Intuitive Machines (LUNR) Operating Expenses (2021 - 2026)
Intuitive Machines (LUNR) reported Operating Expenses of $253.3 million for Q2 2026, up 220.8% from $78.95 million a year earlier and up 12.1% from the prior quarter.
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Analysis
Intuitive Machines (LUNR) Operating Expenses (2021 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Intuitive Machines' Operating Expenses came in at $624.97 million, up 114.2% year-over-year; for FY2025, it was $297.29 million, up 4.2% from FY2024.
- Operating Expenses has increased for three consecutive years, with a four-year compound annual growth rate of 396.0% (FY2021 to FY2025).
- By year, Operating Expenses came in at $285.4 million in FY2024 (+102.9%), $140.67 million in FY2023 (+49.9%), $93.83 million in FY2022 and $491,341 in FY2021.
- The Q2 2026 figure ranks as the highest quarterly Operating Expenses in data going back to Q3 2021.
- Year over year, Operating Expenses has now increased in each of the last three quarters, with growth averaging 78.2% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q2 2026 (growth of 220.8%); the low point was Q3 2025 (a decline of 6.0%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $225.93 million (Q1 2026), $77.88 million (Q4 2025) and $67.86 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | General Electric | 324.09 Bn | 278.09 Bn | 4.68 Bn | 10.86 Bn |
| 2 | Rtx | 250.22 Bn | 223.44 Bn | 5.13 Bn | 21.96 Bn |
| 3 | Boeing | 147.08 Bn | 53.78 Bn | 2.41 Bn | 2.35 Bn |
| 4 | Lockheed Martin | 117.51 Bn | 104.24 Bn | 2.45 Bn | - |
| 5 | Howmet Aerospace | 90.46 Bn | 86.07 Bn | 951.00 Mn | 156.00 Mn |
| 6 | General Dynamics | 89.77 Bn | 76.88 Bn | 2.18 Bn | 12.63 Bn |
| 7 | Motorola Solutions | 73.88 Bn | 70.24 Bn | 1.68 Bn | 756.00 Mn |
| 8 | Northrop Grumman | 68.68 Bn | 57.92 Bn | 2.12 Bn | 9.78 Bn |
| 9 | Honeywell International | 66.86 Bn | 19.30 Bn | 3.65 Bn | 1.87 Bn |
| 10 | Intuitive Machines | 3.21 Bn | 2.60 Bn | 35.87 Mn | 253.30 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 253.30 Mn |
| Mar 31, 2026 | 225.93 Mn |
| Dec 31, 2025 | 77.88 Mn |
| Sep 30, 2025 | 67.86 Mn |
| Jun 30, 2025 | 78.95 Mn |
| Mar 31, 2025 | 72.60 Mn |
| Dec 31, 2024 | 68.06 Mn |
| Sep 30, 2024 | 72.20 Mn |
| Jun 30, 2024 | 69.14 Mn |
| Mar 31, 2024 | 75.99 Mn |
| Dec 31, 2023 | 38.08 Mn |
| Sep 30, 2023 | 36.74 Mn |
| Jun 30, 2023 | 32.16 Mn |
| Mar 31, 2023 | 33.69 Mn |
| Dec 31, 2022 | 27.36 Mn |
| Sep 30, 2022 | 22.07 Mn |
| Jun 30, 2022 | 21.44 Mn |
| Mar 31, 2022 | 22.97 Mn |
| Dec 31, 2021 | 449,716.00 |
| Sep 30, 2021 | 21,737.00 |
API Access
Intuitive Machines 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=LUNR&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "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=operating-expenses&ticker=LUNR&period=max&api_key=YOUR_API_KEY");
const data = await res.json();