Evalute.ai - Benchmarking LLMs for Complex Programming

Public Benchmark Problems

Hypergraph Theory Difficulty: ★★★★☆

Rado’s Theorem

Gives necessary and sufficient conditions for a system of sets to admit a transversal, generalizing Hall's condition.

Full formal problem description

Hidden test cases (not visible to the model)

Time and memory limits

50 test cases

Poset Theory Difficulty: ★★★★★

Dilworth's Theorem

In any finite partially ordered set, the size of the largest antichain equals the minimum number of chains needed to partition the set.

Full formal problem description

Hidden test cases (not visible to the model)

Time and memory limits

50 test cases

Linear Programming Difficulty: ★★★☆☆

Birkhoff–von Neumann Theorem

Every doubly-stochastic matrix can be written as a convex combination of permutation matrices.

Full formal problem description

Hidden test cases (not visible to the model)

Time and memory limits

50 test cases

Rigorous Evaluation Framework

Each problem page includes all the components needed for proper evaluation:

Full formal problem description with mathematical precision
Clear input/output constraints
Hidden test cases (not visible to the model)
Strict time and memory limits

This format mimics how humans are judged in competitive programming — and we bring the same rigor to LLMs.

Secure Evaluation Engine

Sandboxed Execution Environment

Our evaluation engine runs submissions in a secure, isolated sandbox with strict time and memory constraints to simulate real-world conditions.

Complete process isolation with Docker containers
Precise time measurement with nanosecond precision
Memory limits enforced with cgroups
Hidden test cases to prevent overfitting

Problem:Rado’s Theorem Passed

Test Cases 50/50 passed

Time Efficiency 98% within limits

Memory Usage 100% within limits

API for Model Evaluation


import requests

api_key = "your_api_key_here"
problem_id = "opt_bst_2023"

# Model's solution code
solution_code = \"\"\"
def construct_optimal_bst(keys, freq):
    # Implementation goes here
    return optimal_bst
\"\"\"

# Prepare the request
headers = {"Authorization": f"Bearer {api_key}"}
data = {
    "problem_id": problem_id,
    "language": "python",
    "source_code": solution_code
}

# Submit for evaluation
response = requests.post(
    "https://api.evalute.ai/v1/evaluate",
    headers=headers,
    json=data
)

print(response.json())


const fetch = require('node-fetch');

const apiKey = "your_api_key_here";
const problemId = "opt_bst_2023";

// Model's solution code
const solutionCode = `
function constructOptimalBST(keys, freq) {
    // Implementation goes here
    return optimalBST;
}`;

// Prepare the request
const response = await fetch('https://api.evalute.ai/v1/evaluate', {
    method: 'POST',
    headers: {
        'Authorization': `Bearer ${apiKey}`,
        'Content-Type': 'application/json'
    },
    body: JSON.stringify({
        problem_id: problemId,
        language: "javascript",
        source_code: solutionCode
    })
});

console.log(await response.json());


curl -X POST "https://api.evalute.ai/v1/evaluate" \
     -H "Authorization: Bearer your_api_key_here" \
     -H "Content-Type: application/json" \
     -d '{
           "problem_id": "opt_bst_2023",
           "language": "python",
           "source_code": "def construct_optimal_bst(keys, freq):\n    # Implementation goes here\n    return optimal_bst"
         }'

Simple Integration for AI Labs

Our REST API allows seamless integration with your model development pipeline. Get detailed evaluation results with a single API call.

Features:

Submit solutions in 15+ programming languages
Get detailed performance metrics
Webhook support for asynchronous evaluation
Rate limiting and usage analytics

Authentication:

Secure API key authentication with granular permissions for team collaboration.

Model Leaderboard

Birkhoff–von Neumann Theorem Challenge

Rank	Model	Organization	Score	Pass Rate	Efficiency	Submitted
1	Openai o3	OpenAI	56	56%	88%	2025-07-15
2	Gemini 2.5 pro	Google	50	50%	82%	2025-07-10
3	Claude Opus 4.0 thinking	Anthropic	60	60%	85%	2025-07-15

Showing 1 to 3 of 24 results

...

Trusted by Leading AI Labs

★★★★★

"Evalute.ai provides the most rigorous testing framework we've found for our models. The detailed metrics help us identify exactly where improvements are needed."

Sarah Johnson

Anonymous Reviewer #1

★★★★★

"The hidden test cases and strict time limits give us confidence that our models can perform under real-world constraints."

Michael Chen

Anonymous Reviewer #2

★★★★☆

"The API integration made it easy to incorporate benchmarking into our development pipeline. We run tests automatically with every model update."

Priya Patel

Anonymous Reviewer #3

Benchmark Your AI Models Like Never Before

Public Benchmark Problems

Rado’s Theorem

Dilworth's Theorem

Birkhoff–von Neumann Theorem

Rigorous Evaluation Framework

Secure Evaluation Engine

Sandboxed Execution Environment

Model Performance Comparison

API for Model Evaluation

Simple Integration for AI Labs

Features:

Authentication:

Model Leaderboard

Birkhoff–von Neumann Theorem Challenge

Trusted by Leading AI Labs

Sarah Johnson

Michael Chen

Priya Patel

Ready to Benchmark Your Models?

Evalute LLM Demo

Benchmark Your AI Models Like Never Before

Public Benchmark Problems

Rado’s Theorem

Dilworth's Theorem

Birkhoff–von Neumann Theorem

Rigorous Evaluation Framework

Secure Evaluation Engine

Sandboxed Execution Environment

Model Performance Comparison

API for Model Evaluation

Simple Integration for AI Labs

Features:

Authentication:

Model Leaderboard

Birkhoff–von Neumann Theorem Challenge

Trusted by Leading AI Labs

Sarah Johnson

Michael Chen

Priya Patel

Ready to Benchmark Your Models?

Problem Details

Test Case Results

Evalute LLM Demo