Abbott Laboratories (ABT) Operating Expenses (2009 - 2026)
Abbott Laboratories' Operating Expenses was $10.9 billion in Q2 2026, up 19.9% from $9.09 billion a year earlier and up 11.0% from the prior quarter.
Abbott Laboratories (ABT) Operating Expenses (2009 - 2026) Analysis & Trends
On a trailing twelve-month basis, Abbott Laboratories' Operating Expenses was $39.24 billion through Jun 30, 2026, up 10.2% year-over-year; for FY2025, it was $36.28 billion, up 3.3% from FY2024.
- Operating Expenses shows a five-year compound annual growth rate of 4.4% (FY2020 to FY2025).
- In earlier years, Operating Expenses was $35.13 billion in FY2024 (+4.4%), $33.63 billion in FY2023 (-4.7%), $35.29 billion in FY2022 (+1.8%) and $34.65 billion in FY2021 (+18.5%).
- The Q2 2026 figure marks the highest quarterly Operating Expenses in data going back to Q2 2009.
- Compared with a year earlier, Operating Expenses has increased for ten straight quarters, with growth averaging 7.1% over the last eight quarters.
- The best year-over-year quarter for Operating Expenses over five years was Q2 2026 (growth of 19.9%); the worst was Q1 2023 (a decline of 8.3%).
- Per Business Quant data, ABT's Operating Expenses in the three quarters before Q2 2026 was $9.82 billion (Q1 2026), $9.21 billion (Q4 2025) and $9.31 billion (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 250.84 Bn | 229.99 Bn | 4.88 Bn | 9.91 Bn |
| 2 | Abbott Laboratories | 174.73 Bn | 145.82 Bn | 7.27 Bn | 10.90 Bn |
| 3 | Danaher | 159.85 Bn | 143.67 Bn | 3.61 Bn | 2.48 Bn |
| 4 | Intuitive Surgical | 146.79 Bn | 126.35 Bn | 1.96 Bn | 988.50 Mn |
| 5 | Medtronic | 114.54 Bn | 80.41 Bn | 6.34 Bn | 4.06 Bn |
| 6 | Stryker | 105.67 Bn | 91.79 Bn | 4.50 Bn | 2.84 Bn |
| 7 | Boston Scientific | 63.93 Bn | 58.94 Bn | 3.85 Bn | 2.67 Bn |
| 8 | Becton Dickinson | 50.12 Bn | 47.27 Bn | 2.32 Bn | 4.32 Bn |
| 9 | Edwards Lifesciences | 50.06 Bn | 34.17 Bn | 1.35 Bn | 840.10 Mn |
| 10 | Agilent Technologies | 49.40 Bn | 42.29 Bn | 1.04 Bn | 1.43 Bn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 10.90 Bn |
| Mar 31, 2026 | 9.82 Bn |
| Dec 31, 2025 | 9.21 Bn |
| Sep 30, 2025 | 9.31 Bn |
| Jun 30, 2025 | 9.09 Bn |
| Mar 31, 2025 | 8.67 Bn |
| Dec 31, 2024 | 9.06 Bn |
| Sep 30, 2024 | 8.78 Bn |
| Jun 30, 2024 | 8.71 Bn |
| Mar 31, 2024 | 8.58 Bn |
| Dec 31, 2023 | 8.46 Bn |
| Sep 30, 2023 | 8.50 Bn |
| Jun 30, 2023 | 8.44 Bn |
| Mar 31, 2023 | 8.24 Bn |
| Dec 31, 2022 | 8.79 Bn |
| Sep 30, 2022 | 8.64 Bn |
| Jun 30, 2022 | 8.88 Bn |
| Mar 31, 2022 | 8.98 Bn |
| Dec 31, 2021 | 9.09 Bn |
| Sep 30, 2021 | 8.38 Bn |
Abbott Laboratories Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=ABT&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ABT", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=ABT&period=max&api_key=YOUR_API_KEY");
const data = await res.json();