Maxlinear (MXL) Operating Expenses (2009 - 2026)
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Analysis
Maxlinear (MXL) Operating Expenses (2009 - 2026) Analysis & Trends
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | SK hynix | 24,980.27 Bn | 24,964.04 Bn | 43.99 Bn | 3.63 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 10,337.48 Bn | 9,919.73 Bn | 31.04 Bn | 3.64 Bn |
| 3 | Nvidia | 3,939.34 Bn | 3,713.50 Bn | 72.14 Bn | 8.41 Bn |
| 4 | Broadcom | 1,653.85 Bn | 1,579.89 Bn | 20.46 Bn | 4.50 Bn |
| 5 | Micron Technology | 880.27 Bn | 785.91 Bn | 47.05 Bn | 2.77 Bn |
| 6 | Advanced Micro Devices | 875.86 Bn | 824.07 Bn | 7.21 Bn | 4.38 Bn |
| 7 | Asml Holding | 576.26 Bn | 531.95 Bn | 5.90 Bn | - |
| 8 | Intel | 316.79 Bn | 179.50 Bn | 7.10 Bn | 4.57 Bn |
| 9 | Applied Materials | 294.03 Bn | 294.03 Bn | 5.25 Bn | 1.59 Bn |
| 10 | Maxlinear | 5.08 Bn | 4.80 Bn | 128.07 Mn | 97.84 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 97.84 Mn |
| Jun 30, 2026 | 101.85 Mn |
| Mar 31, 2026 | 96.09 Mn |
| Dec 31, 2025 | 93.45 Mn |
| Sep 30, 2025 | 113.19 Mn |
| Jun 30, 2025 | 86.14 Mn |
| Mar 31, 2025 | 99.93 Mn |
| Dec 31, 2024 | 92.42 Mn |
| Sep 30, 2024 | 110.82 Mn |
| Jun 30, 2024 | 91.01 Mn |
| Mar 31, 2024 | 123.88 Mn |
| Dec 31, 2023 | 110.28 Mn |
| Sep 30, 2023 | 91.76 Mn |
| Jun 30, 2023 | 108.81 Mn |
| Mar 31, 2023 | 113.03 Mn |
| Dec 31, 2022 | 122.18 Mn |
| Sep 30, 2022 | 115.54 Mn |
| Jun 30, 2022 | 125.34 Mn |
| Mar 31, 2022 | 106.46 Mn |
| Dec 31, 2021 | 112.44 Mn |
API Access
Maxlinear 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=MXL&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "MXL", "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=MXL&period=max&api_key=YOUR_API_KEY");
const data = await res.json();