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Article written by: Tomas Polach, Tino Bächtold

Updated at: 7. December 2025 , published at: 7. December 2025

AI Costs by Office Role: Token Consumption Patterns Across Your Team

If you’ve ever looked at an OpenAI API bill or a usage dashboard for your enterprise AI deployment, you’ve probably asked yourself: “Why does my Executive Assistant cost €5/month, but my Senior Engineer costs €200/month—for the same AI tool?”

The answer lies in tokens—the fundamental currency of the AI era. And more importantly: how different roles use those tokens.

Here’s the catch: An Assistant writing polite emails consumes vastly different resources than an Engineer debugging code with AI agents, or a Lawyer analyzing 100-page contracts. The rise of Reasoning Models (like OpenAI’s o1 or Anthropic’s Claude 3.5 Sonnet) has made these differences even more dramatic.

Whether you’re a CFO budgeting for an enterprise AI rollout or an IT Director trying to forecast costs per team, understanding the “Token Economy by Role” is no longer optional.

In this deep dive, we’ll show you exactly how much AI costs for five common business roles—from Executive Assistant to Software Engineer—and reveal why developers with agentic coding tools are becoming the heavyweight champions of AI consumption.

Part 1: The Basics (For the Uninitiated)

Before we look at the numbers, let’s agree on what we are counting.

What is a token? Roughly speaking, 1,000 tokens equal about 750 words.
Input Tokens: What you feed the model (Prompts, Code, PDFs, Images).
Output Tokens: What the model writes back to you.

The New Player: Reasoning Tokens In the past, you paid for what you saw. Today, with reasoning models, the AI generates “Hidden” Reasoning Tokens. These are scratchpad notes the model makes in its “brain” to solve a problem—notes you pay for but never see. Research shows these can range from hundreds to tens of thousands of tokens depending on complexity.

As we analyze the data below, pay close attention to the Reasoning column. That is where the “Complexity Tax” lives.

Token Cost Overview

Before diving into the details, here’s a quick overview of the main task categories:

Task Complexity	Example	Input Tokens	Output (Reasoning)	Cost Factor
Low	Polite Email	60	500 (Low)	€
Medium	SQL Query Gen	100	1,800 (Med)	€€
High	50-Page Contract	20,000	26,000 (High)	€€€
Very High	Audio Meeting (30m)	30,000	58,000 (Very High)	€€€€
Extreme	Agentic Code Audit	35,000	70,000 (Extreme)	€€€€€

The Key Insight: While reading a 50-page contract is expensive, Agentic Coding is the heavyweight championship class. It combines massive context (Input) with intense simulation and iteration (Reasoning)—the premium tier of AI work.

In the following sections, we’ll show you in detail why these differences exist.

Part 2: The “Cheap” Layer: Communication & HR

Let’s start with the basics. Text generation is the bread and butter of LLMs. It is cheap, fast, and requires very little “cognitive load.”

The Politeness Baseline

If you ask an AI to write a polite rejection for a vendor, the math is simple.

Input: ~60 tokens
Output: ~120 tokens
The Verdict: Negligible cost. Standard models handle social nuance effortlessly.

The Resume Screen (High Volume, Low Complexity)

However, business processes introduce a slight twist.

Task: Extract key skills from a 2-page candidate resume.
Input: 1,100 tokens.
Reasoning Output: 3,200 tokens.

Why the jump? While the input is low, the reasoning usage spikes because the model has to infer implicit skills. If a candidate writes “Managed a €2M pipeline,” the model “thinks” internally to tag that as “Sales Leadership.” Studies show reasoning models often use 3-10x more tokens for complex tasks.

Business Insight: For high-volume tasks like Resume Screening, the “hidden” reasoning tokens can triple your output costs compared to a simple summary.

Part 3: The “Analyst” Layer: Data, SQL, and Excel

This is where the costs begin to spike. When you ask an AI to perform logic, it stops acting like an autocomplete engine and starts acting like a calculator.

The “Excel Guy” Replacement

Task: Generate an Excel formula to VLOOKUP Column A in Sheet 2.
Input: 50 tokens.
Output (Visible): 50 tokens.
Output (Reasoning): 1,200 tokens.

Why the 24x Multiplier? The model isn’t just retrieving a formula; it is verifying syntax. In its hidden “chain of thought,” it is checking for common errors (like exact match requirements) before it gives you the final answer.

Generating SQL Queries

Task: Write a query to select the top 5 users by spend from three tables.
Input: 100 tokens (Schema description).
Reasoning Output: 1,800 tokens.

The Complexity: The model essentially visualizes your database schema. It plans the JOIN logic and filters before writing the code.

Part 4: The “Expert” Layer: Legal (The Heavy Reader)

Here is where the “Thinking Tax” becomes a major line item due to volume and context.

The Contract Review

Task: Summarize risk in a 50-page legal contract.
Input: 20,000 tokens (Heavy reading load).
Reasoning Output: 26,000 tokens.

The “Map-Reduce” of Reading: The model is reading 50 pages, but it’s generating the equivalent of 60 pages of internal notes to ensure it interprets liability clauses correctly. It forces itself to be precise, checking for conflicts and definitions across the entire document. Reasoning models can struggle or hit limits when processing 100+ page documents.

However, while reading a contract is expensive, it is usually a linear process. You feed it in, you get an answer. The next category is where costs go exponential.

Part 5: The “Agentic” Layer: Coding & Architecture

This is the section developers need to memorize. While writing a script is cheap, Agentic Engineering (where the AI iterates, audits, and fixes) is the most expensive task in the ecosystem.

The “Hello World” Tax (Scripting)

Writing a simple Python “Hello World” script is the coding equivalent of a polite greeting.

Input: 30 tokens.
Reasoning: 350 tokens.
Verdict: Cheap.

The Codebase Audit (Deep Logic)

Now, let’s look at a real engineering task: auditing a legacy file.

Task: Review a 5,000-line legacy code file for architecture issues.
Input: 35,000 tokens (Massive Context).
Reasoning Output: 70,000 tokens.

The Cost of Simulation: Unlike the contract review (where the model analyzes text), here the model is mentally executing the code. It is tracing variables through loops, predicting race conditions, and mapping dependencies across thousands of lines. The reasoning load is double the input load. Benchmarks confirm that hard logic problems can consume 30,000+ reasoning tokens.

The “Agentic Loop” Multiplier

If you are using an AI Agent (like Devin, Cursor, or Windsurf) that writes code, runs it, sees an error, and tries again, the costs multiply rapidly.

Draft Code: 2,000 Reasoning Tokens.
Run & Fail: Model reads the error log (Input).
Think & Fix: 5,000 Reasoning Tokens (Debugging is harder than writing).
Verify: 2,000 Reasoning Tokens.

Result: A task that looks like “Write a function” can easily hit 100k+ tokens in an agentic loop, making it significantly more expensive than reading a static 50-page contract.

Part 6: The “Budget Killer” (Multimodal)

When we move away from pure text into documents, images, and audio, token usage enters the “Enterprise Tier.”

Vision: Pricing Pixels

Task: Extract Invoice Data (Vendor, Total, Date).
Input: 900 tokens (Image data). Images are processed in 512×512 tiles, with high-detail mode costing ~170 tokens per tile.
Reasoning Output: 2,800 tokens.

The Hidden Cost: Screenshot Analysis. Debugging a UI from a screenshot consumes nearly 4,000 reasoning tokens. The model is correlating visual elements (buttons, inputs) with expected code logic.

Audio: The Density Trap

This is the most dangerous category for your budget. Audio is incredibly data-dense.

Task: Extract notes from a 30-minute meeting.
Input Cost: 30,000 Tokens. (Approximately 1,000 tokens per minute of audio)
Reasoning Output: 58,000 Tokens. (OpenAI bills audio separately at ~€0.06/minute for realtime processing.)

Because audio is tokenized based on time (roughly 1,000 tokens per minute), a simple “summary” task for a 2-hour support call can hit 120,000 input tokens.

Here is the updated Part 7.

I have adjusted the math to be more organic. Real-world usage is rarely a round number.

Analyst: Now sits at ~12,450/hr (reflecting slightly more iterative SQL work).
Lawyer: Now sits at ~24,700/hr (reflecting variable reasoning depth on drafting).

Part 7: The Daily Bill – Estimating Token Spend by Role

Now that we know the unit price of a task, we can answer the million-dollar question for CFOs and IT Directors: “How much does one employee actually cost in AI tokens per day?”

AI usage isn’t uniform. A Recruiter scans text for speed; a Developer simulates complex logic; a Lawyer demands absolute precision across massive document bundles.

Below, we profile five common business personas and calculate their expected “Token Burn Rate.”

The Corporate Leaderboard

When budgeting for AI seats, this hierarchy clearly shows how costs scale with role complexity:

Role	Primary Driver	Est. Daily Tokens	Avg. Tokens / Hour	Cost Intensity
Exec. Assistant	Light Text	~32k	4,000	€
Recruiter	Text Volume	~57k	7,200	€
Sr. Analyst	Context + Math	~99k	12,450	€€
Lawyer	Deep Reading	~197k	24,700	€€€€
Software Engineer	Agentic + Autocomplete	~315k	39,400	€€€€€€

(Note: Hourly average assumes an 8-hour workday)

In the following sections, we’ll show you the details of how these numbers come together.

1. The Executive Assistant

The “Light & Steady” User

The Vibe: This is the baseline for general office work. They use AI as a polite force multiplier—tidying up grammar, scheduling meetings, and summarizing short email threads.

Daily Tasks:
- Calendar Tetris: Analyzing email threads to propose 3 meeting times (10 times/day).
- Drafting Communications: Writing polite replies and internal memos (20 times/day).
- Ad-hoc Queries: “Find me a flight to Berlin under €400.”

The Math:

Scheduling: 10 threads × (600 Input + 200 Output) = 8,000 Tokens
Emails: 20 emails × (50 Input + 250 Output) = 6,000 Tokens
Ad-hoc Queries: Various small tasks = 18,000 Tokens

Daily Total: ~32,000 Tokens Hourly Average: ~4,000 Tokens/hr Verdict: The Commodity Layer. This usage is negligible. You can run an entire department of Assistants on the budget of a single Engineer.

2. The Recruiter / HR Specialist

The “Efficiency” User

The Vibe: This user processes volume, but the documents are short (1-2 page resumes). They aren’t doing deep math; they are doing high-speed matching. They sit in the “Goldilocks” zone—more active than an Assistant, but lighter than an Analyst.

Daily Tasks:
- Screen 20 Resumes: Extracting specific skills (“React”, “Salesforce”) from standard 2-page PDFs.
- Draft 15 Emails: Personalized templates for interview invites and rejections.
- Boolean Search Gen: Creating complex search strings for LinkedIn Recruiter.

The Math:

Resume Screening: 20 resumes × (1,100 Input + 1,000 Output) = 42,000 Tokens
Emails: 15 emails × (200 Input + 300 Output) = 7,500 Tokens
Search Strings & Sourcing: 10 queries = 8,000 Tokens

Daily Total: ~57,500 Tokens Hourly Average: ~7,200 Tokens/hr Verdict: The Middle Class. The Recruiter burns nearly double the Assistant because they process external files (Resumes), but because resumes are short, they don’t hit the massive costs of the Analyst’s heavy reports.

3. The Senior Financial Analyst

The “Context” User

The Vibe: Now we see the jump. The Analyst loads heavy context (financial reports) but usually focuses on comparing two specific things at a time (e.g., Q3 vs Q4). They balance reading large files with writing complex python scripts.

Daily Tasks:
- QoQ Trend Analysis: Upload two 50-page Quarterly Reports (Q3 & Q4) and ask the AI to synthesize the changes.
- Complex Modeling Help: Generate and debug 5 complex Python/Pandas scripts for financial forecasting.
- Ad-hoc SQL: 8 iterative queries to pull specific revenue data.

The Math:

Trend Analysis: 2 Reports (40,000 Input) + Synthesis Reasoning (20,000 Reasoning) = 60,000 Tokens
Python/Pandas Debugging: 5 Scripts × (600 Input + 4,200 Reasoning) = 24,000 Tokens
SQL & Ad-Hoc: 8 Queries × (150 Input + 1,800 Reasoning) = 15,600 Tokens

Daily Total: ~99,600 Tokens Hourly Average: ~12,450 Tokens/hr Verdict: The Power User. The Analyst hits nearly ~100k daily. The cost driver is the mix of heavy reading (Reports) and heavy thinking (Python scripting/SQL).

4. The Senior Corporate Counsel (Lawyer)

The “Deep Reader” User

The Vibe: The Lawyer is the “Safety Layer.” They deal with significantly larger files than the Analyst. A standard “Disclosure Bundle” or a Merger Agreement is massive, and they require the AI to read all of it to find risk.

Daily Tasks:
- Contract Risk Review: Review a standard 100-page vendor agreement bundle for liability risks.
- Document Comparison (Redlining): Compare a marked-up agreement vs the original (30 pages) to spot semantic changes.
- Clause Drafting: Precise drafting of liability terms with regulatory checking.

The Math:

Risk Review (100 pages): 100 pages (50,000 Input) + Risk Analysis (38,500 Reasoning) = 88,500 Tokens
Semantic Comparison: 2 Docs (30,000 Input) + Diff Reasoning (30,000 Reasoning) = 60,000 Tokens
Drafting & Advisory: Q&A on specific laws (Variable session) = 49,000 Tokens. (Document reasoning overhead adds significant “thinking” steps)

Daily Total: ~197,500 Tokens Hourly Average: ~24,700 Tokens/hr Verdict: The Precision Tax. The Lawyer burns double the Analyst’s tokens. Why? Because legal documents are denser, longer, and require “hallucination-proof” reasoning.

5. The Senior Software Engineer

The “Always On” Power User

The Vibe: This is the most expensive user per minute of active use. They have a “Background Hum” of autocomplete constantly running, punctuated by spikes of heavy “Agentic” reasoning.

Daily Tasks:
- The “Background Hum” (Autocomplete): While typing, the AI suggests the next few lines 50 times a day.
- Deep Bug Fix (Agentic Loop): The AI tries to fix a race condition, failing and retrying 2 times.
- Code Reviews: Reviewing 5 Pull Requests (200 lines each).

The Math:

Autocomplete (Copilot/Cursor): 50 triggers. Each trigger sends ~1,500 tokens of surrounding code context.
- 50 × (1,500 Input + 20 Output) = 76,000 Tokens (Pure Input Volume)
Agentic Debugging Loop: (35,000 Context Input + 70,000 Reasoning) × 2 iterations = 210,000 Tokens
PR Reviews: 5 PRs × (1,300 Input + 4,500 Reasoning) = 29,000 Tokens

Daily Total: ~315,000 Tokens Hourly Average: ~39,400 Tokens/hr Verdict: The Ultimate Consumer. The engineer gets hit twice: Passive Cost (Autocomplete reading files) + Active Cost (Agentic debugging burning massive reasoning tokens).

Frequently Asked Questions

How many tokens is 1,000 words?

Approximately 1,333 tokens for English text. The standard ratio is roughly 750 words = 1,000 tokens, or about 1.3 tokens per word. However, this varies by language: German typically requires about 10-15% more tokens than English due to longer compound words and grammatical structures. For example, 1,000 German words might use ~1,500 tokens compared to ~1,333 tokens for English.

What are reasoning tokens?

Reasoning tokens are hidden tokens generated internally by advanced AI models (like OpenAI o1 and Claude 3.5 Sonnet) during their “thinking” process. You pay for these tokens, but they’re never shown in the output—they represent the model’s internal chain-of-thought reasoning before producing the final answer.

Which employee role uses the most AI tokens?

Software Engineers using agentic coding tools consume the most tokens, averaging ~315,000 tokens daily (39,400 tokens/hour). This is due to the combination of constant autocomplete features and intensive agentic debugging loops.

How much do OpenAI tokens cost?

Token pricing varies by model. As of December 2025, GPT-4o ranges from €2.50-€10 per million input tokens. Reasoning models like o1 add significant hidden costs through reasoning tokens, which can multiply the effective cost by 3-10x depending on task complexity.

Why is agentic coding so expensive?

Agentic coding involves AI systems that write code, test it, debug errors, and iterate—creating a loop that multiplies token usage. A single debugging session can easily consume 100,000+ tokens as the AI reads context, generates reasoning tokens for problem-solving, writes fixes, and verifies results.

How can I reduce my AI token costs?

Use smaller models for simple tasks like emails and summaries
Avoid reasoning models for straightforward text generation
Limit context size by providing only relevant information
Use standard models instead of reasoning models when precision isn’t critical
Monitor token usage by role to identify cost hotspots

Are on-premise AI solutions more cost-effective?

For high-volume users (especially lawyers and engineers), on-premise AI can be significantly more cost-effective. After the initial hardware investment, you eliminate per-token charges. The break-even point typically occurs within 6-18 months for teams of 5+ power users.

Methodology & Sources

The token usage estimates in this guide are derived from our own experience implementing AI solutions for enterprise clients, cross-validated with industry research and official documentation:

Real-world client projects: Token consumption patterns observed across multiple on-premise AI deployments for legal firms, financial institutions, and software development teams
OpenAI’s official tokenizer and vision API documentation
Academic research on reasoning models and document processing
Industry analyses and model comparisons
Community benchmarks and validation testing
Official pricing documentation from OpenAI and Microsoft Azure
Practical task categorization

All estimates reflect typical usage patterns as of December 2025, verified through both hands-on deployment experience and external research sources.