Xpeng (XPEV) Total Non-Current Liabilities (2019 - 2026)
Xpeng (XPEV) reported Total Non-Current Liabilities of $2.84 billion for Q2 2026, up 44.0% from $1.97 billion a year earlier and up 3.0% from the prior quarter.
Powered by 
Analysis
Xpeng (XPEV) Total Non-Current Liabilities (2019 - 2026) Analysis & Trends
At the end of FY2025, Xpeng posted Total Non-Current Liabilities of $2.1 billion, up 32.5% from FY2024.
- Total Non-Current Liabilities has a five-year compound annual growth rate of 41.2% (FY2020 to FY2025).
- By year, Total Non-Current Liabilities came in at $1.58 billion in FY2024 (-4.0%), $1.65 billion in FY2023 (+8.8%), $1.52 billion in FY2022 (+76.1%) and $861.82 million in FY2021 (+130.5%).
- The Q2 2026 figure ranks as the highest quarterly Total Non-Current Liabilities in data going back to Q4 2019.
- Year over year, Total Non-Current Liabilities has now increased in each of the last six quarters, with growth averaging 24.4% over the last eight quarters.
- The high point for year-over-year Total Non-Current Liabilities in five years was Q4 2021 (growth of 133.3%); the low point was Q4 2024 (a decline of 1.1%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $2.76 billion (Q1 2026), $2.07 billion (Q4 2025) and $2.07 billion (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Total Non-Current Liabilities (Qtr) |
|---|---|---|---|---|---|
| 1 | Tesla | 1,223.67 Bn | 1,049.69 Bn | 4.75 Bn | 47.48 Bn |
| 2 | Toyota Motor | 243.73 Bn | -163.01 Bn | 20.95 Bn | 3.42 Bn |
| 3 | Honda Motor | 126.52 Bn | -7.78 Bn | 8.46 Bn | 5.36 Bn |
| 4 | General Motors | 72.50 Bn | -34.01 Bn | 7.33 Bn | 122.27 Bn |
| 5 | Ferrari | 68.83 Bn | 61.27 Bn | 1.18 Bn | 5.63 Bn |
| 6 | Ford Motor | 48.36 Bn | -94.13 Bn | 6.08 Bn | 219.21 Bn |
| 7 | Rivian Automotive | 18.97 Bn | -4.34 Bn | 179.00 Mn | 8.76 Bn |
| 8 | Magna International | 18.29 Bn | 15.51 Bn | 1.61 Bn | - |
| 9 | Stellantis | 13.35 Bn | -153.18 Bn | 5.55 Bn | 74.97 Bn |
| 10 | Xpeng | 9.11 Bn | -1.64 Bn | 601.83 Mn | 2.84 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 2.84 Bn |
| Mar 31, 2026 | 2.76 Bn |
| Dec 31, 2025 | 2.07 Bn |
| Sep 30, 2025 | 2.07 Bn |
| Jun 30, 2025 | 1.97 Bn |
| Mar 31, 2025 | 2.10 Bn |
| Dec 31, 2024 | 1.61 Bn |
| Sep 30, 2024 | 1.76 Bn |
| Jun 30, 2024 | 1.54 Bn |
| Mar 31, 2024 | 1.56 Bn |
| Dec 31, 2023 | 1.63 Bn |
| Sep 30, 2023 | 1.57 Bn |
| Mar 31, 2023 | 1.51 Bn |
| Dec 31, 2022 | 1.47 Bn |
| Sep 30, 2022 | 1.27 Bn |
| Jun 30, 2022 | 1.07 Bn |
| Mar 31, 2022 | 1.01 Bn |
| Dec 31, 2021 | 859.01 Mn |
| Sep 30, 2021 | 729.00 Mn |
| Jun 30, 2021 | 693.14 Mn |
API Access
Xpeng 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=XPEV&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "total-non-current-liabilities", "ticker": "XPEV", "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=XPEV&period=max&api_key=YOUR_API_KEY");
const data = await res.json();