Alternatives
Products that do what LLM Debate Benchmark does
- 1AN
2014 · saysaw.org
- 2AA
2015 · en.arguman.org
- 3AD
Ever wish you could get the best arguments for both sides of a debate? I built an AI-powered debate platform that pits language models against each other on controversial topics. Each AI is randomly assigned a side (pro/con). You vote before and after to see if you were persuaded. Most content today presents lopsided arguments. They provide strong points for one side, weak ones for the other. This project aims to surface the strongest arguments from both sides, using LLMs to simulate a fair debate. With enough usage, I want to use it to benchmark LLMs. My hypothesis is that randomly…
2025 · bot-bicker.vercel.app
- 4LB
2025 · github.com
- 5CL
2023 · convoclash.net
- 6CD
2016 · citizendebate.org
- 7PA
Dec 2025 · oddbit.ai
- 8BA
2017 · ballotter.com
- 9BD
2017 · bigquery.cloud.google.com
- 10TF
2011 · barkles.com
- 11DA
2017 · debategate.net
- 12

- 13LT
2025 · github.com
- 14LD
2024 · github.com
- 15AA
2015 · arguman.org
- 16LD
2024 · github.com
- 17FC
2012 · debate.quipvideo.com
- 18LC
Sep 2025 · github.com
- 19LR
Sep 2025 · github.com
- 20

3d ago · redactle.net
- 21DS
2012 · openedcaptions.com
- 22DU
2023 · github.com
- 23PP
I've been working on applying LLMs to long-context, verifiable problems over the past year, and today I'm releasing a benchmark of 62,000 pencil puzzles across 94 types (sudoku, nonori, slitherlink, etc.). The benchmark also allows for intermediate checks /rule breaks for all varieties at any step. I tested 51 models against a subset (300 puzzles) in two modes: single-shot (output the full solution) and agentic (iterate with verifier feedback). Some results: - Best model (GPT 5.2@xhigh) solves 56%. (~ half the puzzles are unsolved by any model) - Agentic solves average 29 turns. The…
Mar 2026 · ppbench.com
- 24MC
2018 · github.com
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