Codex Candy Eval Benchmark

Skill by ara.so — Codex Skills collection.

A Python-based benchmarking tool that evaluates OpenAI Codex/GPT models using a standardized candy math puzzle. Measures reasoning token usage against answer correctness to assess model performance degradation patterns (降智测试).

What It Does

Batch tests Codex CLI with a specific candy distribution math problem
Tracks reasoning token consumption across different effort levels
Calculates accuracy rates by detecting the correct answer (21)
Supports multiple reasoning effort levels: low, medium, high, xhigh
Zero third-party dependencies - pure Python with Codex CLI integration

Prerequisites

You must have Codex CLI installed and authenticated:

# Install Codex CLI (follow official instructions)
# Authenticate with your OpenAI credentials
codex auth login

Installation

git clone https://github.com/haowang02/codex-candy-eval.git
cd codex-candy-eval

No additional dependencies required - uses only Python standard library.

Key Commands

Basic Usage

# Run single test with default settings (medium reasoning)
python codex_candy_eval.py

# Test specific model with high reasoning effort
python codex_candy_eval.py -m gpt-5.5 -r high

# Run 10 tests to get statistical accuracy
python codex_candy_eval.py -n 10

# Full benchmark with all parameters
python codex_candy_eval.py -m gpt-5.5 -r xhigh -n 20

Command-Line Arguments

-m, --model: Codex model name (e.g., gpt-5.5, gpt-4, defaults to local CLI default)
-r, --reasoning-effort: Reasoning level - low, medium, high, xhigh (default: medium)
-n, --tests: Number of test iterations (default: 1)

The Candy Puzzle

The benchmark uses a standardized math problem:

"小明有5颗糖，小红比小明多3颗，小刚比小红多2倍。问小刚有多少颗糖？"

(Xiaoming has 5 candies, Xiaohong has 3 more than Xiaoming, Xiaogang has 2 times more than Xiaohong. How many candies does Xiaogang have?)

Correct Answer: 21

The script validates responses by checking if "21" appears as a standalone number in the output.

Code Examples

Running Programmatic Benchmarks

import subprocess
import json
import re

def run_codex_eval(model="gpt-5.5", reasoning="medium", num_tests=5):
    """Run candy eval and parse results"""
    cmd = [
        "python", "codex_candy_eval.py",
        "-m", model,
        "-r", reasoning,
        "-n", str(num_tests)
    ]
    
    result = subprocess.run(cmd, capture_output=True, text=True)
    return result.stdout

def extract_accuracy(output):
    """Extract accuracy percentage from output"""
    match = re.search(r'(\d+\.?\d*)%', output)
    return float(match.group(1)) if match else None

# Compare reasoning levels
for effort in ['low', 'medium', 'high', 'xhigh']:
    output = run_codex_eval(reasoning=effort, num_tests=10)
    accuracy = extract_accuracy(output)
    print(f"{effort}: {accuracy}% accuracy")

Typical Output Structure

Running test 1/5...
Reasoning tokens: 1234
Response contains correct answer: ✓

Running test 2/5...
Reasoning tokens: 1156
Response contains correct answer: ✗

...

Results:
Total tests: 5
Correct: 3
Accuracy: 60.0%
Average reasoning tokens: 1195

Custom Test Implementation

#!/usr/bin/env python3
import subprocess
import sys

CANDY_PROMPT = """小明有5颗糖，小红比小明多3颗，小刚比小红多2倍。问小刚有多少颗糖？

请详细说明你的推理过程。"""

def query_codex(prompt, model=None, reasoning="medium"):
    """Query Codex CLI directly"""
    cmd = ["codex", "query"]
    
    if model:
        cmd.extend(["--model", model])
    
    cmd.extend(["--reasoning-effort", reasoning])
    cmd.append(prompt)
    
    result = subprocess.run(
        cmd,
        capture_output=True,
        text=True,
        encoding='utf-8'
    )
    
    return result.stdout

def validate_answer(response):
    """Check if response contains correct answer 21"""
    # Look for standalone "21" (not part of larger number)
    import re
    pattern = r'\b21\b'
    return bool(re.search(pattern, response))

# Usage
response = query_codex(CANDY_PROMPT, model="gpt-5.5", reasoning="high")
is_correct = validate_answer(response)
print(f"Correct: {is_correct}")
print(f"Response:\n{response}")

Configuration

The script reads from Codex CLI's default configuration (~/.codexrc or equivalent). No additional config files needed.

Environment Variables

If your Codex CLI uses environment variables:

export OPENAI_API_KEY="your-key-here"
export CODEX_MODEL="gpt-5.5"  # Optional default model

Common Patterns

Batch Comparison Across Models

# Test multiple models
for model in gpt-4 gpt-5 gpt-5.5; do
  echo "Testing $model..."
  python codex_candy_eval.py -m $model -r high -n 20 | tee results_$model.txt
done

Reasoning Effort Analysis

# Compare reasoning levels for same model
for effort in low medium high xhigh; do
  python codex_candy_eval.py -m gpt-5.5 -r $effort -n 10
done

Statistical Sampling

# Large sample for statistical significance
python codex_candy_eval.py -m gpt-5.5 -r medium -n 100 > benchmark_results.txt

Troubleshooting

"codex: command not found"

Ensure Codex CLI is installed and in PATH:

which codex
# If not found, reinstall or add to PATH
export PATH="$PATH:/path/to/codex/bin"

Authentication Errors

Re-authenticate with Codex CLI:

codex auth logout
codex auth login

Incorrect Detection Rate

The script uses regex \b21\b to detect standalone "21". If you get false negatives, check:

Character encoding issues (script uses UTF-8)
Model returning answer in different format (e.g., "twenty-one")
Response being truncated

Python Encoding Issues

If Chinese characters display incorrectly:

export PYTHONIOENCODING=utf-8
python codex_candy_eval.py -m gpt-5.5 -r high -n 5

Expected Behavior

Correct Answer: 21 (Xiaoming: 5, Xiaohong: 8, Xiaogang: 8×2+8 = 21)
Higher Reasoning Effort: Generally increases accuracy but uses more tokens
Model Variations: Different models show different accuracy patterns
Token Usage: Typical range 800-2000 reasoning tokens per query

codex-candy-eval-benchmark