Noodles (NDLS) Operating Expenses (2012 - 2026)
Noodles' Operating Expenses came in at $128.58 million for Q2 2026, down 8.9% from $141.21 million a year earlier but up 3.2% from the prior quarter.
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Analysis
Noodles (NDLS) Operating Expenses (2012 - 2026) Analysis & Trends
Over the trailing twelve months to Jun 30, 2026, Noodles reported Operating Expenses of $508.46 million, down 3.7% year-over-year; for FY2025, it came in at $526.68 million, up 1.1% from FY2024.
- Operating Expenses carries a five-year compound annual growth rate of 4.9% (FY2020 to FY2025).
- Going back by year, Operating Expenses was $521.05 million in FY2024 (+2.5%), $508.43 million in FY2023 and $469.34 million in FY2021 (+13.5%).
- The five-year range for quarterly Operating Expenses is $119.15 million (Q4 2021) to $141.21 million (Q2 2025).
- Year-over-year, Operating Expenses has declined for three consecutive quarters, with an average decline of 1.7% over the last seven quarters.
- The fastest year-over-year change in Operating Expenses over five years came in Q3 2021 (growth of 13.8%), and the weakest in Q2 2026 (a decline of 8.9%).
- Business Quant data shows NDLS's Operating Expenses at $124.6 million (Q1 2026), $126.88 million (Q4 2025) and $128.41 million (Q3 2025) in the three quarters before Q2 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Mcdonalds | 165.62 Bn | 160.44 Bn | 6.42 Bn | 3.76 Bn |
| 2 | Starbucks | 108.79 Bn | 96.42 Bn | - | 8.42 Bn |
| 3 | Chipotle Mexican Grill | 40.35 Bn | 36.33 Bn | - | 2.82 Bn |
| 4 | Yum Brands | 37.59 Bn | 34.48 Bn | 1.47 Bn | 1.51 Bn |
| 5 | Restaurant Brands International | 24.96 Bn | 22.07 Bn | 1.38 Bn | 1.80 Bn |
| 6 | Darden Restaurants | 22.28 Bn | 21.39 Bn | -113.70 Mn | 3.20 Bn |
| 7 | Restaurant Brands International Limited Partnership | 17.01 Bn | 12.57 Bn | 1.38 Bn | 1.80 Bn |
| 8 | Yum China Holdings | 14.08 Bn | 8.44 Bn | 537.00 Mn | 2.79 Bn |
| 9 | Texas Roadhouse | 10.31 Bn | 9.67 Bn | - | 1.54 Bn |
| 10 | Noodles | 82.34 Mn | 73.64 Mn | 90.30 Mn | 128.58 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 128.58 Mn |
| Mar 31, 2026 | 124.60 Mn |
| Dec 30, 2025 | 126.88 Mn |
| Sep 30, 2025 | 128.41 Mn |
| Jul 1, 2025 | 141.21 Mn |
| Apr 1, 2025 | 130.18 Mn |
| Dec 31, 2024 | 129.14 Mn |
| Oct 1, 2024 | 127.55 Mn |
| Jul 2, 2024 | 138.87 Mn |
| Apr 2, 2024 | 125.49 Mn |
| Jan 2, 2024 | 132.76 Mn |
| Oct 3, 2023 | 125.82 Mn |
| Jul 4, 2023 | 125.43 Mn |
| Apr 4, 2023 | 128.30 Mn |
| Sep 27, 2022 | 127.85 Mn |
| Jun 28, 2022 | 129.19 Mn |
| Mar 29, 2022 | 118.64 Mn |
| Dec 28, 2021 | 119.15 Mn |
| Sep 28, 2021 | 119.81 Mn |
| Jun 29, 2021 | 119.44 Mn |
API Access
Noodles 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=NDLS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "NDLS", "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=NDLS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();