Bionano Genomics (BNGO) Operating Expenses (2017 - 2026)
Bionano Genomics (BNGO) posted Operating Expenses of $11.53 million for Q2 2026, up 2.3% from $11.28 million a year earlier and up 3.4% from the prior quarter.
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Analysis
Bionano Genomics (BNGO) Operating Expenses (2017 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Operating Expenses at Bionano Genomics was $46.52 million, down 36.7% year-over-year; for FY2025, it came in at $46.52 million, down 55.4% from FY2024.
- Annual Operating Expenses shows a five-year compound annual growth rate of 2.4% (FY2020 to FY2025).
- In prior years, Bionano Genomics' Operating Expenses was $104.36 million in FY2024 (-53.6%), $224.81 million in FY2023 (+63.3%), $137.64 million in FY2022 (+70.0%) and $80.98 million in FY2021 (+96.0%).
- Quarterly Operating Expenses has run from a low of $11.15 million in Q1 2026 to a high of $115.96 million in Q3 2023 over five years.
- On a year-over-year basis, Operating Expenses increased in 1 of the last eight quarters, with an average decline of 38.9%.
- The strongest year-over-year quarter for Operating Expenses in the past five years was Q3 2023, with growth of 241.5%; the weakest was Q3 2024, with a decline of 69.4%.
- According to Business Quant data, Operating Expenses for the three prior quarters was $11.15 million (Q1 2026), $11.94 million (Q4 2025) and $11.91 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Thermo Fisher Scientific | 251.30 Bn | 230.45 Bn | 4.88 Bn | 9.91 Bn |
| 2 | Abbott Laboratories | 174.70 Bn | 145.78 Bn | 7.27 Bn | 10.90 Bn |
| 3 | Danaher | 158.94 Bn | 142.76 Bn | 3.61 Bn | 2.48 Bn |
| 4 | Intuitive Surgical | 145.87 Bn | 125.42 Bn | 1.96 Bn | 988.50 Mn |
| 5 | Medtronic | 111.49 Bn | 77.36 Bn | 6.34 Bn | 4.06 Bn |
| 6 | Stryker | 106.96 Bn | 93.08 Bn | 4.50 Bn | 2.84 Bn |
| 7 | Boston Scientific | 63.62 Bn | 58.63 Bn | 3.85 Bn | 2.67 Bn |
| 8 | Edwards Lifesciences | 50.80 Bn | 34.92 Bn | 1.35 Bn | 840.10 Mn |
| 9 | Becton Dickinson | 49.59 Bn | 46.74 Bn | 2.32 Bn | 4.32 Bn |
| 10 | Bionano Genomics | 20.37 Mn | -44.87 Mn | 4.31 Mn | 11.53 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 11.53 Mn |
| Mar 31, 2026 | 11.15 Mn |
| Dec 31, 2025 | 11.94 Mn |
| Sep 30, 2025 | 11.91 Mn |
| Jun 30, 2025 | 11.28 Mn |
| Mar 31, 2025 | 11.40 Mn |
| Dec 31, 2024 | 15.36 Mn |
| Sep 30, 2024 | 35.46 Mn |
| Jun 30, 2024 | 19.60 Mn |
| Mar 31, 2024 | 33.95 Mn |
| Dec 31, 2023 | 27.39 Mn |
| Sep 30, 2023 | 115.96 Mn |
| Jun 30, 2023 | 41.55 Mn |
| Mar 31, 2023 | 39.91 Mn |
| Dec 31, 2022 | 39.33 Mn |
| Sep 30, 2022 | 33.96 Mn |
| Jun 30, 2022 | 33.55 Mn |
| Mar 31, 2022 | 30.80 Mn |
| Dec 31, 2021 | 29.02 Mn |
| Sep 30, 2021 | 21.83 Mn |
API Access
Bionano Genomics 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=BNGO&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "BNGO", "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=BNGO&period=max&api_key=YOUR_API_KEY");
const data = await res.json();