IDEX vs io.net — how do they compare? IDEX trades at Rp33.85 (market cap Rp74,11M, Rp36,08M 24h volume), while io.net trades at Rp2,067 (market cap Rp787,04M, Rp377,3M 24h volume). The key difference: io.net is far larger — about 10.6× IDEX's market cap, and io.net's supply is capped (381,5M / 800M IO (48%)) while IDEX's keeps growing. Which is the better fit depends on your goals — on Pluang, investors hold IDEX for 20 Days and io.net for 34 Days on average.
| IDEX | IO | |
|---|---|---|
Market Cap | Rp74,11M | Rp787,04M |
Volume (24h) | Rp36,08M | Rp377,3M |
Circulating Supply | 1B IDEX | 381,5M / 800M IO (48%) |
Typical Hold Time | 20 Days | 34 Days |
Signals from Pluang's Aura AI — not financial advice
IDEX token currently trades with a market cap of Rp74,11M and circulating supply of 1M tokens, indicating relatively low market penetration. The 20-day average hold time suggests moderate investor conviction. Recent trading activity shows limited volume, with the token operating in a narrow range. No significant protocol upgrades or ecosystem developments have been reported recently, keeping the project in a consolidation phase.
Overall outlook remains cautious with limited near-term catalysts. Key opportunity lies in potential ecosystem expansion, while major risks include low liquidity, regulatory uncertainty, and competition from more established DeFi protocols. Investors should monitor for meaningful protocol updates or exchange listings that could drive adoption.
No Aura AI signal available yet.
What Pluang investors did over the last 30 days
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IDEX describes itself as the first hybrid liquidity DEX, merging an order book model with an automated market maker (AMM). It combines the speed and functionality of traditional order books with the security and liquidity of AMMs. By integrating an off-chain trading engine with on-chain trade settlement, IDEX offers a unique approach to decentralized exchanges.
Read more on IDEX →io.net, formerly known as ANTBIT, leverages a decentralized computing network powered by Solana and Aptos to provide machine learning engineers with access to distributed cloud clusters. It aims to address challenges like limited availability, poor choice, and high costs associated with accessing GPUs in the public cloud.
Read more on IO →