Maxlinear (MXL) Operating Expenses (2010 - 2026)
Maxlinear (MXL) recorded Operating Expenses of $101.85 million in Q2 2026, up 18.2% from $86.14 million a year earlier and up 6.0% from the prior quarter.
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Analysis
Maxlinear (MXL) Operating Expenses (2010 - 2026) Analysis & Trends
On a TTM basis, Maxlinear's Operating Expenses came in at $404.58 million as of Jun 30, 2026, up 3.9% year-over-year; for FY2025, it came in at $392.7 million, down 6.1% from FY2024.
- Annual Operating Expenses has declined for three straight years, though with a five-year compound annual growth rate of 4.6% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $418.13 million in FY2024 (-1.4%), $423.88 million in FY2023 (-9.7%), $469.53 million in FY2022 (+9.0%) and $430.59 million in FY2021 (+37.2%).
- Quarterly Operating Expenses has ranged from $86.14 million in Q2 2025 to $125.34 million in Q2 2022 over the past five years.
- On a year-over-year basis, Operating Expenses rose in four of the last eight quarters, with an average decline of 0.3%.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 20.8% in Q3 2024, against a decline of 20.6% in Q3 2023 at the low end.
- Per Business Quant, the preceding three quarters came in at $96.09 million (Q1 2026), $93.45 million (Q4 2025) and $113.19 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,638.19 Bn | 5,412.36 Bn | 72.14 Bn | 8.41 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,452.80 Bn | 2,078.51 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Broadcom | 1,695.44 Bn | 1,621.48 Bn | 20.46 Bn | 4.50 Bn |
| 4 | Micron Technology | 1,213.55 Bn | 1,152.31 Bn | 35.06 Bn | 1.72 Bn |
| 5 | Advanced Micro Devices | 1,034.54 Bn | 991.29 Bn | 6.20 Bn | 4.21 Bn |
| 6 | Asml Holding | 719.69 Bn | 675.53 Bn | 5.90 Bn | - |
| 7 | Intel | 601.78 Bn | 486.52 Bn | 6.51 Bn | 4.71 Bn |
| 8 | Lam Research | 434.81 Bn | 411.60 Bn | 3.48 Bn | 965.25 Mn |
| 9 | Applied Materials | 428.57 Bn | 394.02 Bn | 4.59 Bn | 1.51 Bn |
| 10 | Maxlinear | 9.61 Bn | 9.30 Bn | 97.66 Mn | 101.85 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| 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 |
| Sep 30, 2021 | 106.01 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();