Python Competitive Programming & Algorithmic Reasoning Benchmark

A comprehensive benchmark designed to evaluate AI models' capabilities in competitive programming, algorithmic problem-solving, and software engineering tasks. This benchmark tests models on novel programming challenges that require deep understanding of algorithms, data structures, and efficient code implementation.

Python Programming
Algorithmic Reasoning
Competitive Programming
Code Quality

Pass@1 Accuracy

0%25%50%75%100%
OpenAI o3
46%
Anthropic Opus 4
37.6%
DeepMind Gemini 2.5 Pro
23.4%
xAI Grok 3
8.4%
Amazon Nova Pro
6.8%
Meta Llama 4 Maverick
6.4%

Benchmark Overview

Our benchmark evaluates AI models across multiple dimensions of programming and algorithmic reasoning

Algorithmic Reasoning

Complex problem-solving requiring deep understanding of algorithms and data structures

Novel Problem Creation

All questions were written by our expert annotators, ensuring the dataset consists entirely of novel problems

Competitive Programming

Programming challenges similar to those found in coding competitions

Uncontaminated Evaluation

Addresses need for challenging, uncontaminated data

Problem Categories

Comprehensive coverage of algorithmic problem types and difficulty levels

Dynamic Programming

Problems that require breaking down complex problems into simpler subproblems and storing solutions to avoid redundant calculations. Tests understanding of optimal substructure and overlapping subproblems.

Greedy Algorithms

Problems that require making locally optimal choices at each step to find a globally optimal solution. Tests ability to identify when greedy strategies are applicable and effective.

Graph Algorithms

Problems involving graph traversal, pathfinding, and graph analysis. Tests understanding of graph representations, search algorithms, and graph theory concepts.

Backtracking

Problems that require systematically exploring all possible solutions by building candidates incrementally and abandoning partial solutions that cannot be completed.

Problem Example

See an example of the types of algorithmic challenges our benchmark evaluates

Factory Worker Scheduling Problem

Problem Statement

You are managing a factory that produces products in batches. Each batch contains some number of products that must be assembled in some amount of time. You have a fixed number of workers in the factory, and each one of them is capable of assembling a given number of products per unit of time. But every time one worker has finished the batch that he was assigned to and then a new batch comes, he has to take his break and then prepare the new batch, which implies a delay.

The goal is to determine the minimum number of worker-hours required to assemble all products, considering a break between batches. If it is impossible to assemble all products within the given time, return -1.

Function Signature

def minimum_worker_hours(N: int, max_capacity: int, batches: List[Tuple[int, int, int]]) -> int:

Input Parameters

  • N: Number of product batches (1 ≤ N ≤ 1000)
  • max_capacity: Products per worker per time unit (1 ≤ max_capacity ≤ 10^9)
  • batches: List of tuples (start_time, end_time, num_products)

Input Validation

  • • N must be within range (1 ≤ N ≤ 1000)
  • • batches list size must equal N
  • • max_capacity must be within range (1 ≤ max_capacity ≤ 10^9)
  • • Each batch must satisfy: 1 ≤ start_time ≤ end_time ≤ 10^9
  • • Each batch must satisfy: 1 ≤ num_products ≤ 10^9
  • • Raise "not valid input" exception for violations

Test Cases

Valid Input Tests

minimum_worker_hours(10, 89, [(1, 2, 82), (2, 2, 31), (3, 4, 63), (6, 7, 18), (9, 9, 44), (10, 11, 95), (13, 13, 52), (13, 15, 39), (15, 16, 70), (17, 18, 54)]) == 571
minimum_worker_hours(10, 79, [(2, 2, 70), (2, 10, 35), (10, 10, 76), (11, 11, 66), (12, 12, 75), (12, 14, 88), (15, 16, 76), (17, 18, 97), (19, 20, 105), (20, 20, 46)]) == 862
minimum_worker_hours(4, 10, [(1, 3, 1), (3, 3, 10), (5, 6, 15), (7, 8, 1)]) == 36
minimum_worker_hours(4, 7, [(2, 4, 9), (7, 8, 13), (8, 8, 7), (9, 9, 5)]) == -1

Input Validation Tests

try:
    minimum_worker_hours(5, 10, [(1, 3, 1), (3, 3, 10), (5, 6, 15), (7, 8, 1)])
    assert False
except ValueError as e:
    assert str(e) == "not valid input"
try:
    minimum_worker_hours(4, 10, [(1, 3, 1), (3, 2, 10), (5, 6, 15), (7, 8, 1)])
    assert False
except ValueError as e:
    assert str(e) == "not valid input"
try:
    minimum_worker_hours(-1, -1, [])
    assert False
except ValueError as e:
    assert str(e) == "not valid input"

Problem Complexity Analysis

Algorithmic Challenges

  • Scheduling Optimization: Finding optimal worker assignment across time intervals
  • Resource Allocation: Managing worker capacity constraints
  • Time Management: Handling breaks and batch transitions
  • Feasibility Checking: Determining if all products can be assembled

Expected Solution Approach

This problem requires understanding of:

  • • Greedy algorithms for optimal scheduling
  • • Binary search for finding minimum worker hours
  • • Time interval processing and overlap detection
  • • Input validation and error handling

Methodology

Comprehensive evaluation framework for assessing AI algorithmic reasoning capabilities

Problem Set Generation

Our benchmark comprises 500 novel, original coding problems specifically designed to test algorithmic reasoning and competitive programming skills. These problems were:

  • • Internally created by human problem designers
  • • Crafted to be unique and not present in existing training datasets
  • • Developed to challenge core algorithmic thinking and problem-solving abilities

Contamination Prevention

To ensure the integrity of our evaluation:

  • • All problems were created through an internal, controlled process
  • • No publicly available coding problem repositories were used as source material
  • • Extensive checks were performed to confirm the originality of each problem

Evaluation Approach

Prompt Template

All models were evaluated using the following standardized prompt:

"You are an expert Python programmer, and here is your task:\n{problem_description}\n\nYour code should pass these tests:\n{test_cases}"

Assessment Metric

We utilized the Pass@1 (single-shot prompt) accuracy metric, which measures:

  • • The model's ability to generate a correct solution on the first attempt
  • • Comprehensive test cases covering various input scenarios
  • • Full problem constraints and expected output formats

Model Testing Procedure

For each model:

  • • Identical problem set was used across all benchmarked models
  • • Single, consistent prompting strategy applied
  • • Evaluation focused on raw, unassisted code generation capabilities

Significance and Implications

Our benchmark reveals critical insights:

  • 1. A significant performance gap exists between top-tier models (OpenAI and Anthropic) and other AI labs
  • 2. Current AI models demonstrate limited capabilities in solving foundational algorithmic reasoning problems
  • 3. The benchmark underscores the challenges in developing AI systems capable of nuanced, complex problem-solving

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