Maxlinear (MXL) Cash & Equivalents (2010 - 2026)
Maxlinear's Cash & Equivalents came in at $64.81 million for Q2 2026, down 40.3% from $108.62 million a year earlier but up 6.1% from the prior quarter.
Maxlinear (MXL) Cash & Equivalents (2010 - 2026) Analysis & Trends
At the end of FY2025, Maxlinear's Cash & Equivalents was $72.81 million, down 38.6% from FY2024.
- Cash & Equivalents carries a five-year compound annual growth rate of -13.3% (FY2020 to FY2025).
- Going back by year, Cash & Equivalents was $118.58 million in FY2024 (-36.7%), $187.29 million in FY2023 (unchanged), $187.35 million in FY2022 (+43.5%) and $130.57 million in FY2021 (-12.3%).
- The five-year range for quarterly Cash & Equivalents is $61.08 million (Q1 2026) to $224.58 million (Q2 2023).
- Year-over-year, Cash & Equivalents has declined for ten consecutive quarters, with an average decline of 36.2% over the last eight quarters.
- The fastest year-over-year change in Cash & Equivalents over five years came in Q3 2021 (growth of 75.4%), and the weakest in Q1 2025 (a decline of 46.4%).
- Business Quant data shows MXL's Cash & Equivalents at $61.08 million (Q1 2026), $72.81 million (Q4 2025) and $111.86 million (Q3 2025) in the three quarters before Q2 2026.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Cash & Equiv. (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,638.19 Bn | 5,412.36 Bn | 72.14 Bn | 22.44 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,452.80 Bn | 2,078.51 Bn | 27.22 Bn | 99.16 Bn |
| 3 | Broadcom | 1,695.44 Bn | 1,621.48 Bn | 20.46 Bn | 23.98 Bn |
| 4 | Micron Technology | 1,213.55 Bn | 1,152.31 Bn | 35.06 Bn | 25.00 Bn |
| 5 | Advanced Micro Devices | 1,034.54 Bn | 991.29 Bn | 6.20 Bn | 5.09 Bn |
| 6 | Asml Holding | 719.69 Bn | 675.53 Bn | 5.90 Bn | 15.03 Bn |
| 7 | Intel | 601.78 Bn | 486.52 Bn | 6.51 Bn | 12.87 Bn |
| 8 | Lam Research | 434.81 Bn | 411.60 Bn | 3.48 Bn | 5.58 Bn |
| 9 | Applied Materials | 428.57 Bn | 394.02 Bn | 4.59 Bn | 7.04 Bn |
| 10 | Maxlinear | 9.61 Bn | 9.30 Bn | 97.66 Mn | 64.81 Mn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 64.81 Mn |
| Mar 31, 2026 | 61.08 Mn |
| Dec 31, 2025 | 72.81 Mn |
| Sep 30, 2025 | 111.86 Mn |
| Jun 30, 2025 | 108.62 Mn |
| Mar 31, 2025 | 102.77 Mn |
| Dec 31, 2024 | 118.58 Mn |
| Sep 30, 2024 | 148.48 Mn |
| Jun 30, 2024 | 185.11 Mn |
| Mar 31, 2024 | 191.88 Mn |
| Dec 31, 2023 | 187.29 Mn |
| Sep 30, 2023 | 187.03 Mn |
| Jun 30, 2023 | 224.58 Mn |
| Mar 31, 2023 | 207.85 Mn |
| Dec 31, 2022 | 187.35 Mn |
| Sep 30, 2022 | 181.50 Mn |
| Jun 30, 2022 | 211.36 Mn |
| Mar 31, 2022 | 151.11 Mn |
| Dec 31, 2021 | 130.57 Mn |
| Sep 30, 2021 | 169.42 Mn |
Maxlinear Cash & Equivalents 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=cash-and-equivalents&ticker=MXL&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "cash-and-equivalents", "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=cash-and-equivalents&ticker=MXL&period=max&api_key=YOUR_API_KEY");
const data = await res.json();