L1 comparison
The promise of a World Computer and the emergence of a Blockchain Singularity has far reaching consequences in technology, sociology, economics, politics, communication, entertainment, and most aspects of our digital lives. As the industry is one of rapid innovation and progress, and as projects constantly and dynamically change, it's important to take stock, every now and then, to note how we're doing, and to check if we're on track to achieve the goals of decentralization, scalability, usability, and functionality.
The industry is now moving out of its infancy, which is seen by the increasing number of smart contract developers, rather than core protocol developers, and users wanting to fully engage with a platform, rather than simply sending transactions back and forth. The shift away from simple payment systems, towards Web3 is well on its way, and it's within this scope that we attempt to map the blockchain landscape on this page.
We compare top performing blockchain projects across a number of metrics that we expect to yield a 'good' Web3 experience under the categories of core protocol, developer experience, and user experience.
Unless otherwise stated, all data is correct as of December 9th 2022. Metrics are explained and references are given below.
Base comparisons
Here we compare standard metrics that are used to measure performance of the core protocol of popular blockchain projects. Note that these metrics should not always be taken at face value. While references are listed below to note where the figures can be found, it's not always clear how these figures are computed. Additionally, parts of different projects may have the same name, but often are constructed differently (most notably, transactions), and so should not be compared blindly like-for-like. The a16z blog has a nice article describing how the industry should think about metrics.
| Metrics / L1 | Average TPS | Average finality | Average block time (seconds) | Average tx Cost | Average energy consumption wh/tx | Size of network (nodes) | On-Chain storage cost |
|---|---|---|---|---|---|---|---|
| ICP | 5382 (update calls) | 1.4secs | 0.936 | $0.0000022 | 0.008 | 823 | $5 (3.95T cycles x 1XDR) |
| Algorand | 15.5 | 3.5secs | 3.5 | $0.00025 | 2.7 | 1530 | |
| Avalanche | 49.52 | 2.3secs | 2.3 | $0.0066 (C-Chain only) | 4.76 | 1195 | $206,875 (15,62 5AVAX) |
| Cardano | 2.37 | 20 | $0.1 | 51.59 | 1050 | $17,035 - $113,507 (53,236 – 354708ADA) | |
| Ethereum | 11.1 | 15mins | 12.08 | $2.39 | 6.29 | 6562 | $15,494,409 (12,643.75 ETH) |
| Near | 8.25 | 3.3secs | 1.11 | $0.0031 | 0.036 | 798 | |
| Solana | 286 (non-voting calls) | 5-12secs | 0.542 | $0.000026 | 0.166 | 1872 | $48,625 (3,477.69 SOL) |
- Average TPS measures the transactions processed per second - note that the interval over which these are measured does vary across chains. The dollar amounts are computed by converting the native token cost cycles/gas/fee needed per transaction, to USD given the exchange rate on December 9th 2022.
- Average finality refers to the amount of time that passes between the proposal of a new valid block containing transactions until the block has been finalized and its content is guaranteed to not be reversed or modified (for some blockchains, e.g., Bitcoin, this guarantee can only be probabilistic).
- Average block time refers to the amount of time between blocks (per subnet on the IC)
- Average tx Cost measures the cost of a transaction. Note that the definition of 'transaction' varies widely across chains, where some are described below. The dollar amounts are computed by converting the native token cost cycles/gas/fee needed per transaction, to USD given the exchange rate on December 9th 2022. (Cardano and Ethereum figures found in Messari dashboard.)
- Average energy Consumption measures the network energy consumption to process a transaction (measured in watt hours)
- Size of network (nodes) notes the number of nodes currently making up the network
- On-chain storage cost gives the dollar cost and the native token cost of storing 1GB of data per year on chain.
Comparing developer experience
Whether they were writing games, operating systems or text editing applications, in the 70s, 80s and early 90s, developers always had to face limitations imposed by hardware. Applications were constrained to accessing a few kilobytes of memory through small stacks and heaps, using limited (and constantly changing) instruction sets, and using significant amounts of power to run instructions. The history repeats itself in the blockchain landscape these days. Application developers are limited to stack sizes of a few kilobytes to several megabytes at best. Persistent storage is expensive and limited. Programmers are bound to using cumbersome APIs that make hidden assumptions in terms of numbers of executed instructions. And, moreover, most chains operate inefficiently, burning too much power per executed transaction. This not only limits the types of applications that can be deployed on chain, but also increases development and testing time (and cost).
As opposed to all existing blockchains, the IC brings modern programming to on-chain developers, allowing them to use time for creativity rather than fixing memory packing issues or spreading computation in small iterations that do not hit instruction limits. The IC programming model offers orthogonal persistence, large stack and heap spaces (4GB), stable storage of 48GB (with plans for increase) in mainstream languages, such as Rust, or even Python.
| Metrics / L1 | Stable tx cost | HTTPs outcalls | Smart contract language support | Max stack size | Max persisted memory (per smart-contract) | Active developers (full-time / monthly) | Active repositories |
|---|---|---|---|---|---|---|---|
| ICP | ✅ | ✅ | Motoko (native), Rust, TypeScript, Python | 4 GiB | 52 GiB | 121 / 406 | 3973 |
| Algorand | ❌ | ❌ | Teal (native), Python | 4 MB | 1 MB | 52 / 192 | 438 |
| Avalanche | ❌ | ❌ | Solidity | 95 / 332 | 1901 | ||
| Cardano | ❌ | ❌ | Plutus (native), Haskell | 163 / 465 | 1252 | ||
| Ethereum | ❌ | ❌ | Solidity (native), Vyper, Yul, FE | 32 KiB | 2^261 B (however, 15,494,409$ per GiB) | 1873 / 5734 | 29117 |
| Near | ❌ | ❌ | Rust, Javascript | 256 KiB | 32 KiB | 205 / 781 | 5352 |
| Solana | ❌ | ❌ | Rust C, C++ | 383 / 2082 | 6137 |
- Stable tx cost provides the ability to have predictable costs for computation
- HTTPs outcalls is the ability to communicate directly with Web2 services (outside of the network)
- Max stack size is the maximum size the stack can grow for smart contracts and serves as a measure for the complexity of code that is supported by each platform
- Max persisted memory is the maximum size of persisted memory supported by each platform. Persisted memory is preserved across individual function calls
- Active developers counts the number of developers who made commits on more than 10 days in a month (full-time) or original code authors who made commits in a given month. Source Electric Capital. Figures true as of 15/12/22
- Active repositories are sourced from the Electric Capital crypto ecosystems list. Figures true as of 15/12/22
Comparing user experience
It is widely accepted that the Web3 user experience needs massive development before mainstream adoption is likely. In this section we start to map out key metrics for Web3 usability. First and foremost is privacy, identity management and authentication. On many projects, every interaction that a user ever makes can be traced and monitored. While transparency is good for some things, we argue that this is a severe hindrance to adoption. Financial privacy and the freedom to interact should be paramount. We also note the tools needed to interact with a project. We see this as a measure of accessability and openness to onboarding. Finally, we include metrics about participation in the network. A large draw of Web3 is the fact that users can become owners and drivers of the platform. Here we include the percentage of native tokens staked as a measure of user confidence and participation in the project.
| Metrics / L1 | Privacy-preserving authentication | Prerequisites to use | Staking ratio | Monthly active wallets |
|---|---|---|---|---|
| ICP | ✅ | Browser | 73.89% | 93k |
| Cardano | ❌ | Browser, browser extension, tokens | 71.58% | |
| Avalanche | ❌ | Browser, browser extension, tokens | 61.78% | 390k |
| Algorand | ❌ | Browser, browser extension, tokens | 51.17% | |
| Ethereum | ❌ | Browser, browser extension, tokens | 13.57% | 15m |
| Near | ❌ | Browser, browser extension, tokens | 43.19% | |
| Solana | ❌ | Browser, browser extension, tokens | 68.59% | 655k |
- Privacy-preserving authentication notes whether a project allows privacy-preserving interactions with the blockchain.
- Prerequisites to use lists what is needed to interact with the project
- Staking ratio gives the percentage of native tokens that are staked in the protocol. The staking ratio metrics are from Staking Rewards and are correct as of 19.12.2022
- Monthly active wallets counts the wallet addresses that sent or received native currency in a given month (December 2022)
A note about average transactions cost
- Algorand: https://metrics.algorand.org/#/protocol/, explanation: Average transaction fee of all transactions in the selected time period. Algorand fees
- Cardano: Cardano fees Fees are constructed around two constants (a and b). The formula for calculating minimal fees for a transaction (tx) is a times size(tx) + b, where:
- a/b are protocol parameters
- size(tx) is the transaction size in bytes
- Solana: Solana fees
A note about finality
It is sometimes unclear what exactly different projects mean by finality, so we note our references here and describe how the above figures are computed.
- For ICP, finality calculated as described in the whitepaper in section 5.11.6.
- For Near, we use the fact finalization occurs after 3 blocks (as stated in the docs) and use the current block time of 1.1 seconds from the explorer.
- All other finality figures for now come from the finality section here.
A note about Decentralization
Decentralization is key to make web3 dapps run in a trustless manner. However, decentralization has many dimensions and cannot be understood and quantified using a single number or coefficient. One can distinguish between a) the decentralization of the entities running the machines on top of which a protocol runs, b) the decentralization of the consensus and sharding mechanism, c) the governance system, the owners of liquid tokens etc. In this case, the whole is greater than the sum of its parts and one cannot understand decentralization as a single discussion on each of these topics.
See Decentralization Wiki Page for more details.
References
- ICP : IC Dashboard
- ADA : Cardano explorer and cexplorer
- AVAX : Snowtrace and Avalanche explorer
- ALGO : Algorand website and Algorand metrics site
- ETH : Etherscan
- NEAR : Near explorer and Near docs
- SOL : Solana website and Solana beach