Opera (OPRA) Total Liabilities (2016 - 2026)
Opera's Total Liabilities was $1.16 billion in Q2 2026, up 8.6% from $1.07 billion a year earlier and up 1.9% from the prior quarter.
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Analysis
Opera (OPRA) Total Liabilities (2016 - 2026) Analysis & Trends
At the end of FY2025, Total Liabilities at Opera came in at $1.14 billion, up 8.3% from FY2024.
- Total Liabilities shows a five-year compound annual growth rate of 79.9% (FY2020 to FY2025).
- In earlier years, Total Liabilities was $1.06 billion in FY2024, $91.09 million in FY2023 (+19.1%), $76.47 million in FY2022 (-3.7%) and $79.42 million in FY2021 (+30.8%).
- The Q2 2026 figure marks the highest quarterly Total Liabilities in data going back to Q4 2016.
- Compared with a year earlier, Total Liabilities has increased for six straight quarters, with growth averaging 170.9% over the last six quarters.
- The best year-over-year quarter for Total Liabilities over five years was Q2 2024 (growth of 994.9%); the worst was Q2 2022 (a decline of 31.2%).
- Per Business Quant data, OPRA's Total Liabilities in the three quarters before Q2 2026 was $1.14 billion (Q1 2026), $1.14 billion (Q4 2025) and $1.06 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 263.71 Bn | 248.78 Bn | 2.30 Bn | 20.97 Bn |
| 2 | CrowdStrike Holdings | 243.16 Bn | 223.56 Bn | 1.10 Bn | 6.89 Bn |
| 3 | Fortinet | 127.21 Bn | 113.14 Bn | 1.64 Bn | 9.31 Bn |
| 4 | Snowflake | 114.15 Bn | 101.47 Bn | 1.04 Bn | 6.54 Bn |
| 5 | Datadog | 98.45 Bn | 80.09 Bn | 881.34 Mn | 3.18 Bn |
| 6 | Zscaler | 36.67 Bn | 22.79 Bn | - | 5.27 Bn |
| 7 | Okta | 36.35 Bn | 26.44 Bn | 641.00 Mn | 2.17 Bn |
| 8 | Axon Enterprise | 32.29 Bn | 26.81 Bn | 546.45 Mn | 3.81 Bn |
| 9 | MongoDB | 31.47 Bn | 21.94 Bn | 569.77 Mn | 797.71 Mn |
| 10 | Opera | 1.59 Bn | 872.88 Mn | 114.91 Mn | 148.76 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 148.76 Mn |
| Mar 31, 2026 | 151.97 Mn |
| Dec 31, 2025 | 132.54 Mn |
| Sep 30, 2025 | 112.15 Mn |
| Jun 30, 2025 | 113.93 Mn |
| Mar 31, 2025 | 113.52 Mn |
| Dec 31, 2024 | 115.49 Mn |
| Sep 30, 2024 | 100.97 Mn |
| Jun 30, 2024 | 85.46 Mn |
| Mar 31, 2024 | 95.83 Mn |
| Dec 31, 2023 | 91.09 Mn |
| Sep 30, 2023 | 81.50 Mn |
| Jun 30, 2023 | 92.05 Mn |
| Mar 31, 2023 | 78.10 Mn |
| Dec 31, 2022 | 76.47 Mn |
| Sep 30, 2022 | 78.68 Mn |
| Jun 30, 2022 | 64.50 Mn |
| Mar 31, 2022 | 71.82 Mn |
| Dec 31, 2021 | 79.42 Mn |
| Sep 30, 2021 | 90.53 Mn |
API Access
Opera 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=OPRA&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-liabilities", "ticker": "OPRA", "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=OPRA&period=max&api_key=YOUR_API_KEY");
const data = await res.json();