COST

Costco Wholesale Corp Price

Closed
COST
$1,010.69
-$5.22(-0.51%)

*Data last updated: 2026-05-02 00:35 (UTC+8)

As of 2026-05-02 00:35, Costco Wholesale Corp (COST) is priced at $1,010.69, with a total market cap of $448.84B, a P/E ratio of 51.71, and a dividend yield of 0.53%. Today, the stock price fluctuated between $1,006.50 and $1,031.33. The current price is 0.41% above the day's low and 2.00% below the day's high, with a trading volume of 1.45M. Over the past 52 weeks, COST has traded between $937.02 to $1,035.78, and the current price is -2.42% away from the 52-week high.

COST Key Stats

Yesterday's Close$1,014.53
Market Cap$448.84B
Volume1.45M
P/E Ratio51.71
Dividend Yield (TTM)0.53%
Dividend Amount$1.47
Diluted EPS (TTM)19.25
Net Income (FY)$8.09B
Revenue (FY)$275.23B
Earnings Date2026-07-29
EPS Estimate4.95
Revenue Estimate$68.69B
Shares Outstanding442.41M
Beta (1Y)0.978
Ex-Dividend Date2026-05-01
Dividend Payment Date2026-05-15

About COST

Costco Wholesale Corporation, together with its subsidiaries, engages in the operation of membership warehouses in the United States, Puerto Rico, Canada, the United Kingdom, Mexico, Japan, Korea, Australia, Spain, France, Iceland, China, and Taiwan. It offers branded and private-label products in a range of merchandise categories. The company offers sundries, dry groceries, candies, coolers, freezers, liquor, and tobacco and deli products; appliances, electronics, health and beauty aids, hardware, garden and patio products, sporting goods, tires, toys and seasonal products, office supplies, automotive care products, postages, tickets, apparel, small appliances, furniture, domestics, housewares, special order kiosks, and jewelry; and meat, produce, service deli, and bakery products. It also operates pharmacies, opticals, food courts, hearing-aid centers, and tire installation centers, as well as 636 gas stations; and offers business delivery, travel, same-day grocery, and various other services online in various countries. As of August 29, 2021, the company operated 815 membership warehouses, including 564 in the United States and Puerto Rico, 105 in Canada, 39 in Mexico, 30 in Japan, 29 in the United Kingdom, 16 in South Korea, 14 in Taiwan, 12 in Australia, 3 in Spain, 1 in Iceland, 1 in France, and 1 in China. It also operates e-commerce websites in the United States, Canada, the United Kingdom, Mexico, South Korea, Taiwan, Japan, and Australia. The company was formerly known as Costco Companies, Inc. and changed its name to Costco Wholesale Corporation in August 1999. Costco Wholesale Corporation was founded in 1976 and is based in Issaquah, Washington.
SectorConsumer Defensive
IndustryDiscount Stores
CEORon Vachris
HeadquartersIssaquah,WA,US
Official Websitehttps://www.costco.com
Employees (FY)341.00K
Average Revenue (1Y)$807.14K
Net Income per Employee$23.75K

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Costco Wholesale Corp (COST) is currently trading at $1,010.69, with a 24h change of -0.51%. The 52-week trading range is $937.02–$1,035.78.

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Costco Wholesale Corp (COST) Latest News

2026-04-30 12:10

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2026-04-30 07:53

TwentyOne Capital Consolidates Bitcoin Mining, Treasury, and Finance Operations, Expands Holdings

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2026-04-30 04:17

Nvidia Blackwell GPU Costs Double, But Per-Token Inference Expense Falls 35x vs Hopper

According to Nvidia's latest blog analysis, Blackwell GPUs cost nearly double per hour compared to Hopper generation, yet deliver 35-fold lower per-token inference costs. Using DeepSeek-R1 as a test model, Blackwell (GB300 NVL72) rents at $2.65 per GPU per hour versus Hopper's $1.41, but single GPU throughput jumps from 90 to 6,000 tokens per second. This 65x throughput gain reduces per-million-token costs from $4.20 to $0.12. The $0.12 figure assumes full software optimization including FP4 low-precision inference and multi-token prediction (MTP). Without MTP enabled, per-million-token costs reach approximately $2.35, dropping to $0.11 with it active, demonstrating a 21x optimization impact from that feature alone.

2026-04-29 09:18

Bitcoin Must Break $79,000 to Confirm Bottom, Willy Woo Says; Analyst Gives Move 30% Odds

According to analyst Willy Woo, Bitcoin (BTC) must break above $79,000 and hold above $65,000 to confirm a structural bottom, with Woo giving the $79,000 breakout a 30% probability. The $79,000 level represents the cost basis of recent investors; if BTC flips it into support, it would signal the end of the ongoing bearish phase. Woo cautioned that if Bitcoin falls below the mid-$60,000 level, a more severe correction may occur. Bitcoin was trading at $76,237 at the time of writing, down 1.3% on the day.

2026-04-29 08:17

Claude's Chinese Language Tokenization Cost 65% Higher Than English, OpenAI Only 15% More

Gate News message, April 29 — AI researcher Aran Komatsuzaki conducted a comparative analysis of tokenization efficiency across six major AI models by translating Rich Sutton's seminal paper "The Bitter Lesson" into nine languages and processing them through OpenAI, Gemini, Qwen, DeepSeek, Kimi, and Claude's tokenizers. Using the English version's token count on OpenAI as the baseline (1x), the study revealed significant disparities: processing the same content in Chinese required 1.65x tokens on Claude, compared to only 1.15x on OpenAI. Hindi showed an even more extreme result on Claude, exceeding the baseline by over 3x. Anthropic ranked lowest among the six models tested. Critically, when the identical Chinese text was processed across different models—all measured against the same English baseline—the results diverged dramatically: Kimi consumed only 0.81x tokens (even less than English), Qwen 0.85x, while Claude required 1.65x. This gap reveals a pure tokenization efficiency problem, not an inherent language issue. Chinese models demonstrated superior efficiency in processing Chinese, suggesting the disparity stems from tokenizer optimization rather than the language itself. The practical implications for users are substantial: increased token consumption directly raises API costs, extends model response latency, and depletes context windows more rapidly. Tokenization efficiency depends on the linguistic composition of a model's training data—models trained predominantly on English compress English text more efficiently, while languages with lower data representation are tokenized into smaller, less efficient fragments. Komatsuzaki's conclusion underscores a fundamental principle: market size determines tokenization efficiency. Larger markets receive better optimization, while underrepresented languages face significantly higher token costs.

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