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 | 311.41 Bn | 269.93 Bn | 5.02 Bn | 95.58 Bn |
| 2 | Rtx | 270.60 Bn | 238.87 Bn | 5.81 Bn | 82.00 Mn |
| 3 | Lockheed Martin | 147.27 Bn | 131.51 Bn | 2.44 Bn | 48.98 Bn |
| 4 | Boeing | 135.84 Bn | 44.80 Bn | 2.17 Bn | 161.64 Bn |
| 5 | General Dynamics | 100.63 Bn | 87.75 Bn | 2.18 Bn | 14.57 Bn |
| 6 | Motorola Solutions | 81.53 Bn | 77.88 Bn | 1.68 Bn | 14.44 Bn |
| 7 | Northrop Grumman | 79.44 Bn | 66.89 Bn | 2.35 Bn | 31.46 Bn |
| 8 | Howmet Aerospace | 79.38 Bn | 75.14 Bn | 1.00 Bn | 6.79 Bn |
| 9 | Honeywell International | 69.24 Bn | 69.24 Bn | - | - |
| 10 | Intuitive Machines | 1.73 Bn | -77.30 Mn | 35.87 Mn | 2.06 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 2.06 Bn |
| Mar 31, 2026 | 1.93 Bn |
| Dec 31, 2025 | 1.45 Bn |
| Sep 30, 2025 | 1.12 Bn |
| Jun 30, 2025 | 815.71 Mn |
| Mar 31, 2025 | 609.20 Mn |
| Dec 31, 2024 | 1.29 Bn |
| Sep 30, 2024 | 622.40 Mn |
| Jun 30, 2024 | 338.42 Mn |
| Mar 31, 2024 | 584.03 Mn |
| Dec 31, 2023 | 322.01 Mn |
| Sep 30, 2023 | 410.03 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();