Gas optimization

Use real-time gas statistics from the Relayer to submit transactions at the selected gas price for cost and confirmation speed.

Gas pricing fundamentals

Every MultiversX transaction specifies a gasPrice in atomic EGLD units (10^-18 EGLD) and a gasLimit in gas units:

fee = gasPrice * gasUsed

The network enforces a minimum gasPrice of 1,000,000,000 (10^9). Validators prioritize higher-priced transactions, so the minimum price during congestion risks delayed inclusion. PPU (price per unit) is the effective cost per gas unit. For transactions with a non-empty data field, each byte costs an additional 1,500 gas units. PPU accounts for this overhead, so it compares costs across transaction types more accurately than raw gasPrice.

The sliding window

Gas statistics are computed over a 30-minute sliding window of 10-second buckets (180 buckets total). As time advances, the oldest bucket expires and the window shifts forward.

Metric	Measures	Spam sensitivity
`avg`	Mean over all transactions in window	Biased low by spam volume
`bucketAvg`	Mean of per-bucket averages	Less sensitive to volume spikes
`p50`	50th percentile gas price	Often at minimum during spam
`p75`	75th percentile	Spam-resistant; typical legitimate price
`p90`	90th percentile	Upper bound; high congestion

Spam attacks cluster at the minimum price. The p75 percentile filters these out and reflects what legitimate transactions paid, making it a stronger default for most applications.

Choosing the right percentile

Scenario	Recommended	Rationale
Background operations	`p50` or `bucketAvg`	Cheapest with historical inclusion
Standard user swaps	`p75`	Reliable inclusion without overpay
Time-sensitive arbitrage	`p90`	Competitive even during congestion
Mempool pressure > 0.5	Shift up one tier (e.g., `p75` → `p90`)	Queue is growing; higher price improves ordering
Shard has no transactions	`1,000,000,000` (minimum)	Shard is idle; minimum is sufficient

When a shard reports all gas price fields as 1000000000 and PPU as 0, it has no recent transaction history. The minimum price is sufficient.

Using mempool pressure

The networkStats stream on wss://relayer.xoxno.com/ws/stats pushes updates at adaptive intervals. Each message includes mempool.pressure per shard, representing current queue depth relative to throughput capacity.

function selectPercentile(pressure, baseline = 'normal') {
  if (pressure > 0.75) return 'p90';   // heavy congestion
  if (pressure > 0.50) return 'p75';   // moderate
  if (pressure > 0.25) return 'p75';   // light
  return baseline === 'low' ? 'bucketAvg' : 'p75';
}

Block time expectations

Transaction type	Current network	After Supernova (600 ms blocks)
Same-shard	~6 seconds	~600 ms
Cross-shard	~12 seconds (two block rounds)	~1.2 seconds

The blockTimeMsP50ByShard field in networkStats gives the current median block time per shard. For cross-shard transactions, sum both shards.

Adaptive gas pricing example

cURL
TypeScript
Python
Rust

printf '{"action":"subscribe","topic":"gasStats"}\n' \
  | websocat wss://relayer.xoxno.com/ws

const ws = new WebSocket('wss://relayer.xoxno.com/ws');
const gasStats = new Map(); // shard → latest stats

ws.onopen = () => {
  ws.send(JSON.stringify({
    action: 'subscribe', topic: 'gasStats'
  }));
};

ws.onmessage = (e) => {
  const msg = JSON.parse(e.data);
  if (msg.type === 'gasStats') {
    gasStats.set(msg.data.shard, msg.data);
  }
};

function getGasPrice(shard, priority = 'normal') {
  const stats = gasStats.get(shard);
  if (!stats) return 1_000_000_000; // network minimum

  const p = stats.gasPrice.percentiles;
  switch (priority) {
    case 'low':    return stats.gasPrice.bucketAvg;
    case 'normal': return p.p75;
    case 'high':   return p.p90;
    default:       return p.p75;
  }
}

import asyncio
import json
import websockets

gas_stats: dict[int, dict] = {}

async def listen():
    async with websockets.connect("wss://relayer.xoxno.com/ws") as ws:
        await ws.send(json.dumps({
            "action": "subscribe", "topic": "gasStats"
        }))
        async for msg in ws:
            data = json.loads(msg)
            if data.get("type") == "gasStats":
                gas_stats[data["data"]["shard"]] = data["data"]

def get_gas_price(shard: int, priority: str = "normal") -> int:
    stats = gas_stats.get(shard)
    if not stats:
        return 1_000_000_000

    p = stats["gasPrice"]["percentiles"]
    if priority == "low":
        return stats["gasPrice"]["bucketAvg"]
    elif priority == "high":
        return p["p90"]
    else:
        return p["p75"]

use futures_util::{SinkExt, StreamExt};
use tokio_tungstenite::{connect_async, tungstenite::Message};

let (mut ws, _) = connect_async("wss://relayer.xoxno.com/ws").await?;

let subscribe = serde_json::json!({
    "action": "subscribe",
    "topic": "gasStats"
})
.to_string();

ws.send(Message::Text(subscribe.into())).await?;

while let Some(message) = ws.next().await {
    let text = message?.into_text()?;
    let event: serde_json::Value = serde_json::from_str(&text)?;

    if event["type"] == "gasStats" {
        println!("{}", event["data"]["gasPrice"]["percentiles"]["p75"]);
    }
}

Practical guidelines

Do:

Subscribe to gasStats before your first transaction; the first message arrives immediately
Default to p75 for user-facing swaps
Escalate to p90 during periods of high network activity
Use bucketAvg for background or non-urgent operations

Avoid:

Using the raw avg field (spam volume biases it low)
Setting gasPrice below 1,000,000,000 (the network rejects it)
Caching gas stats beyond a few seconds; conditions shift within a single block

Gas statistics

Full gasStats message schema and field descriptions.

Network stats

networkStats stream with mempool pressure data.

Get started

Resources

FAQs

API overview

Gas pricing fundamentals

The sliding window

Choosing the right percentile

Using mempool pressure

Block time expectations

Adaptive gas pricing example

Practical guidelines

Gas statistics

Network stats

​Gas pricing fundamentals

​The sliding window

​Choosing the right percentile

​Using mempool pressure

​Block time expectations

​Adaptive gas pricing example

​Practical guidelines

​Related pages

Gas statistics

Network stats

Gas pricing fundamentals

The sliding window

Choosing the right percentile

Using mempool pressure

Block time expectations

Adaptive gas pricing example

Practical guidelines

Related pages