Performance of WebAssembly Runtimes in 2026
summary
WebAssembly runtimes are generally improving in performance, though the rate of progress varies significantly across different implementations. While some runtimes show steady annual gains, others experience dramatic leaps or stagnate. The most significant performance boosts come not from general runtime optimizations, but from the adoption of specific ISA extensions like wide_arithmetic, which allow Wasm to approach native execution speeds for compute-heavy tasks.
key points
- Runtime performance is inconsistent, with Wasmtime showing steady improvement, Bun making a massive leap between 2025 and 2026, and WAMR remaining consistently fast.
- The introduction of wide_arithmetic instructions provides a substantial speedup for cryptographic workloads, drastically reducing the gap between WebAssembly and native code in supported runtimes like Wasmer and Wasmtime.
- The choice of runtime and compilation mode, such as AOT (Ahead-of-Time) versus interpreter mode, remains critical because performance varies wildly between the fastest and slowest current options.
who should read it
Software engineers and developers who implement CPU-intensive workloads in WebAssembly and need to choose the most performant runtime for their deployment.
discussion positive
consensus
Commenters generally agree that while WASM runtimes are approaching native performance, benchmarks are often flawed or misleading. There is a shared skepticism toward extreme performance claims, such as 10,000x speedups, and the use of unrecognized optimization flags. Users emphasize that the trade-off of a 2x native speed difference is acceptable given the sandboxing and portability benefits.
top insight
Performance gaps in WASM runtimes often stem from specific codegen gaps, such://as int8 dot products and angular distances where runtimes leave performance on the table. Addressing these specific microbenchmarks in the backend/codegen work can help close the gap to native execution. This provides a concrete path for improving runtime efficiency beyond general optimization.
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