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Claude Excel Plugin vs ChatGPT: Which AI Assistant Wins for Spreadsheet Work?

Cowrie
Cowrie
Dev @ Bswen

I’ve been paying for both ChatGPT Plus and Claude Pro for months. Last week, I finally decided to test them head-to-head on Excel work to see if I could drop one subscription.

The results surprised me.

The Problem

Every month, I spend hours building spreadsheets for quarterly reports. Sales tracking, profit margins, trend analysis—the usual Excel grind. I’d been using ChatGPT’s code interpreter for this, but after hearing about Claude’s native xlsx skill, I wondered: could Claude actually be better for spreadsheet work?

I’ve seen mixed reviews online. One Reddit user said Claude’s Excel capabilities made them cancel ChatGPT entirely. Another complained the plugin was “absolutely terrible.” So I spent a week running identical tasks through both to find the truth.

The Setup

I gave both AIs the same three tasks:

  1. Create a quarterly sales tracking spreadsheet with sample data
  2. Analyze a 50,000-row CSV of transaction data
  3. Generate visualizations showing revenue trends

Task 1: Creating Spreadsheets from Scratch

ChatGPT’s Approach

ChatGPT handled this through its code interpreter. I asked it to create a sales tracking sheet, and it wrote Python code that generated a CSV:

import pandas as pd


# ChatGPT generated this internally
data = {
    'Quarter': ['Q1 2025', 'Q1 2025', 'Q1 2025'],
    'Product': ['Widget A', 'Widget B', 'Widget C'],
    'Revenue': [125000, 98000, 156000],
    'Units_Sold': [1250, 980, 1560],
    'Profit_Margin': [0.22, 0.18, 0.25]
}


df = pd.DataFrame(data)
df.to_csv('sales_tracking.csv', index=False)

The result? A downloadable CSV file. But I needed to open it in Excel myself, format it, add formulas—extra steps that added up.

Claude’s Approach

Claude took a different path. Using its xlsx skill, it created an actual Excel file directly:

// Claude's xlsx skill generates .xlsx files directly
const response = await anthropic.messages.create({
  model: "claude-sonnet-4-20250514",
  max_tokens: 1000,
  messages: [{
    role: "user",
    content: "Create a quarterly sales tracking spreadsheet with sample data including columns: Quarter, Product, Revenue, Units Sold, Profit Margin"
  }]
});

The output was a real .xlsx file with formatted headers, conditional formatting for profit margins, and built-in formulas.

Winner: Claude—direct xlsx output saved me formatting time.

Task 2: Large Dataset Analysis

Here’s where things got interesting.

I uploaded a 50,000-row transaction CSV to both AIs and asked for:

  • Revenue breakdown by region
  • Top 10 products by profit margin
  • Seasonal trend identification

ChatGPT’s Performance

ChatGPT’s code interpreter loaded the data and ran pandas operations. It worked, but I hit the token limit mid-analysis. The 128K context window meant I had to break the analysis into chunks.

+------------------+
|   ChatGPT Flow   |
+------------------+
        |
        v
+------------------+
| Upload CSV       |
+------------------+
        |
        v
+------------------+
| Load into pandas |
+------------------+
        |
        v
+------------------+
| Hit token limit  |  <-- Had to restart here
+------------------+
        |
        v
+------------------+
| Continue in new  |
| conversation     |
+------------------+

I lost context between conversations. Claude remembered my preferences from earlier messages.

Claude’s Performance

Claude’s 200K+ context window handled the entire dataset in one go. I uploaded the CSV using its code execution tool:

const fileObject = await anthropic.beta.files.create({
  file: createReadStream("transactions_50k.csv")
});


const response = await anthropic.beta.messages.create({
  model: "claude-opus-4-6",
  betas: ["files-api-2025-04-14"],
  max_tokens: 4096,
  messages: [{
    role: "user",
    content: [
      { type: "text", text: "Analyze this transaction data: create summary by region, identify top products, and flag seasonal trends" },
      { type: "container_upload", file_id: fileObject.id }
    ]
  }],
  tools: [{
    type: "code_execution_20250825",
    name: "code_execution"
  }]
});

The analysis was more thorough because Claude could “see” the entire dataset at once.

Winner: Claude—larger context window made a real difference for big spreadsheets.

Task 3: Visualization

ChatGPT’s Charts

ChatGPT excelled here. Its built-in chart generation produced clean, presentation-ready visualizations:

  • Bar charts with proper axis labels
  • Pie charts for regional breakdowns
  • Time series with trend lines

I could download these as PNG files directly.

Claude’s Visualization

Claude’s approach was more technical. It generated visualizations through code execution:

# Claude executed this internally
import matplotlib.pyplot as plt


regions = ['North', 'South', 'East', 'West']
revenue = [450000, 380000, 520000, 410000]


plt.bar(regions, revenue)
plt.title('Revenue by Region')
plt.xlabel('Region')
plt.ylabel('Revenue ($)')
plt.savefig('revenue_by_region.png')

The charts were functional but required more iteration to match ChatGPT’s polish.

Winner: ChatGPT—better out-of-the-box visualizations.

The Trade-offs I Discovered

After a week of testing, here’s what I learned:

When Claude Wins

ScenarioWhy Claude is Better
Creating new spreadsheetsDirect xlsx output, no CSV conversion needed
Large dataset analysis200K+ context window handles more data
Multi-step workflowsRemembers context across longer conversations
API integrationNative xlsx skill is cleaner than code interpreter

When ChatGPT Wins

ScenarioWhy ChatGPT is Better
Quick visualizationsBuilt-in chart generation is more polished
Broader ecosystemWorks with other OpenAI tools you might already use
Documentation/examplesMore community resources available
Simple analysisCode interpreter is mature and reliable

The “Terrible Reviews” Explained

Those mixed Reddit reviews make sense now. Claude’s Excel plugin isn’t bad—it’s just different:

  • Users expecting ChatGPT clone: Disappointed because Claude works differently
  • Users with simple needs: Overwhelmed by Claude’s technical approach
  • Users with complex workflows: Thrilled because Claude handles multi-step tasks better

One user’s “absolutely terrible” was another user’s reason to cancel ChatGPT.

My Decision

I kept Claude Pro and dropped ChatGPT Plus. Here’s why:

  1. My use case matches Claude’s strengths: I create complex spreadsheets, not just analyze data
  2. Context matters: I often need to iterate across multiple messages
  3. Direct xlsx output: Saves me 10-15 minutes per spreadsheet

But if your workflow is different—especially if you need quick visualizations—ChatGPT might be the better choice.

Quick Decision Guide

                    Need Excel Help?
                          |
            +-------------+-------------+
            |                           |
      Create new sheets?         Analyze existing?
            |                           |
     +------+------+           +--------+--------+
     |             |           |                 |
  Complex      Simple      Large data       Small data
     |             |           |                 |
  Claude       ChatGPT     Claude           ChatGPT
     |             |           |                 |
  + leads to    + leads to  + handles       + faster
  direct xlsx   quick viz   more context    iteration

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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