AppBench B
AI benchmark · Multi-Domain
- Publisher
- AfterQuery
- Domains
- Multi-Domain
- On X
- @afterquery ↗
- Tier
- B why
- Snapshot
Latest from AfterQuery on X
@afterquery ↗What is AppBench?
App-Bench measures how much required functionality AI coding assistants and web-app builders deliver when generating full-stack web applications from a single prompt without human code edits.
Coding mostly
“App-Bench is a benchmark designed to evaluate how well AI-driven tools can automatically build modern web applications.”
afterquery.com/leaderboard/app-bench ↗
Tier B
Established: 15 or more models scored, or curated RL directories rate it.
How much stands behind a tier: usage, who backs it, a confirmed line from its own site or paper, and a clear read on X. High with three of the four, medium with two, low otherwise.
- 3 models scored
- built by AfterQuery
- in curated RL directories (9 pts)
How we got to tier B
- Adoption: 3 models scored; built by AfterQuery. Tier C.
- Not yet read on X.
- In curated RL directories: 9 points, a floor of B. Raised to B.
- Confirmed: a line from its own site or paper places it.
A clear view on X moves a tier one step up or down; most entries don't move. How tiers work.
Training methods
Methods AppBench uses, offers or builds for, with public evidence: its own website says so, or two independent sites do.
- RLVR Reinforcement Learning with Verifiable Rewards On its own website
“Launching the RLVR stage on Tinker:”
afterquery.com - GRPO Group Relative Policy Optimization On its own website
“Not all tasks are equally useful for RL. Tasks the model solves consistently or fails every time provide no learning signal. We select tasks where the SFT checkpoint has a 10–80% solve rate. These are the tasks where GRPO can compute meaningful advantages: some attempts succeed, some fail, and the model can learn from the difference.”
afterquery.com - SFT Supervised Fine-Tuning On its own website
“Our training pipeline had two sequential stages. The first stage teaches the model what good terminal-agent behavior looks like via SFT on gold trajectories. The second stage uses RL to push the model toward solving harder problems it only partially gets right after SFT.”
afterquery.com
Where AppBench sits
Its realm in the universe, live. Hover a star for its name; click to open it. Open it in the universe
Next: browse all benchmarks. Entry from the RL Research daily scrape of public sources, 2026-10-11 snapshot.