Compare Old Dominion Freight Line Inc (ODFL) vs Rigetti Computing Inc (RGTI) Price & Performance
Price performance (Past 24H)
Key statistics
Key statistics
Old Dominion Freight Line Inc vs Rigetti Computing Inc — how do they compare? Old Dominion Freight Line Inc trades at $232.8 (market cap $48.83B), while Rigetti Computing Inc trades at $15.22 (market cap $5.08B). The key difference: Old Dominion Freight Line Inc is far larger — about 9.6× Rigetti Computing Inc's market cap, and Old Dominion Freight Line Inc pays a 0.49% dividend while Rigetti Computing Inc pays none. Which is the better fit depends on your goals.
| ODFL | RGTI | |
|---|---|---|
Market Cap | $48.83B | $5.08B |
Sector | Industrials | Technology |
52-Week High | $248.73 | $56.34 |
52-Week Low | $126.29 | $12.90 |
Enterprise Value | $48.58B | $4.67B |
Dividend Yield | 0.49% | — |
Returns comparison
Trailing returns across standard periods
Returns comparison
Trailing returns across standard periods
About Old Dominion Freight Line Inc
Old Dominion Freight Line is the fourth-largest less-than-truckload carrier in the United States, with more than 240 service centers and 9,200-plus tractors. OD is by far one of the most disciplined and efficient providers in the trucking industry, and its profitability and capital returns stand head and shoulders above its peers. Strategic initiatives revolve around increasing network density through market share gains and maintaining industry-leading service via consistent infrastructure investment.
Read more on ODFL →About Rigetti Computing Inc
Rigetti Computing, Inc. is a pioneer in quantum computing, focusing on developing and deploying quantum-classical computing systems. The company designs and fabricates superconducting quantum processors and integrates them with a full-stack software and control platform. Rigetti offers access to its quantum computers through the cloud, aiming to solve complex computational problems that are intractable for classical computers, with applications in finance, chemistry, and machine learning.
Read more on RGTI →
