Intuitive Machines (LUNR) Total Liabilities (2021 - 2026)
Intuitive Machines (LUNR) posted Total Liabilities of $2.18 billion for Q2 2026, up 154.8% from $854.76 million a year earlier and up 6.2% from the prior quarter.
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Analysis
Intuitive Machines (LUNR) Total Liabilities (2021 - 2026) Analysis & Trends
At the end of FY2025, Intuitive Machines' Total Liabilities came in at $1.51 billion, up 10.9% from FY2024.
- Annual Total Liabilities has increased for three consecutive years, with a four-year compound annual growth rate of 41.0% (FY2021 to FY2025).
- In prior years, Intuitive Machines' Total Liabilities was $1.36 billion in FY2024 (+292.5%), $347.34 million in FY2023 (+194.6%), $117.9 million in FY2022 (-69.2%) and $382.76 million in FY2021.
- The Q2 2026 figure stands as the highest quarterly Total Liabilities in data going back to Q3 2021.
- On a year-over-year basis, Total Liabilities has increased in each of the last five quarters, with growth averaging 116.4% over the last eight quarters.
- The strongest year-over-year quarter for Total Liabilities in the past five years was Q4 2024, with growth of 292.5%; the weakest was Q4 2022, with a decline of 69.2%.
- According to Business Quant data, Total Liabilities for the three prior quarters was $2.05 billion (Q1 2026), $1.51 billion (Q4 2025) and $1.16 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | General Electric | 324.09 Bn | 278.09 Bn | 4.68 Bn | 109.81 Bn |
| 2 | Rtx | 250.22 Bn | 223.44 Bn | 5.13 Bn | 105.86 Bn |
| 3 | Boeing | 147.08 Bn | 53.78 Bn | 2.41 Bn | 159.76 Bn |
| 4 | Lockheed Martin | 117.51 Bn | 104.24 Bn | 2.45 Bn | 53.68 Bn |
| 5 | Howmet Aerospace | 90.46 Bn | 86.07 Bn | 951.00 Mn | 7.52 Bn |
| 6 | General Dynamics | 89.77 Bn | 76.88 Bn | 2.18 Bn | 33.34 Bn |
| 7 | Motorola Solutions | 73.88 Bn | 70.24 Bn | 1.68 Bn | 16.55 Bn |
| 8 | Northrop Grumman | 68.68 Bn | 57.92 Bn | 2.12 Bn | 32.88 Bn |
| 9 | Honeywell International | 66.86 Bn | 19.30 Bn | 3.65 Bn | 58.49 Bn |
| 10 | Intuitive Machines | 3.21 Bn | 2.60 Bn | 35.87 Mn | 2.18 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 2.18 Bn |
| Mar 31, 2026 | 2.05 Bn |
| Dec 31, 2025 | 1.51 Bn |
| Sep 30, 2025 | 1.16 Bn |
| Jun 30, 2025 | 854.76 Mn |
| Mar 31, 2025 | 635.24 Mn |
| Dec 31, 2024 | 1.36 Bn |
| Sep 30, 2024 | 720.15 Mn |
| Jun 30, 2024 | 369.20 Mn |
| Mar 31, 2024 | 658.98 Mn |
| Dec 31, 2023 | 347.34 Mn |
| Sep 30, 2023 | 445.72 Mn |
| Jun 30, 2023 | 769.78 Mn |
| Mar 31, 2023 | 983.03 Mn |
| Dec 31, 2022 | 117.90 Mn |
| Sep 30, 2022 | 402.51 Mn |
| Jun 30, 2022 | 389.34 Mn |
| Mar 31, 2022 | 386.74 Mn |
| Dec 31, 2021 | 382.76 Mn |
| Sep 30, 2021 | 296.79 Mn |
API Access
Intuitive Machines 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=LUNR&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "LUNR", "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=LUNR&period=max&api_key=YOUR_API_KEY");
const data = await res.json();