Synopsys (SNPS) Operating Expenses (2009 - 2026)
Synopsys (SNPS) recorded Operating Expenses of $1.44 billion in fiscal Q3 2026 (quarter ended Jul 31, 2026), up 20.6% from $1.19 billion a year earlier.
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Analysis
Synopsys (SNPS) Operating Expenses (2009 - 2026) Analysis & Trends
On a TTM basis, Synopsys' Operating Expenses came in at $5.68 billion as of Jul 31, 2026, up 42.5% year-over-year; for FY2025 (ended Oct 31, 2025), it came in at $4.52 billion, up 28.1% from FY2024.
- Annual Operating Expenses has increased for three straight fiscal years, with a five-year compound annual growth rate of 14.7% (FY2020 to FY2025).
- Across earlier fiscal years, Operating Expenses came in at $3.53 billion in FY2024 (+17.0%), $3.01 billion in FY2023 (+17.3%), $2.57 billion in FY2022 (-1.5%) and $2.61 billion in FY2021 (+14.9%).
- Quarterly Operating Expenses has ranged from $497.38 million in fiscal Q4 2022 to $1.57 billion in fiscal Q1 2026 over the past five years.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 68.0% in fiscal Q1 2026, against a decline of 31.6% in fiscal Q4 2022 at the low end.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Nvidia | 5,515.53 Bn | 5,289.69 Bn | 72.14 Bn | 8.41 Bn |
| 2 | Taiwan Semiconductor Manufacturing | 2,348.76 Bn | 1,974.46 Bn | 27.22 Bn | 3.13 Bn |
| 3 | Broadcom | 1,668.85 Bn | 1,594.89 Bn | 20.46 Bn | 4.50 Bn |
| 4 | Micron Technology | 1,189.94 Bn | 1,128.71 Bn | 35.06 Bn | 1.72 Bn |
| 5 | Advanced Micro Devices | 992.04 Bn | 948.79 Bn | 6.20 Bn | 4.21 Bn |
| 6 | Asml Holding | 682.73 Bn | 638.57 Bn | 5.90 Bn | - |
| 7 | Intel | 585.14 Bn | 469.87 Bn | 6.51 Bn | 4.71 Bn |
| 8 | Lam Research | 393.49 Bn | 370.29 Bn | 3.48 Bn | 965.25 Mn |
| 9 | Applied Materials | 386.29 Bn | 351.73 Bn | 4.59 Bn | 1.51 Bn |
| 10 | Synopsys | 80.02 Bn | 76.41 Bn | 1.80 Bn | 1.44 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | 1.44 Bn |
| Jan 31, 2026 | 1.57 Bn |
| Oct 31, 2025 | 1.48 Bn |
| Jul 31, 2025 | 1.19 Bn |
| Apr 30, 2025 | 909.49 Mn |
| Jan 31, 2025 | 933.50 Mn |
| Oct 31, 2024 | 950.16 Mn |
| Jul 31, 2024 | 874.86 Mn |
| Apr 30, 2024 | 822.24 Mn |
| Jan 31, 2024 | 879.17 Mn |
| Oct 31, 2023 | 757.04 Mn |
| Jul 31, 2023 | 793.88 Mn |
| Apr 30, 2023 | 722.13 Mn |
| Jan 31, 2023 | 821.05 Mn |
| Oct 31, 2022 | 497.38 Mn |
| Jul 31, 2022 | 742.66 Mn |
| Apr 30, 2022 | 662.72 Mn |
| Jan 31, 2022 | 666.24 Mn |
| Oct 31, 2021 | 727.41 Mn |
| Jul 31, 2021 | 650.14 Mn |
API Access
Synopsys 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=SNPS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "SNPS", "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=SNPS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();