ImmunityBio (IBRX) Operating Expenses (2014 - 2026)
ImmunityBio (IBRX) reported Operating Expenses of $112.94 million for Q2 2026, up 15.6% from $97.71 million a year earlier but down 0.9% from the prior quarter.
ImmunityBio (IBRX) Operating Expenses (2014 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, ImmunityBio's Operating Expenses came in at $417.59 million, up 22.1% year-over-year; for FY2025, it came in at $369.32 million, up 2.9% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 10.8% (FY2020 to FY2025).
- By year, Operating Expenses came in at $358.93 million in FY2024 (-1.1%), $362.87 million in FY2023 (+3.2%), $351.54 million in FY2022 (+6.1%) and $331.21 million in FY2021 (+49.5%).
- Five-year quarterly Operating Expenses spans a low of $76.95 million in Q4 2024 and a high of $114 million in Q1 2026.
- Year over year, Operating Expenses has now increased in each of the last four quarters, with growth averaging 9.0% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q4 2021 (growth of 157.8%); the low point was Q1 2025 (a decline of 15.0%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $114 million (Q1 2026), $102.97 million (Q4 2025) and $87.69 million (Q3 2025).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Eli Lilly | 1,138.33 Bn | 1,106.91 Bn | 19.71 Bn | 7.95 Bn |
| 2 | Johnson & Johnson | 618.82 Bn | 537.35 Bn | 17.26 Bn | 10.12 Bn |
| 3 | AbbVie | 503.36 Bn | 476.47 Bn | 12.70 Bn | 10.56 Bn |
| 4 | Merck | 369.59 Bn | 323.96 Bn | 12.21 Bn | 12.80 Bn |
| 5 | Novartis Ag | 298.57 Bn | 262.56 Bn | 11.24 Bn | 6.09 Bn |
| 6 | Astrazeneca | 261.25 Bn | 234.94 Bn | 12.86 Bn | 9.70 Bn |
| 7 | Amgen | 237.35 Bn | 192.75 Bn | 7.24 Bn | 6.54 Bn |
| 8 | Gilead Sciences | 191.27 Bn | 165.47 Bn | 6.22 Bn | 18.20 Bn |
| 9 | Pfizer | 170.03 Bn | 116.68 Bn | 10.94 Bn | 6.68 Bn |
| 10 | ImmunityBio | 9.11 Bn | 7.87 Bn | 50.94 Mn | 112.94 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 112.94 Mn |
| Mar 31, 2026 | 114.00 Mn |
| Dec 31, 2025 | 102.97 Mn |
| Sep 30, 2025 | 87.69 Mn |
| Jun 30, 2025 | 97.71 Mn |
| Mar 31, 2025 | 80.95 Mn |
| Dec 31, 2024 | 76.95 Mn |
| Sep 30, 2024 | 86.36 Mn |
| Jun 30, 2024 | 100.38 Mn |
| Mar 31, 2024 | 95.24 Mn |
| Dec 31, 2023 | 85.53 Mn |
| Sep 30, 2023 | 80.22 Mn |
| Jun 30, 2023 | 85.19 Mn |
| Mar 31, 2023 | 111.94 Mn |
| Dec 31, 2022 | 84.97 Mn |
| Sep 30, 2022 | 90.92 Mn |
| Jun 30, 2022 | 79.66 Mn |
| Mar 31, 2022 | 95.99 Mn |
| Dec 31, 2021 | 79.66 Mn |
| Sep 30, 2021 | 78.90 Mn |
ImmunityBio 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=IBRX&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "IBRX", "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=IBRX&period=max&api_key=YOUR_API_KEY");
const data = await res.json();