LadderBench

A graded capability ladder for evaluating (large) language models: 150 multiple-choice questions organized in 5 difficulty levels (30 each), strictly ordered from easy to expert. Because every level is scored separately, LadderBench shows where a model's abilities break down, not just an average score - two models with the same overall accuracy can have very different level profiles.

Motivation

Averaged benchmark scores hide useful structure: a model can score 70% by acing easy questions and guessing on hard ones, or by being uniformly mediocre. LadderBench's ladder design exposes that difference:

Level	Name	What it probes	Example
1	easy	basic facts, single-step arithmetic	"What is 7 + 8?"
2	basic	everyday knowledge, word problems, gentle traps	"Which month has at least 28 days?"
3	intermediate	reading comprehension, applied reasoning, classic traps	bat & ball ($0.05), lily pads (day 29)
4	hard	multi-step math, sequences, work rates, percentages	clock-angle 7.5°, chicken/rabbit legs
5	expert	competition math, logic puzzles	2^50 mod 7, trailing zeros of 100!, knights & knaves

Expected behavior of a healthy model: a roughly monotonic decline from level 1 to level 5. A model that scores better on level 5 than level 4 is a signal of noise (too few samples) or a poorly calibrated level - which is exactly what this design is meant to catch.

Data format

One JSON object per line (JSONL), identical schema in every file:

{
  "id": "l5_12",
  "level": 5,
  "category": "math",
  "question": "How many trailing zeros does 100! (100 factorial) have?",
  "choices": ["10", "21", "24", "25"],
  "answer": "C"
}

Fields:

• id - stable unique identifier (l<level>_<nn>)
• level - 1..5
• category - arithmetic / knowledge / common-sense / reading / math / logic / sequence / probability / trick / reasoning / safety
• question - the prompt shown to the model (passages are embedded in the text)
• choices - exactly 4 answer options, presented to the model as (A)-(D)
• answer - the gold letter, one of A/B/C/D

Splits

File	Rows	Level
level1_easy.jsonl	30	1 easy
level2_basic.jsonl	30	2 basic
level3_intermediate.jsonl	30	3 intermediate
level4_hard.jsonl	30	4 hard
level5_expert.jsonl	30	5 expert

Evaluation protocol

The recommended protocol (implemented in the standalone runner):

1. Present the question with options lettered (A)-(D); instruct the model to answer with the letter only.
2. Parse the selected letter from the response (robust to "(B)", "B.", etc.).
3. Score 1.0 for an exact letter match, else 0.0; report per-level accuracy.
4. Use temperature 0 (greedy) for reproducibility.

Important: the random-guessing baseline is 25% (4 options). With 30 questions per level, each question is worth ~3.3 percentage points - treat per-level gaps under ~10 points cautiously and report the guessing baseline alongside results.

Standalone runner

This folder ships run_ladderbench.py, a zero-dependency runner (Python 3.8+ standard library only) so anyone can evaluate any model on LadderBench without installing anything else. It works with any OpenAI-compatible chat endpoint:

# Ollama (default endpoint)
python run_ladderbench.py --model qwen2.5:3b-instruct-q4_K_M

# LM Studio
python run_ladderbench.py --model my-model --base-url http://127.0.0.1:1234/v1 --api-key lmstudio

# llama.cpp llama-server
python run_ladderbench.py --model any-name --base-url http://127.0.0.1:8080/v1

# OpenAI / other hosted APIs
python run_ladderbench.py --model gpt-4o --base-url https://api.openai.com/v1 --api-key sk-...

# Quick pass: only hard levels, 5 questions each
python run_ladderbench.py --model <model> --levels 4,5 --limit 5

What it does: letter-answer prompting over every question, robust letter parsing, per-level accuracy table, and a JSON report (results_<model>_<timestamp>.json) containing a full per-question audit trail (prediction, gold, raw output) for error analysis.

Protocol notes:

• temperature 0 (greedy) by default for reproducibility;
• prompt asks for the letter only; parsing tolerates "(B)", "B.", etc.;
• report the 25% guessing baseline next to results;
• the audit file lets you compute per-category scores too (see category).

Extending / contributing

Add rows to any level file following the schema above (keep ids unique), or create a new level file and register it in the loader. When adding questions: verify the gold answer independently, make distractors plausible, and avoid answer-letter position bias by distributing correct letters across A-D.

License

CC-BY-4.0. Questions are original works written for this benchmark; classic puzzle ideas (bat & ball, lily pads, knights & knaves) are folklore and are rephrased here.