eth_estimateL1Fee - Estimate L1 data posting fee

Estimate L1 data posting fee on the Mantle network. This method helps calculate the additional cost of posting transaction data to Ethereum L1 via EigenDA as part of Mantle's modular L2 architecture.

OverviewCopy Anchor Link

Mantle's modular L2 is an optimistic rollup that periodically submits batched transaction data to Ethereum L1 through EigenDA for data availability. While most transaction execution happens on L2 with low gas costs paid in MNT, there's an additional fee component for the L1 data availability costs.

curl -X POST https://api-mantle-mainnet.n.dwellir.com/YOUR_API_KEY \\
      -H "Content-Type: application/json" \\
      -d '{
        "jsonrpc": "2.0",
        "method": "eth_estimateL1Fee",
        "params": [
          {
            "from": "0x742d35Cc6634C0532925a3b844Bc9e7595f0bEb5",
            "to": "0x833589fcd6edb6e08f4c7c32d4f71b54bda02913",
            "gas": "0x5208",
            "gasPrice": "0x3b9aca00",
            "value": "0x1bc16d674ec80000",
            "data": "0x"
          }
        ],
        "id": 1
      }'

Implementation ExampleCopy Anchor Link

Understanding L1 Fees on MantleCopy Anchor Link

Fee ComponentsCopy Anchor Link

1. L2 Execution Fee: Standard gas cost for transaction execution on Mantle (paid in MNT)
2. L1 Data Fee: Cost of posting transaction data to Ethereum L1 via EigenDA for data availability

Data Size ImpactCopy Anchor Link

L1 fees scale with transaction data size:

• Simple transfers: Minimal L1 fee
• Contract interactions: Moderate L1 fee based on calldata
• Large data payloads: Significant L1 fee component

Cost Optimization StrategiesCopy Anchor Link

1. Minimize Data: Reduce unnecessary data in transactions
2. Batch Operations: Combine multiple operations into single transaction
3. Use Events: Emit events instead of storing large data on-chain
4. Optimize Encoding: Use efficient data encoding methods
5. Leverage EigenDA: Benefit from efficient data availability

Common Use CasesCopy Anchor Link

1. Transaction Cost PlanningCopy Anchor Link

// Plan transaction costs before execution
async function planTransactionCosts(transactions) {
  const feeEstimator = new MantleFeeEstimator(rpcUrl);
  const plans = [];
  
  for (const tx of transactions) {
    const breakdown = await feeEstimator.getFullFeeBreakdown(tx);
    plans.push({
      transaction: tx,
      estimatedCost: breakdown.totalEstimatedCost,
      l1FeeRatio: breakdown.breakdown.l1FeePercentage,
      recommendation: breakdown.breakdown.l1FeePercentage > 70 ? 
        'Consider batching or data optimization' : 'Cost efficient'
    });
  }
  
  return plans.sort((a, b) => a.estimatedCost - b.estimatedCost);
}

2. Data Optimization AnalysisCopy Anchor Link

def optimize_contract_call_data(base_transaction, data_variations):
    """Find most cost-effective data encoding"""
    
    fee_estimator = MantleFeeEstimator(rpc_url)
    results = []
    
    for name, data in data_variations.items():
        tx = {**base_transaction, 'data': data}
        breakdown = fee_estimator.get_full_fee_breakdown(tx)
        
        results.append({
            'encoding': name,
            'data_size': len(data) // 2 - 1,  # Hex bytes
            'total_cost': breakdown['total_estimated_cost'],
            'l1_fee': breakdown['l1_fee'],
            'efficiency': breakdown['l1_fee'] / (len(data) // 2 - 1)
        })
    
    # Sort by total cost
    return sorted(results, key=lambda x: x['total_cost'])

Related MethodsCopy Anchor Link

• eth_estimateGas - Estimate L2 execution gas
• eth_gasPrice - Get current L2 gas price
• eth_feeHistory - Historical fee data

Need help? Contact our support team or check the Mantle documentation.