEn,
    // ... other fields
  }
}

Multi-language this thing, complex as it is complex, simple as it is simple. Actually it’s just storing content in different languages and taking it out when needed.

Regional Differentiation

function getCityTierLabel(
  cityTier: CityTier,
  region: "cn-mainland" | "international",
  language: SupportedLanguage
): string {
  const city = benchmarkData.cityCoefficients.find(
    item => item.tier === cityTier
  )

  if (region === "cn-mainland") {
    return language === "zh-CN" ? city.label : city.labelEn
  }

  return language === "zh-CN"
    ? city.internationalLabel
    : city.internationalLabelEn
}

Regional differentiation, which is showing different labels for different regions. This isn’t hard either—just judge the region and language, then return the corresponding value.

Summary

AI cost-benefit assessment isn’t anything profound—the core is three accounts: enterprise labor cost, AI usage cost, efficiency improvement magnitude. Calculate these three accounts clearly, and ROI naturally comes out.

This is actually like many things in life—seemingly complex, but when broken down, it’s just that. It’s just that few people are willing to calm down and calculate.

But there’s a point easily overlooked here: the multiplier effect that multi-Agent architecture can bring. No matter how strong a single Agent is, it can only improve efficiency linearly. But multiple Agents working in parallel brings exponential capacity improvement. This is also the core reason HagiCode chose multi-Agent architecture.

One person’s power is limited, a group of people’s power is unlimited. This saying sounds a bit platitudinous, but applied to AI, it’s quite apt.

If you’re also thinking about AI cost issues, welcome to HagiCode Cost to experience our calculator. Or go directly to GitHub to see the source code—might give you some inspiration.

Or maybe not, I can’t guarantee that. Just giving it a try, after all roads are walked by people…

Writing here, suddenly remembered an old saying: “To do good work, one must first sharpen one’s tools.”

It’s just that sometimes, with sharp tools, whether you can use them well is another matter. AI is like a double-edged sword—used well it’s assistance, used poorly it’s a burden. The balance of this, you have to grasp yourself.

Alright, not saying these anymore. Hope this is useful to you.

References

开始使用 HagiCode

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