Match (MTCH) Operating Expenses (2009 - 2026)
Match (MTCH) reported Operating Expenses of $607.66 million for Q2 2026, down 9.3% from $669.82 million a year earlier and down 3.2% from the prior quarter.
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Analysis
Match (MTCH) Operating Expenses (2009 - 2026) Analysis & Trends
Over the twelve months ended Jun 30, 2026, Match's Operating Expenses came in at $2.52 billion, down 4.9% year-over-year; for FY2025, it was $2.61 billion, down 1.6% from FY2024.
- Operating Expenses has a five-year compound annual growth rate of 9.7% (FY2020 to FY2025).
- By year, Operating Expenses came in at $2.66 billion in FY2024 (+8.5%), $2.45 billion in FY2023 (-8.5%), $2.67 billion in FY2022 (+25.4%) and $2.13 billion in FY2021 (+29.5%).
- Five-year quarterly Operating Expenses spans a low of $574.15 million in Q4 2021 and a high of $804.59 million in Q2 2022.
- Year over year, Operating Expenses has now declined in each of the last three quarters, with an average decline of 1.0% over the last eight quarters.
- The high point for year-over-year Operating Expenses in five years was Q2 2022 (growth of 61.6%); the low point was Q2 2023 (a decline of 23.6%).
- Per Business Quant data, the three quarters before Q2 2026 came in at $627.52 million (Q1 2026), $593.33 million (Q4 2025) and $692.94 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Alphabet | 4,185.47 Bn | 3,943.00 Bn | 73.85 Bn | 79.03 Bn |
| 2 | Meta Platforms | 1,794.56 Bn | 1,497.08 Bn | 49.47 Bn | 42.03 Bn |
| 3 | Netflix | 292.71 Bn | 252.90 Bn | 6.52 Bn | 1.51 Bn |
| 4 | Alibaba Group Holding | 251.74 Bn | 65.51 Bn | 15.11 Bn | -5.17 Bn |
| 5 | Shopify | 221.57 Bn | 198.76 Bn | 1.71 Bn | 1.22 Bn |
| 6 | Uber Technologies | 142.45 Bn | 114.25 Bn | 6.38 Bn | 12.30 Bn |
| 7 | Booking Holdings | 124.83 Bn | 57.88 Bn | - | 4.85 Bn |
| 8 | PDD Holdings | 113.76 Bn | -130.01 Bn | 9.45 Bn | -5.39 Bn |
| 9 | Spotify Technology | 106.73 Bn | 64.23 Bn | 1.86 Bn | 640.46 Mn |
| 10 | Match | 10.42 Bn | 6.72 Bn | 648.84 Mn | 607.66 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 607.66 Mn |
| Mar 31, 2026 | 627.52 Mn |
| Dec 31, 2025 | 593.33 Mn |
| Sep 30, 2025 | 692.94 Mn |
| Jun 30, 2025 | 669.82 Mn |
| Mar 31, 2025 | 658.59 Mn |
| Dec 31, 2024 | 636.79 Mn |
| Sep 30, 2024 | 684.82 Mn |
| Jun 30, 2024 | 659.54 Mn |
| Mar 31, 2024 | 674.91 Mn |
| Dec 31, 2023 | 605.98 Mn |
| Sep 30, 2023 | 638.04 Mn |
| Jun 30, 2023 | 614.76 Mn |
| Mar 31, 2023 | 588.84 Mn |
| Dec 31, 2022 | 679.52 Mn |
| Sep 30, 2022 | 598.91 Mn |
| Jun 30, 2022 | 804.59 Mn |
| Mar 31, 2022 | 590.81 Mn |
| Dec 31, 2021 | 574.15 Mn |
| Sep 30, 2021 | 581.25 Mn |
API Access
Match 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=MTCH&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "MTCH", "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=MTCH&period=max&api_key=YOUR_API_KEY");
const data = await res.json();