Pyth excels at high-frequency, low-latency data delivery through its pull-based model. Protocols like Solana's Jupiter and MarginFi actively request price updates on-demand, which minimizes unnecessary on-chain transactions and gas costs for dApps during low volatility. This model is optimized for chains with high throughput and low fees, where the latency of a pull request is negligible. Pyth's network of over 90 first-party data providers feeds a $2.5B+ Total Value Secured (TVS) ecosystem, demonstrating its adoption for latency-sensitive perpetuals and spot markets.
LABS
Comparisons
Pyth vs Supra: Cross-Chain Access
A technical analysis comparing Pyth's push-based oracle network with Supra's pull-based, low-latency model. We evaluate performance, cost, security, and ideal use cases for CTOs and protocol architects.
Chainscore © 2026
introduction
THE ANALYSIS
Introduction: The Push vs Pull Oracle Paradigm
A fundamental architectural choice separates Pyth and Supra, defining their performance, cost, and suitability for different DeFi applications.
Supra takes a different approach with its push-based (or publish-subscribe) oracle design. Its decentralized oracle nodes proactively broadcast price updates to all subscribed chains at predefined intervals or deviation thresholds. This results in consistently fresh data being available on-chain without dApp initiation, crucial for options protocols and money markets that require guaranteed, time-bound updates. The trade-off is potentially higher baseline gas consumption on the destination chain, as updates occur regardless of immediate demand.
The key trade-off: If your priority is minimizing operational gas costs on high-throughput L1/L2s and you can manage update triggers, Pyth's pull model is optimal. If you prioritize guaranteed, predictable data freshness for critical liquidation engines or time-sensitive derivatives across diverse, lower-throughput ecosystems, Supra's push mechanism provides stronger assurances. The choice hinges on whether you value cost efficiency per update or absolute data availability.
tldr-summary
Pyth vs Supra: Cross-Chain Access
TL;DR: Core Differentiators
Key strengths and trade-offs for CTOs evaluating oracle infrastructure for multi-chain deployments.
01
Pyth: Unmatched Data Breadth & Adoption
Specific advantage: 500+ price feeds covering crypto, FX, equities, and commodities with $4B+ in total value secured (TVS). This matters for institutional DeFi (e.g., Synthetix, Ribbon Finance) requiring deep, diverse market data for complex derivatives and structured products.
500+
Price Feeds
$4B+
TVS
02
Pyth: Pull-Based Model for Cost Control
Specific advantage: On-demand data updates where users pay only for the data they consume. This matters for gas-sensitive applications on L2s and high-throughput chains, allowing protocols like Drift and MarginFi to optimize operational costs versus paying for continuous push updates.
03
Supra: Ultra-Low Latency & Finality
Specific advantage: Sub-2 second data finality via its proprietary consensus mechanism (Moonshot Consensus). This matters for high-frequency trading (HFT) dApps and perps exchanges where front-running protection and near-instant price updates are critical for user experience and capital efficiency.
< 2 sec
Finality
04
Supra: Push-Based Freshness Guarantee
Specific advantage: Continuous, automated data pushes to all integrated chains. This matters for real-time settlement and risk engines (e.g., lending protocols, options platforms) that require guaranteed, up-to-the-second data without relying on user-initiated pulls, reducing latency spikes.
05
Pyth: Superior Ecosystem Integration
Specific advantage: Native integration with 60+ blockchains and major DeFi protocols, backed by a consortium of 100+ first-party publishers. This matters for protocol architects prioritizing composability and security with established standards, minimizing integration risk and audit overhead.
60+
Chains
06
Supra: Novel Data Feeds & Composability
Specific advantage: Offers unique feeds like volatility indices and TWAPs alongside prices, and a cross-chain interoperability layer (DORA) for data composability. This matters for innovative protocols building advanced options, volatility products, or needing to orchestrate data flows across ecosystems.
CROSS-CHAIN ORACLE DATA ACCESS
Feature Comparison: Pyth vs Supra
Direct comparison of key metrics and features for cross-chain price feed oracles.
| Metric | Pyth Network | Supra |
|---|---|---|
Data Sources (Publishers) | 90+ | 30+ |
Supported Blockchains | 50+ | 50+ |
Price Feeds Available | 400+ | 800+ |
Update Frequency | ~400ms | ~500ms |
Pull Oracle Model | ||
Push Oracle Model | ||
Native Token for Staking | PYTH | SUPRA |
Historical Data Access |
CROSS-CHAIN ORACLE ACCESS COMPARISON
Pyth vs Supra: Performance & Cost Benchmarks
Direct comparison of key technical and economic metrics for oracle data access across blockchains.
| Metric | Pyth Network | Supra |
|---|---|---|
Data Update Latency (Target) | < 400 ms | < 500 ms |
Supported Blockchains | 60+ | 50+ |
Price Feeds Available | 500+ | 800+ |
Data Pull Model | ||
Data Push Model | ||
Avg. Update Cost (Solana) | $0.0001 | $0.00015 |
Avg. Update Cost (EVM) | $0.10 - $0.50 | $0.05 - $0.20 |
Consensus Mechanism | Pythnet (Solana-based) | Moonshot Consensus (DAG-based) |
pros-cons-a
PROS AND CONS
Pyth Network vs. Supra Oracles: Cross-Chain Access
Key strengths and trade-offs for CTOs evaluating cross-chain oracle infrastructure.
01
Pyth: Unmatched Market Coverage
Specific advantage: 500+ real-time price feeds across crypto, forex, equities, and commodities, with over $3.5B in total value secured. This matters for DeFi protocols like Synthetix or MarginFi that require deep, institutional-grade data for complex derivatives and cross-margin trading.
02
Pyth: Deep Liquidity Provider Integration
Specific advantage: Direct data sourcing from 90+ premier exchanges and trading firms (e.g., Jane Street, CBOE). This matters for perpetuals DEXs and money markets where latency and manipulation resistance are critical, as it provides sub-second updates with robust attestations.
03
Supra: Novel Consensus for Speed & Finality
Specific advantage: Proprietary DORA consensus and Moonshot consensus algorithms enabling ~3-5 second finality for price updates. This matters for high-frequency on-chain applications like gaming or options protocols where traditional oracle update cycles are a bottleneck.
04
Supra: Vertical Integration & Incentive Alignment
Specific advantage: Full-stack control over its oracle node network and tokenomics designed for data accuracy staking. This matters for new L1/L2 chains seeking a tightly integrated, one-stop oracle solution to bootstrap their ecosystem with aligned security incentives.
05
Pyth: Potential Drawback - Complexity & Cost
Specific trade-off: The pull-based update model requires active on-chain polling, which can lead to higher gas costs for applications if not optimized. This matters for high-volume, cost-sensitive applications on Ethereum L1, where constant data pulls can become a significant operational expense.
06
Supra: Potential Drawback - Ecosystem Maturity
Specific trade-off: Smaller current footprint with ~50+ integrated chains vs. Pyth's 50+. This matters for enterprise CTOs with a multi-chain mandate who require proven, battle-tested integrations on all major networks like Arbitrum, Base, and Solana from day one.
pros-cons-b
PYTH VS SUPRA: CROSS-CHAIN ACCESS
Supra: Pros and Cons
Key strengths and trade-offs for CTOs evaluating oracle infrastructure for multi-chain deployments.
01
Supra's Pro: Ultra-Low Latency & High Frequency
Sub-second finality for price feeds: Supra's Moonshot consensus and DORA algorithm target 500-800ms data updates. This matters for perps, options, and money markets where stale data directly impacts liquidations and arbitrage.
< 1 sec
Update Target
02
Supra's Pro: Novel Pull-Based Architecture
On-demand data fetching: Unlike Pyth's standard push model, Supra's pull-based design lets applications request fresh data at execution time, reducing gas costs for inactive feeds. This matters for intermittent or event-driven protocols like insurance or prediction markets.
03
Supra's Con: Smaller Ecosystem & Track Record
Newer, less battle-tested: While live on 25+ testnets/mainnets, Supra's TVL and protocol integrations trail Pyth's. This matters for risk-averse institutions who prioritize proven security and extensive audits (Pyth has been live since 2021).
2021
Pyth Mainnet Launch
04
Supra's Con: Limited Data Diversity
Focus on crypto & FX: Supra's feed catalog is currently concentrated on core DeFi assets. This matters for protocols needing real-world data (RWA, sports, weather) or niche equities, where Pyth's 400+ feeds and partnerships (e.g., CBOE, Binance) provide broader coverage.
CHOOSE YOUR PRIORITY
Decision Framework: When to Use Which
Pyth for DeFi
Verdict: The dominant choice for established, high-value protocols. Strengths: Unmatched Total Value Secured (TVS), exceeding $5B, providing deep liquidity confidence. Offers low-latency price feeds for 500+ assets with pull-oracle flexibility. Battle-tested by protocols like Synthetix, Jupiter, and Drift. Superior for perpetuals, money markets, and options requiring institutional-grade data.
Supra for DeFi
Verdict: A strong contender for novel, multi-chain applications prioritizing speed. Strengths: Sub-2-second finality via its DORA consensus and cross-chain intents enable new DeFi primitives. Lower operational costs for high-frequency updates. Ideal for cross-chain arbitrage bots, fast-twitch lending protocols, and derivatives on emerging L2s like Blast or Manta. Its pull-and-push hybrid model suits reactive strategies.
verdict
THE ANALYSIS
Final Verdict and Recommendation
Choosing between Pyth and Supra hinges on your protocol's specific requirements for data freshness, cost structure, and architectural philosophy.
Pyth excels at delivering ultra-low-latency, high-frequency price data for institutional-grade DeFi because of its first-party data model and pull-based oracle design. For example, it consistently achieves sub-second updates for assets like SOL/USD and ETH/USD, supporting high-leverage perpetuals on protocols like Synthetix and MarginFi. Its massive network of over 90 first-party data providers and $500M+ in staked value (TVS) provides strong security assurances for applications where data integrity is paramount.
Supra takes a different approach by prioritizing deterministic finality and cross-chain state synchronization through its novel Distributed Oracle Agreement (DORA) and Moonshot consensus. This results in a trade-off: while offering verifiable on-chain proofs and strong liveness guarantees, its initial focus has been on broader asset coverage and novel data feeds (like VWAP and volatility indices) for emerging L1s and L2s such as Sui, Aptos, and Polygon zkEVM.
The key trade-off: If your priority is minimizing latency for established, high-value assets on major EVM/Solana chains and you value a massive, battle-tested data provider network, choose Pyth. If you prioritize deterministic, provable data finality, require novel data types, or are building on a newer, high-performance non-EVM chain, choose Supra. For CTOs, the decision matrix is clear: Pyth for mainstream DeFi robustness; Supra for innovative data products on next-generation infrastructure.
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