Thermo Fisher Scientific (TMO) Total Liabilities (2009 - 2026)
Thermo Fisher Scientific's Total Liabilities came in at $60.49 billion for Q2 2026, up 19.2% from $50.75 billion a year earlier but down 1.4% from the prior quarter.
Thermo Fisher Scientific (TMO) Total Liabilities (2009 - 2026) Analysis & Trends
At the end of FY2025, Thermo Fisher Scientific's Total Liabilities was $56.93 billion, up 19.2% from FY2024.
- Total Liabilities carries a five-year compound annual growth rate of 10.5% (FY2020 to FY2025).
- Going back by year, Total Liabilities was $47.77 billion in FY2024 (-8.1%), $52 billion in FY2023 (-2.1%), $53.12 billion in FY2022 (-2.1%) and $54.27 billion in FY2021 (+57.1%).
- The five-year range for quarterly Total Liabilities is $34.84 billion (Q3 2021) to $61.34 billion (Q1 2026).
- Year-over-year, Total Liabilities has increased for four consecutive quarters, with growth averaging 6.2% over the last eight quarters.
- The fastest year-over-year change in Total Liabilities over five years came in Q1 2022 (growth of 67.6%), and the weakest in Q4 2024 (a decline of 8.1%).
- Business Quant data shows TMO's Total Liabilities at $61.34 billion (Q1 2026), $56.93 billion (Q4 2025) and $52 billion (Q3 2025) in the three quarters before Q2 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 243.20 Bn | 222.35 Bn | 4.88 Bn | 60.49 Bn |
| 2 | Abbott Laboratories | 172.35 Bn | 143.43 Bn | 7.27 Bn | 57.45 Bn |
| 3 | Danaher | 154.53 Bn | 138.35 Bn | 3.61 Bn | 39.78 Bn |
| 4 | Intuitive Surgical | 150.04 Bn | 129.59 Bn | 1.96 Bn | 2.58 Bn |
| 5 | Medtronic | 113.25 Bn | 79.11 Bn | 6.34 Bn | 42.46 Bn |
| 6 | Stryker | 106.37 Bn | 92.49 Bn | 4.50 Bn | 23.94 Bn |
| 7 | Boston Scientific | 61.94 Bn | 56.95 Bn | 3.85 Bn | 20.04 Bn |
| 8 | Becton Dickinson | 50.05 Bn | 47.20 Bn | 2.32 Bn | 26.32 Bn |
| 9 | Edwards Lifesciences | 49.13 Bn | 33.24 Bn | 1.35 Bn | 3.14 Bn |
| 10 | Agilent Technologies | 48.07 Bn | 40.96 Bn | 1.04 Bn | 6.60 Bn |
Historic Data
| Date | Value |
|---|---|
| Jun 27, 2026 | 60.49 Bn |
| Mar 28, 2026 | 61.34 Bn |
| Dec 31, 2025 | 56.93 Bn |
| Sep 27, 2025 | 52.00 Bn |
| Jun 28, 2025 | 50.75 Bn |
| Mar 29, 2025 | 49.68 Bn |
| Dec 31, 2024 | 47.77 Bn |
| Sep 28, 2024 | 51.39 Bn |
| Jun 29, 2024 | 51.08 Bn |
| Mar 30, 2024 | 51.59 Bn |
| Dec 31, 2023 | 52.00 Bn |
| Sep 30, 2023 | 51.71 Bn |
| Jul 1, 2023 | 50.30 Bn |
| Apr 1, 2023 | 52.35 Bn |
| Dec 31, 2022 | 53.12 Bn |
| Oct 1, 2022 | 46.93 Bn |
| Jul 2, 2022 | 48.23 Bn |
| Apr 2, 2022 | 51.73 Bn |
| Dec 31, 2021 | 54.27 Bn |
| Oct 2, 2021 | 34.84 Bn |
Thermo Fisher Scientific Total Liabilities 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=total-liabilities&ticker=TMO&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "TMO", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()
const res = await fetch("https://data.businessquant.com/historic?slug=total-liabilities&ticker=TMO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();