Quantifying AI Cost-Benefit Analysis

Your boss asks: "How much does it cost to equip employees with AI assistants, and is it worth it?" You can't answer, and you feel unsure. This article discusses how to calculate this clearly.

Background

In recent years, Claude Code, GitHub Copilot, and various AI programming assistants have flooded in like a tidal wave. As a technical person, you've probably already started using them and feel genuinely more efficient—like someone handing you a ladder when you need to climb.

But when it comes to discussing ROI with your boss or clients, you often hit a wall—how do you quantify that subjective feeling of "increased efficiency"? I understand this feeling. It's like when someone asks you "what do you like about her?" and you stutter for a while, only saying "I just do." That's fine, but bosses want numbers, not your feelings.

And that's not the only problem:

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

Efficiency Quantification: How do we translate "productivity gains" across different roles and usage levels into measurable metrics?

Risk Assessment: If competitors大规模 adopt AI, how much will our competitiveness suffer?

Traditional ROI calculations often overlook two critical factors:

1. Enterprise Total Cost Perspective: Only considering salary while ignoring city differences, social insurance, housing fund, and other additional costs
2. Token Economics Model: Lack of a calculation framework connecting AI usage (Tokens) to actual output

Both factors are indispensable. Here's a real example: For the same 300k annual salary, the actual cost to the enterprise in Beijing versus Wuhan can differ by over 30%. And that's not even counting the cost of AI usage itself. Cost is like an iceberg—you only ever see the tip...

About HagiCode

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

HagiCode is essentially just an AI code assistant project. However, during development, we genuinely needed to accurately assess the cost-effectiveness of different AI models—after all, money doesn't grow on trees. To that 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 approach might give you some reference. Or maybe not—I can't guarantee that, but we're 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 proportion analysis
├── ROI calculation
└── Equivalent workforce conversion

Output Layer
├── AI cost proportion
├── Efficiency gain
├── Return on investment
├── Equivalent workforce count
└── Elimination risk assessment

This framework looks complex enough to make your head spin. Actually, the core logic is quite simple: calculate the enterprise's real labor costs clearly, calculate the AI's annual costs clearly, then look at the ROI and equivalent workforce. After all, simplifying complexity is the right path.

Calculating Key Metrics

Enterprise Annual Total Labor Cost

First, enterprise total cost—this isn't simply annual salary multiplied by 12 months. Real costs need to consider two factors:

City Coefficient: 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 cost-of-living premium for first-tier cities—after all, the price of living in Beijing versus Wuhan is indeed different.

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

So the formula is:

enterpriseAnnualTotalLaborCost = annualSalary × (1 + cityCoefficient) + annualSalary/12

City coefficient 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 slightly more complex because AI models charge by Token. And input and output prices differ—output is typically 5-10x more expensive than input. This isn't surprising, after all, output is the AI "working," while 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)
compositeUnitPrice = (3 × inputPrice + outputPrice) / 4

// Daily cost
dailyAICost = dailyTokenUsage(M) × compositeUnitPrice

// Annual cost (based on 264 working days)
annualAICost = dailyAICost × 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:

compositeUnitPrice = (3 × 2.5 + 15) / 4 = 5.625 USD/1M Token

Converting to RMB (assuming 1 USD = 7.25 CNY):

compositeUnitPrice = 5.625 × 7.25 = 40.78 yuan/1M Token

Exchange rates fluctuate, but we fix them for calculation convenience.

Core Benefit Metrics

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

// AI cost proportion
aiCostProportion = annualAICost / enterpriseAnnualTotalLaborCost

// Efficiency gain
efficiencyGain = efficiencyMultiplier - 1

// AI return on investment
aiROI = efficiencyGain / aiCostProportion

// Affordable workflow count
affordableCount = enterpriseAnnualTotalLaborCost / annualAICost

// Equivalent workforce
equivalentWorkforce = 1 + (efficiencyMultiplier - 1) × min(affordableCount, 1)

Meanings of these metrics:

AI Cost Proportion: The percentage of enterprise labor costs consumed to maintain Agent workflows. The lower this number, the more "cost-effective" the AI usage. Who doesn't like saving money?

Return on Investment: Efficiency gain ÷ AI cost proportion. Less than 1 means "somewhat wasteful," greater than 2 means "very worthwhile." This is easy to understand—like spending money to buy time, you calculate whether it's worth it.

Equivalent Workforce: There's a point easily misunderstood here. It's not directly accepting the efficiency multiplier, but whether the enterprise can afford this AI workflow. If affordableCount is less than 1, then equivalent workforce won't reach your expected efficiency multiplier. After all, even the cleverest housewife can't cook without rice...

Practical Calculation Example

Let's do a real accounting example. Assume a first-tier city backend developer:

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

Step 1: Calculate enterprise total cost

enterpriseTotalCost = 30 × (1 + 0.4) + 30/12 = 44.5k

Step 2: Calculate AI annual cost

compositeUnitPrice = 40.78 yuan/1M Token
dailyCost = 12 × 40.78 = 489.36 yuan
annualCost = 489.36 × 264 = 129,191 yuan ≈ 12.9k

Step 3: Calculate benefit metrics

AI cost proportion = 12.9 / 44.5 = 29%
efficiencyGain = 2.5 - 1 = 150%
return on investment = 1.5 / 0.29 = 5.17x

Step 4: Calculate equivalent workforce

affordableCount = 44.5 / 12.9 = 3.45
equivalentWorkforce = 1 + (2.5 - 1) × 1 = 2.5 people

What's the conclusion? This AI usage has an ROI over 5, falling in the "very worthwhile" range. If the entire team uses it, forming approximately 2.5 people's production capacity advantage, it would be very competitive in the market.

This makes sense—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: a single Agent's efficiency gains have an upper limit.

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

Traditional single Agent usage patterns have several bottlenecks:

Serial Limitation: Proposal → Implementation → Review → Fix must wait sequentially. No matter how fast a single Agent is, it can only do one thing at a time. It's like cooking—you can only wash, cut, and stir-fry step by step.

Quota Waste: Monthly quota limits can't be fully utilized. Unused quota this month doesn't roll over to next month. This isn't surprising, just a bit wasteful.

Context Switching: Different tasks require repeatedly establishing context, meaning you have to explain background information each time. Like chatting with different people about the same thing—starting from scratch each time gets tiring.

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

• Parallel 10x+: Multiple Agents drive multiple instances simultaneously, achieving true parallel work
• Throughput Increase: Proposal, implementation, and fixes can advance in parallel without waiting for each other
• Improved Token Utilization: OpenSpec process reduces rework, spreading equivalent consumption

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

• Parallel sessions: 4
• Token utilization improvement: 1.5x

Amplified calculation:

amplifiedEfficiency = 2.5 × 4 = 10x
optimizedDailyToken = (12 × 4) / 1.5 = 32 M
optimizedAnnualCost = 32 × 40.78 × 264 = 344k

New benefit metrics:

newAICostProportion = 34.4 / 44.5 = 77%
newROI = 9 / 0.77 = 11.68x
newEquivalentWorkforce = 1 + (10 - 1) × 1 = 10 people

Although AI cost proportion rose from 29% to 77%, ROI increased from 5.17x to 11.68x, and equivalent workforce changed from 2.5 to 10 people.

This is the power of multi-Agent parallelism. One Agent is one person; ten Agents are a team... The difference isn't just a little bit.

Practical Considerations

Don't Get City Coefficient Wrong

Employment cost differences across 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. A small difference in this number can significantly skew the final result. After all, "a miss is as good as a mile"... This is an old saying, but it still holds true.

Input-Output Ratio Isn't Fixed

Code scenarios default to a 3:1 input-output ratio, matching the proportion of prompts to generated code in actual programming. But if you're doing other types of work—like writing copy or doing data analysis—this ratio might be completely different.

This is normal—different work, different methods.

Efficiency Multiplier Is Subjective

Efficiency multiplier is a subjective estimate. It's recommended to combine with 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 initially—observe for a while before adjusting. After all, higher expectations lead to greater disappointment.

How to Calculate Token Usage

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

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

Or just use HagiCode Cost to calculate—it has reference values for common scenarios. This is convenient and saves you from blind trial and error.

Impact of Exchange Rate Fluctuations

USD models require exchange rate conversion, but rates fluctuate. Calculators typically use fixed rates (like 1 USD = 7.25 CNY), while actual costs may vary with exchange rate fluctuations. This error is usually small, but keep it in mind.

After all, everything has an approximation—precision to several decimal places isn't really necessary...

Technical Implementation Points

If you want to implement this calculation logic yourself, several technical details are 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
}

There's not much to say about this code—it's 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 support is complex in some ways, simple in others. It's essentially storing different language content and retrieving it 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 means displaying different labels for different regions. This isn't difficult—just judge the region and language, then return the corresponding value.

Summary

AI cost-benefit assessment isn't anything profound—the core is three calculations: enterprise labor costs, AI usage costs, and efficiency improvement magnitude. Calculate these three clearly, and the ROI naturally emerges.

This is like many things in life—seemingly complex, but when broken down, it's just that. Few people are willing to sit down and calculate it.

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

One person's power is limited; a group's power is infinite. This sounds like a platitude, but applied to AI, it's fitting.

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

Or maybe not—I can't guarantee that. Just giving it a try, after all, paths are made by walking...

Writing this, I suddenly remembered an old saying: "To do good work, one must first sharpen one's tools."

But sometimes, even with sharp tools, knowing how to use them is another matter. AI is like a double-edged sword—used well, it's assistance; used poorly, it's a burden. The balance is for you to find.

Enough of that. Hope this helps you.

References

• HagiCode Cost - AI Cost Assessment Tool
• HagiCode GitHub Repository
• HagiCode Official Site
• OpenAI Official Pricing
• Anthropic Claude Pricing