ZK-Eval Benchmark Framework

Updated 17 September 2025

ZK-Eval Benchmark is a framework that evaluates zero-knowledge proof systems by assessing circuit power, runtime, and resource usage across cryptographic protocols.
It utilizes rigorous experimental methodologies, including constraint complexity analysis, runtime and memory profiling, and on-chain gas cost evaluation for various SNARK systems.
The benchmark extends to hardware acceleration, federated evaluations, VM optimizations, and LLM-enhanced ZK code synthesis to drive cost-efficient and scalable privacy-preserving applications.

ZK-Eval Benchmark is a domain-driven evaluative framework that measures and compares the efficiency, correctness, and resource consumption of zero-knowledge proof (ZKP) systems, primitives, and associated tools in practical cryptographic deployments and privacy-preserving applications. It encompasses systematic experimental protocols for SNARKs, ZK-friendly hash functions, accelerator architectures, federated evaluation procedures, virtual machine optimization, and LLM-based ZK code synthesis. Its adoption enables rigorous quantitative and qualitative analysis, guiding practitioners to the most performant and cost-efficient ZK building blocks for blockchain, verifiable machine learning, and cryptographic infrastructure.

1. Formal Definition and Scope

ZK-Eval is defined by its multidimensional assessment of zero-knowledge circuit primitives and end-to-end ZKP systems, focusing on:

Constraint complexity (“circuit power”) that directly corresponds to arithmetization size, trusted setup file requirements, and proving time.
Runtime performance and RAM consumption in setup, proof generation, and verification stages for different cryptographic curves and circuit templates.
Comparative evaluation across diverse ZK-friendly hash functions (Poseidon, Poseidon2, Neptune, GMiMC) and SNARK proving systems (Groth16, Plonk, Fflonk) implemented in frameworks such as circom-snarkjs on bn254.
End-user cost implications, including on-chain gas consumption for Ethereum Virtual Machine (EVM) and Hedera, as well as scalability trade-offs enabled by protocol-level optimizations like batch processing through sequencer roles (Guo et al., 3 Sep 2024).

ZK-Eval generalizes to benchmarks of hardware accelerators (e.g., SZKP ASIC (Daftardar et al., 12 Aug 2024), zkSpeed (Daftardar et al., 8 Apr 2025)), protocol-level privacy mechanisms (e.g., ZKP-FedEval (Commey et al., 15 Jul 2025)), compiler optimizations in zkVMs (Gassmann et al., 24 Aug 2025), and automated ZK code generation via LLMs (Xue et al., 15 Sep 2025).

2. Experimental Methodologies

ZK-Eval employs rigorous experimental methodologies rooted in circuit and systems benchmarking traditions:

Circuit Power Metric: Number of constraints in the compiled circuit, typically scaling as $2^k$ for circuit power $k$ , with constraints determined by the hash function parameters, SNARK arithmetization (R1CS vs. Plonkish), and circuit wiring depth (Guo et al., 3 Sep 2024).
Runtime Measurement: Time (seconds) for setup (e.g., .zkey file generation), proof generation, and verification, recorded for each stage and mapped to cryptographic primitives and protocol layers.
RAM Profiling: Memory use during circuit compilation and proof operations, with resource bottlenecks identified for hash functions with higher multiplicative complexity and rounds (e.g., Neptune, GMiMC).
Gas Cost Analysis: On-chain cost implications captured via native blockchain metrics (e.g., Ethereum gas), and impact of design optimizations (e.g., sequencer batch size $d_{\text{slot}}$ , and hash function substitutions) on per-transaction expenditure.
Tabular and Formulaic Modeling:

Stage	Metric	Example Value (Poseidon2, d=7, Groth16)
Setup	Time, RAM	~70s, >50% less RAM vs. GMiMC
Prove	Time, RAM	Fastest in class, lowest RAM
Verify	Gas (EVM/Hedera)	-73% (EVM), -26% (Hedera)

Standard formulas for Merkle tree state transitions and membership proofs are systematically applied:

Merkle tree generation: $(\mathbb{L}, \mathcal{H}) \rightarrow \mathcal{M}$
State update: $(\mathcal{C}, \pi_i) \rightarrow \mathcal{M}^*$
Membership verification: $(\mathcal{C}, r^*, \pi_i^*) \in \mathcal{M}^* \rightarrow \{\text{True}, \text{False}\}$

3. Hash Functions and SNARK System Benchmarks

ZK-Eval evaluates hash functions for ZK circuits emphasizing both arithmetic efficiency and facilitation of succinct proofs:

Poseidon/Poseidon2 consistently outperform Neptune and GMiMC in proof generation runtime and RAM usage under Groth16 arithmetization, especially for large Merkle trees (Guo et al., 3 Sep 2024).
Poseidon2, via circuit-level optimizations, reduces constraints and cuts proving time by nearly 60% when compared to MiMC baselines in slot-based sequencer batching.
In Plonk/Plonkish arithmetization, universality of setup is traded against constraint inflation and higher off-chain memory use.
Fflonk delivers lower on-chain verification costs (~30% less gas than Plonk), albeit with increased setup demands (PTAU power levels).

For SNARK system comparisons:

Groth16 achieves the best off-chain efficiency (compact proofs, low constraint count), at the expense of circuit-specific trusted setup.
Plonk and Fflonk introduce universal setup and tune down on-chain costs, with resource trade-offs evident.
Empirical evaluation on Sepolia (Ethereum), BNB Chain, and Hedera networks confirms cost-saving benefits when optimizing circuits and batch-processing via sequencer aggregators.

4. Hardware Acceleration and Systems-Level ZK-Eval

Recent ZK-Eval instantiations extend benchmarking to hardware accelerators for ZKP primitives, with architectural impact:

SZKP (Daftardar et al., 12 Aug 2024) is an ASIC accelerator, delivering conservative full-proof speedups ( $>400\times$ CPU, $3\times$ ASIC, $12\times$ GPU) by structured dataflows for MSMs and NTTs. Its constant-geometry NTT and pipelined MSM module address irregular memory access typical in prior designs.
zkSpeed (Daftardar et al., 8 Apr 2025) accelerates HyperPlonk protocol, architected for universal setup and small proof sizes ( $\sim$ 5KB). By streaming SumCheck/MLE updates and optimizing MSM for both sparse and dense field elements, zkSpeed achieves a geometric mean speedup of $801\times$ against CPU baselines, providing end-to-end acceleration over $366.46\,\mathrm{mm}^2$ and $2\,\mathrm{TB/s}$ bandwidth.
These accelerators validate that future ZK-Eval benchmarks require hardware-aware evaluation, including area, power density, and parallelism metrics.

5. Federated, Privacy-Preserving, and Virtual Machine Benchmarks

ZK-Eval informs protocol-level privacy auditing and virtual machine (VM) optimization:

ZKP-FedEval (Commey et al., 15 Jul 2025) establishes a benchmark for federated model evaluation: clients assert loss bounds ( $L_i<T$ ) via Groth16-based ZKPs constructed in Circom, enabling verifiable aggregation without revealing raw metrics. Benchmarks report client-side proof generation times ( $\sim$ 0.4s MNIST, $\sim$ 0.12s HAR) and succinct communication costs, with empirical scalability to 20 clients.
Compiler optimization on zkVMs (Gassmann et al., 24 Aug 2025)—spanning 64 LLVM passes and six optimization levels—demonstrates up to 45% execution time gains on RISC Zero via careful pass selection and zkVM-aware cost models. Conventional optimization heuristics (cache/prediction-centric) have limited or negative impact; proof-oriented autotuning and superoptimization are proposed for future zkVM-specific backends.

6. LLM Evaluation and Enhancement

ZK-Eval benchmarks extend to the assessment and augmentation of LLMs for ZK code generation (Xue et al., 15 Sep 2025):

ZK-Eval pipeline probes three levels—language syntax/semantics, gadget usage, and end-to-end program synthesis.
Experiments reveal LLMs achieve near-human accuracy ( $\sim$ 88%) for language knowledge but struggle with gadget-level reasoning (max 52% on Circom logical gadgets), and only 17–32% pass rates on human-eval style tasks.
ZK-Coder framework demonstrates systematic enhancement via constraint sketching (ZKSL), retrieval from knowledge bases, and interactive repair loops, elevating pass rates to 83–94% in Circom/Noir DSLs respectively.
Implications include reducing expertise barriers for ZK development, establishing trust in synthesized ZK code, and enabling correctness and reliability in privacy-preserving applications.

7. Implications and Future Directions

ZK-Eval Benchmark findings yield several research and deployment implications:

Adoption of optimized hash functions (Poseidon2) and well-benchmarked SNARK systems (Groth16, Plonk variants) substantially reduces proving times and on-chain costs in privacy-preserving smart contracts.
Sequencer-centric batch processing is a key architectural pattern, validated across testnets for cost-efficient mixer protocols.
Hardware acceleration solutions, notably SZKP and zkSpeed, are vital for moving ZKP protocols to real-time and large-scale applications, including MLaaS and blockchain consensus.
Compiler toolchains and VM backends require proof-aware optimization passes and autotuning, shifting away from hardware-centric paradigms.
LLM benchmarks and agentic augmentations (ZK-Coder) are necessary for trustworthy, automated generation of ZK circuits, lowering software development complexity.
Prospective research should expand ZK-Eval toward transparent proof systems (STARKs), broader accelerator implementations, and integration with secure aggregation in federated systems.

The ZK-Eval Benchmark is thus foundational for comparative, protocol-oriented, and system-level analysis of zero-knowledge infrastructure across modern cryptographic landscapes.