Pyth vs. Chainlink

How We Stack Up

Selecting an oracle can be difficult. What's the difference between one and another? We've outlined Pyth's key differentiators below.

Core Mission

Pyth’s mission is to provide high-fidelity, high-frequency financial market data to end users securing their DeFi protocols.

Pyth’s data ranges from cryptocurrencies to foreign exchanges to equities to precious metals.

Chainlink’s mission is to expand the capabilities of smart contracts by enabling access to real-world data and off-chain computation.

Chainlink’s data provides solutions for various applications like gaming, sports betting and weather as well as financial data.

Data Providers

Pyth’s data comes from financial institutions in the traditional finance and crypto industries.

These include Jump Trading, Jane Street, Susquehanna, Wintermute, QCP, LMAX, Cboe, IEX, MEMX, Kucoin, Bitstamp, Binance, and more.

Chainlink’s data comes from relayers (often referred to as node operators).

These node operators come from DevOps teams from firms such as 01node, Artfactstakin, Inotel, StakeFish, LinkPool, and more.

Data Source

Pyth uses first-party data that comes from exchanges, trading firms and financial institutions.


Learn more

Chainlink sources some data from exchanges like Kraken and Huobi, but primarily from third-party data aggregators like BraveNewCoin, CoinMarketCap and CoinGecko to deliver price feeds.


Learn more on chain.link

L1 Availability

Pyth is available on the following chains:

  • Aptos
  • Arbitrum
  • Aurora
  • Avalanche
  • BNB Chain
  • Ethereum
  • Fantom
  • -
  • KuCoin Chain
  • Optimism
  • Polygon
  • Solana

Chainlink is available on the following chains:

  • -
  • Arbitrum
  • -
  • Avalanche
  • BNB Chain
  • Ethereum
  • Fantom
  • Heco
  • -
  • Optimism
  • Polygon
  • Solana

Data Ownership

Pyth’s data comes directly from data owners. This includes exchanges and trading firms. The data owners have full rights to the distribution of their price data.

Chainlink's data comes from the node operator relaying it. While it is possible to find out where that data originates from by reaching out to the node operators, the operators themselves are rarely data owners.

Data Fidelity

Pyth publishes aggregated price and Confidence Intervals (CI) for all price feeds. These intervals are a certainty measure of the “true” price of the asset since assets can trade at different prices in different venues.


Chainlink publishes the median price from its data sources once the minimum number of node reporters have reported a price.


Circuit Breaker

Pyth does not use price triggers to prevent certain prices from being pushed. Pyth uses Confidence Intervals to ensure continuous price availability.

This allows projects to consume Pyth’s price feeds during the most volatile market conditions.

Chainlink does use price triggers with an accepted bound established when a feed is created.

This can prevent protocols from updating correctly during the most volatile market conditions.

Oracle Update Frequency

On Pythnet, Pyth price feeds continuously update every 300ms.

On Solana, prices update every 400ms. Using an on-demand price update model Pyth users may permissionlessly push price updates on-chain at every slot for usage.

Thus, Pyth feeds could technically update every 10/15sec on Ethereum, every 3sec on BNB Chain, and in under 1sec on Aptos.


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Chainlink is built as a push model oracle whereby applications can only get an updated price when certain conditions are met.

For Ethereum, Chainlink updates its prices once an hour or at every 0.5% or 1% price deviation.

On BNB Chain, Chainlink updates its prices once a minute or at every 0.1 to 0.5% price deviation.


Learn more on chain.link

Transparency

Pyth’s data is verifiable on-chain.


Chainlink’s data exists off-chain and has to be verified on an individual basis.


Special Features

Pyth has the following special features:


  • Confidence intervals
  • Low latency
  • Native 1-hour EMA
  • On-demand price update model

Chainlink has the following special features:


  • Verifiable random function
  • Proof of reserves
  • External APIs

Performance During Black Swan Events

During the LUNA/UST incident in May ‘22, Pyth managed to track the LUNA price very accurately during the UST de-peg.


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During the LUNA/UST incident in May ‘22,  Chainlink had a “circuit breaker” that stopped updating the price of an asset if it went below 0.1 USD. This caused certain protocols to potentially receive inaccurate prices.


Learn more on chain.link