18th Jul 2026, 08:56 AM
Kiansh (Rajma Lal, 1000gm (2x500gm))
Price may vary by location, shipping cost, and availability.
Lowest in last 1 year
84% Off
₹325
₹2,000
Highest: ₹396
Average: ₹372
Lowest: ₹325
Price History Graph
Offer Price Prices
Lowest: -
Average: -
Highest: -
Price History Information
You can check the price history of Kiansh (Rajma Lal, 1000gm (2x500gm)) here. This product's current Price in India is ₹325. The average and highest prices are ₹372 and ₹396 respectively.
| Lowest Ever Price | ₹325 |
|---|---|
| 30th Mar 2026 | |
| Average Price | ₹372 |
| Based on 551 days price tracking | |
| Highest Price | ₹396 |
| 14th Nov 2025 | |
Price History of All Variants of Kiansh Rajma Lal 1000gm 2x500gm
Offer Price: ₹1,162
Kiansh Dry fruits (Kashmiri Walnuts Kernels/Walnut Without Shell, 800g)
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Offer Price: ₹1,450
Kiansh Dry fruits (California Walnuts Kernels/Walnut Without Shell, 1000g)
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Offer Price: ₹1,162
Kiansh Dry fruits (California Walnuts Kernels/Walnut Without Shell, 800g)
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Offer Price: ₹1,450
Kiansh Dry fruits (Kashmiri Walnuts Kernels/Walnut Without Shell, 1000g)
Get Deal
Current Price in India
| Price | ₹325 |
|---|---|
| MRP | ₹2,000 |
| Savings | ₹1,675 |
| Discount | 83.75% |
Recent Price Drops on Kiansh (Rajma Lal, 1000gm (2x500gm))
Price Drop 30th Mar 2026
₹353 → ₹325
7.93% drop
Price Drop 22nd Mar 2026
₹389 → ₹353
9.25% drop
Product Information
Details
| Product Name | Kiansh (Rajma Lal, 1000gm (2x500gm)) |
|---|---|
| Store Product Code | B0DSPW2YXX |
| Store Name | amazon.in |
| Rating | 10 / 10 |
| Category | Mixed Nuts |
| Product Manufacturer | Ancy E-Retalil PVT.LTD |
| Dimensions | 7 x 5 x 2 cm; 1 kg |
| Country of Origin | India |
Features
- 100% Vegetarian
Images
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Rating and Reviews
10 Based on 12 reviews
Kiansh Rajma Lal 1000gm 2x500gm has 10 rating out of 10. This average rating is consist of 12 individual ratings. You can rate this product at product review page.
* Predictions is created on based of past prices and trends, it does not guarantee that it will happen. These are just prediction for theoretical information only.