Compare Avalon Labs (AVL) vs OpenLedger (OPEN) Price & Performance
Price performance (Past 24H)
Key statistics
Key statistics
Avalon Labs vs OpenLedger — how do they compare? Avalon Labs trades at Rp286.71 (market cap Rp46,36M, Rp13,57M 24h volume), while OpenLedger trades at Rp3,266 (market cap Rp1,04T, Rp121,2M 24h volume). The key difference: OpenLedger is far larger — about 22433.1× Avalon Labs's market cap, and Avalon Labs's circulating supply is 161,7M / 1B AVL (17%) versus 319M / 1B OPEN (32%) for OpenLedger. Which is the better fit depends on your goals — on Pluang, investors hold Avalon Labs for 9 Days and OpenLedger for 23 Days on average.
| AVL | OPEN | |
|---|---|---|
Market Cap | Rp46,36M | Rp1,04T |
Volume (24h) | Rp13,57M | Rp121,2M |
Circulating Supply | 161,7M / 1B AVL (17%) | 319M / 1B OPEN (32%) |
Typical Hold Time | 9 Days | 23 Days |
Investor sentiment on Pluang
What Pluang investors did over the last 30 days
Investor sentiment on Pluang
What Pluang investors did over the last 30 days
About Avalon Labs
Avalon Labs is building an on-chain financial center for Bitcoin, offering solutions like BTC-backed lending, a Bitcoin-backed stablecoin, yield-generating accounts, and a credit card. Our goal is to create a scalable, transparent, and accessible financial network for Bitcoin holders to use Bitcoin as an economic asset. AVL is the governance token of the Avalon Labs ecosystem. We started as the world's largest issuer of Bitcoin-backed collateralized debt positions (CDPs) and have since expanded into DeFi lending, fixed-rate CeDeFi models, and stablecoins. This growth, driven by community demand, positions Avalon as a leader in on-chain finance. With AVL, we empower our community to actively shape the future of Avalon.
Read more on AVL →About OpenLedger
OpenLedger is an AI blockchain that unlocks liquidity for monetizing data, models, applications, and agents. It facilitates the training, deployment, and on-chain tracking of specialized AI models and data, addressing critical challenges related to transparency, attribution, and verifiability in AI.
Read more on OPEN →
