Intuitive Machines (LUNR) Total Non-Current Liabilities (2022 - 2026)
Intuitive Machines (LUNR) reported Total Non-Current Liabilities of $2.1 billion for Q2 2026, up 156.9% from $815.95 million a year earlier and up 5.8% from the prior quarter.
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Analysis
Intuitive Machines (LUNR) Total Non-Current Liabilities (2022 - 2026) Analysis & Trends
At the end of FY2025, Intuitive Machines posted Total Non-Current Liabilities of $1.45 billion, up 12.1% from FY2024.
- Total Non-Current Liabilities has increased for three consecutive years, with a three-year compound annual growth rate of 130.9% (FY2022 to FY2025).
- By year, Total Non-Current Liabilities came in at $1.29 billion in FY2024 (+288.4%), $333.31 million in FY2023 (+182.7%) and $117.9 million in FY2022.
- The Q2 2026 figure ranks as the highest quarterly Total Non-Current Liabilities in data going back to Q4 2022.
- Year over year, Total Non-Current Liabilities has now increased in each of the last five quarters, with growth averaging 117.0% over the last eight quarters.
- The high point for year-over-year Total Non-Current Liabilities in five years was Q4 2024 (growth of 288.4%); the low point was Q2 2024 (a decline of 50.6%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $1.98 billion (Q1 2026), $1.45 billion (Q4 2025) and $1.12 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Non-Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | General Electric | 324.09 Bn | 278.09 Bn | 4.68 Bn | 102.53 Bn |
| 2 | Rtx | 250.22 Bn | 223.44 Bn | 5.13 Bn | 86.00 Mn |
| 3 | Boeing | 147.08 Bn | 53.78 Bn | 2.41 Bn | 157.29 Bn |
| 4 | Lockheed Martin | 117.51 Bn | 104.24 Bn | 2.45 Bn | 48.28 Bn |
| 5 | Howmet Aerospace | 90.46 Bn | 86.07 Bn | 951.00 Mn | 6.90 Bn |
| 6 | General Dynamics | 89.77 Bn | 76.88 Bn | 2.18 Bn | 14.57 Bn |
| 7 | Motorola Solutions | 73.88 Bn | 70.24 Bn | 1.68 Bn | 14.44 Bn |
| 8 | Northrop Grumman | 68.68 Bn | 57.92 Bn | 2.12 Bn | 30.72 Bn |
| 9 | Honeywell International | 66.86 Bn | 19.30 Bn | 3.65 Bn | 52.22 Bn |
| 10 | Intuitive Machines | 3.21 Bn | 2.60 Bn | 35.87 Mn | 2.10 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 2.10 Bn |
| Mar 31, 2026 | 1.98 Bn |
| Dec 31, 2025 | 1.45 Bn |
| Sep 30, 2025 | 1.12 Bn |
| Jun 30, 2025 | 815.95 Mn |
| Mar 31, 2025 | 609.46 Mn |
| Dec 31, 2024 | 1.29 Bn |
| Sep 30, 2024 | 670.36 Mn |
| Jun 30, 2024 | 353.09 Mn |
| Mar 31, 2024 | 620.67 Mn |
| Dec 31, 2023 | 333.31 Mn |
| Sep 30, 2023 | 426.50 Mn |
| Jun 30, 2023 | 714.48 Mn |
| Mar 31, 2023 | 879.63 Mn |
| Dec 31, 2022 | 117.90 Mn |
API Access
Intuitive Machines Total Non-Current 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-non-current-liabilities&ticker=LUNR&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-non-current-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-non-current-liabilities&ticker=LUNR&period=max&api_key=YOUR_API_KEY");
const data = await res.json();