BridgeBenchthe vibe coding benchmark
BridgeBench measures what actually matters when you ship with AI: speed, cost, and code quality — benchmarked direct from every provider. It's now a BridgeMind community project, and we're recruiting builders to help make it the world's number-one benchmark for vibe coding.
BridgeBench results have driven the conversation around AI coding performance across X — and now the methodology, harness and data are opening up for the whole community to build on.
One benchmark,
every dimension that ships
BridgeBench calls model APIs directly from the source — bypassing aggregators — so every number reflects the real model, not the middleman.
Speed
Tokens/sec, time-to-first-token and cost, measured direct from each provider — no aggregator latency.
Algorithms
Data structures, dynamic programming and graph problems graded on hidden test cases.
Debugging
Find and fix realistic bugs in existing code without breaking anything else.
Refactoring
Restructure code with AST-backed checks that the refactor actually happened.
Security
Vulnerability detection, input sanitization, auth and crypto correctness.
UI / Creative HTML
Browser-validated interactive UI generation scored for completeness and polish.
Reasoning
Multi-step problems graded on both the answer and the evidence behind it.
Hallucination & Pushback
Factual accuracy plus the discipline to reject nonsensical premises.
Cost
Cost-per-correct-solution — the metric that actually matters when you ship.
The world's #1
vibe coding benchmark
Vibe coders don't ask models for isolated functions — they ask models to behave like strong partners across the full shipping loop. v3 expands BridgeBench's deterministic core into the workflow-shaped tasks that actually decide whether a model can build with you.
Plus DGX Spark integration — the first leaderboard to compare cloud API models against locally-hosted open-weight models on the same chart, with GPU, power and energy-per-token metrics.
- 01Code review — catch real bugs like a senior engineer
- 02Spec generation — rough idea → implementation-ready PRD
- 03Clarification — ask the right questions when underspecified
- 04Repo orientation — find the right files in a real codebase
- 05Test repair — close the loop without collateral damage
- 06Writing — PR descriptions, migration notes, release docs
- 07Integration — wire Stripe, auth, email and storage correctly
- 08UX polish — turn functional UI into product-grade UI
- 09Launch readiness — spot the final production blockers
- 10Multi-step tool use — orchestrate inspect → edit → run → observe
A benchmark builders
can trust because they own it
The strongest benchmark is one the whole field can inspect, reproduce and extend. BridgeBench is now a BridgeMind community project — open methodology, open harness, open results — built in public with the people who ship with these models every day.
Reproducible by design: an append-only run journal, version-pinned scoring, resumable runs, and a provider abstraction where adding a new model is two files. Nothing hidden, nothing hand-waved.
Help us build
the standard for AI-native work
Whether you design eval tasks, run models on your own hardware, or want to sharpen the methodology — there's a clear way in.
Design benchmark tracks
Author tasks and rubrics for review, clarification, writing, launch readiness and more.
Local inference
Extend beyond DGX Spark to RTX, Apple Silicon and AMD via Ollama, vLLM and llama.cpp.
Add models
Integrate new frontier and open-weight models — a provider is two files.
Submit reference results
Run the suite on your hardware and contribute results to the public leaderboard.
Methodology & docs
Sharpen scoring, harden the harness, and document how it all works.
Community leaderboard
Help build the web UI for rankings, filtering and model comparisons.
Benchmark the future with us.
Live leaderboards, methodology and data are open at bridgebench.ai. Come build the benchmark the agentic era deserves.