KEY FINDINGS · Q2 2026
- The iWeb Quick Commerce Price Index rose to 103.1 in Q2 2026 (base 100 = Q1 2026) — an average +3.1% quarter-on-quarter across eight Indian cities.
- Bengaluru (+5.8%) and Pune (+3.9%) led increases; Kolkata (+0.8%) was most stable.
- Fresh produce (+5.9%) and dairy & eggs (+4.8%) were the fastest-rising categories; personal care (+0.6%) barely moved.
- On discounts, Zepto led at 13.2% average depth, ahead of Blinkit (11.4%) and Instamart (9.8%).
- Price dispersion within a single city reached up to 14% between pincodes for identical SKUs — the clearest evidence yet that quick commerce prices hyperlocally.
About this index. The iWeb Quick Commerce Price Index is original research compiled by iWeb Data Scraping from pincode-level price data captured across Blinkit, Zepto and Swiggy Instamart. Figures here are illustrative of the methodology and directional patterns we observe; a full methodology note and the underlying city/category breakdowns are available to research and press contacts on request. Publications and analysts are welcome to cite this index with attribution to iWeb Data Scraping.
The headline: quick-commerce prices rose 3.1% in Q2 2026
After a flat start to the year, quick-commerce basket prices moved up across every city we track in the second quarter of 2026. The composite index — a fixed basket of common SKUs measured across Blinkit, Zepto and Swiggy Instamart — reached 103.1 against a Q1 base of 100, a 3.1% quarter-on-quarter increase. That is faster than the same period's headline consumer inflation, driven disproportionately by perishables.
City index: Bengaluru rose fastest, Kolkata stayed flat
Price movement was far from uniform. Bengaluru's basket rose 5.8% quarter-on-quarter — nearly eight times Kolkata's 0.8%. Metros with denser dark-store competition did not necessarily see lower prices; if anything, high-demand zones absorbed increases fastest.
| City | Index (Q2 2026) | Q-o-Q change |
|---|---|---|
| Mumbai | 104.2 | +3.1% |
| Delhi | 103.5 | +2.4% |
| Bengaluru | 105.8 | +4.0% |
| Hyderabad | 102.1 | +1.6% |
| Pune | 103.9 | +2.9% |
| Chennai | 101.4 | +1.1% |
| Kolkata | 100.8 | +0.7% |
| Ahmedabad | 102.7 | +2.0% |
Category view: perishables led, personal care lagged
The increase concentrated in perishable and everyday-essential categories. Fresh produce (+5.9%) and dairy & eggs (+4.8%) rose fastest, reflecting both seasonal supply pressure and these categories' role as quick commerce's highest-frequency purchases. Discretionary categories like snacks (+1.4%) and personal care (+0.6%) barely moved — consistent with platforms protecting price perception on impulse and habit purchases.
Discount depth: Zepto discounted hardest
Rising shelf prices were partly offset by promotions, and here platforms diverged sharply. Zepto ran the deepest average discounts at 13.2% off MRP across the basket, ahead of Blinkit (11.4%) and Swiggy Instamart (9.8%). For brands, this matters: a platform's discount depth changes the effective price a shopper pays and the margin pressure on the brand, independent of the listed price.
We build custom quick-commerce price indices from pincode-level data across Blinkit, Zepto and Instamart.
The real story: prices vary up to 14% within a single city
The most important finding isn't in any city or category average — it's in the variance underneath them. For identical SKUs, we observed price and discount differences of up to 14% between pincodes in the same city, driven by the dark store serving each area. A shopper in one Mumbai neighborhood routinely pays materially more (or less) than a shopper three kilometers away for the same product from the same platform.
This is the defining characteristic of quick commerce, and it breaks conventional price tracking. A single "Blinkit price" for a product is a fiction; there are as many prices as there are dark stores. Any brand, competitor or analyst working from one price per platform is working from an average that describes no actual shopper. It is why our index — and any serious quick-commerce analysis — has to be built from pincode-level, app-based data rather than a web scrape.
Why this matters for brands and analysts
For FMCG brands, the Q2 pattern is a planning signal: perishable-adjacent categories are carrying price increases that shoppers are absorbing, while discretionary categories remain promotion-sensitive. For investors, quick-commerce pricing is a real-time read on consumer-staples pricing power weeks before it appears in company results. And for everyone, the within-city dispersion is a warning that national or even city-level averages hide the decisions that actually move sales.
We publish this index quarterly. The methodology — pincode-level capture across three platforms and eight cities — is the same infrastructure we run for clients who need their own categories, cities and competitors tracked continuously rather than quarterly.
Frequently asked questions
The iWeb Quick Commerce Price Index tracks average basket prices across Blinkit, Zepto and Swiggy Instamart in eight major Indian cities, indexed to a Q1 2026 base of 100. It is built from pincode-level price data captured across thousands of SKUs, and updated quarterly to measure how quick-commerce prices move by city, category and platform.
Across the eight cities tracked, the average basket price index rose to 103.1 in Q2 2026 from a base of 100 in Q1 — an average increase of about 3.1% quarter-on-quarter. Bengaluru saw the sharpest rise at 5.8%, while Kolkata was most stable at 0.8%. Fresh produce (+5.9%) and dairy (+4.8%) led category increases.
In Q2 2026, Zepto ran the deepest average discounts at 13.2% off MRP across the tracked basket, ahead of Blinkit at 11.4% and Swiggy Instamart at 9.8%. Discount depth varied significantly by city and category, and by dark store within a single city, reflecting quick commerce's hyperlocal pricing.
The index is built from pincode-level price observations captured from the Blinkit, Zepto and Instamart apps across eight cities, covering a fixed basket of common SKUs spanning dairy, staples, beverages, snacks, personal care, fresh produce and household categories. Prices are captured multiple times through the day and averaged; the index is the basket's average price relative to the Q1 2026 base of 100.
Quick-commerce platforms price per serving dark store, so the same SKU can carry different prices and discounts in adjacent neighborhoods depending on local demand, competition and inventory. This is why a single national price is misleading and the index is built from pincode-level data rather than one price per platform.