beyondbench 0.1.0

BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models

BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models

101+ Models Evaluated | 44 Reasoning Tasks | 117 Variations | >10^15 Unique Instances

Explore Leaderboard | Read Paper | GitHub | Documentation

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What is BeyondBench?

BeyondBench introduces a revolutionary approach to evaluating reasoning capabilities in language models without relying on traditional static benchmarks. Our system dynamically generates novel problems across 44 distinct reasoning tasks with 117 variations, ensuring that models cannot memorize solutions and must demonstrate true reasoning abilities.

Key Features

• Dynamic Problem Generation — Problem space >10^15 unique instances, zero risk of data contamination
• Three Difficulty Levels — Easy (29 tasks), Medium (5 tasks, 49 variations), Hard (10 tasks, 68 variations)
• Multi-Backend Support — OpenAI, Gemini, Anthropic APIs + vLLM and HuggingFace Transformers
• Contamination-Resistant — No static benchmark memorization, novel problems every run
• Comprehensive Metrics — Accuracy, instruction-following compliance, token efficiency
• 101+ Models Evaluated — Open-source and proprietary, regularly updated

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Installation

From PyPI

pip install beyondbench

With Optional Dependencies

# All API clients (OpenAI, Gemini, Anthropic)
pip install beyondbench[all-apis]

# vLLM support (requires CUDA)
pip install beyondbench[vllm]

# Everything (all APIs + vLLM + dev tools + visualization)
pip install beyondbench[full]

From Source

git clone https://github.com/ctrl-gaurav/BeyondBench.git
cd BeyondBench
pip install -e .

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Quick Start

Interactive Wizard

beyondbench

Command Line

# Evaluate GPT-4o on the easy suite
beyondbench evaluate --model-id gpt-4o --api-provider openai --suite easy

# Evaluate a local model with vLLM
beyondbench evaluate --model-id meta-llama/Llama-3.2-3B-Instruct --backend vllm --suite all

# Evaluate Claude on hard tasks
beyondbench evaluate --model-id claude-sonnet-4-20250514 --api-provider anthropic --suite hard

# List all available tasks
beyondbench list-tasks

Python API

from beyondbench import EvaluationEngine, ModelHandler, TaskRegistry

# Initialize model handler
model = ModelHandler(
    model_id="gpt-4o",
    api_provider="openai",
    api_key="your-api-key"
)

# Run evaluation
engine = EvaluationEngine(model_handler=model, output_dir="./results")
results = engine.run_evaluation(suite="easy", datapoints=100)

# Print results
print(f"Average Accuracy: {results['summary']['avg_accuracy']:.2%}")

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New in v0.1.0

• API Server: beyondbench serve - FastAPI REST API
• Config Files: beyondbench init and beyondbench run-config
• Results Viewer: beyondbench results list/show/compare
• Task Info: beyondbench info <task>
• CI/CD: GitHub Actions for testing, linting, PyPI publishing
• 239 Tests: Comprehensive test coverage

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Supported Backends

Backend	Models	Features
OpenAI	GPT-4o, GPT-4o-mini, GPT-5, GPT-5-mini	Reasoning effort control
Gemini	Gemini 2.5 Pro, Gemini 2.5 Flash	Thinking budget configuration
Anthropic	Claude Sonnet 4, Claude Opus 4	Latest Claude models
vLLM	Any HuggingFace model	Batch processing, tensor parallelism
Transformers	Any HuggingFace model	CPU/GPU inference

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Task Suites

Easy Suite (29 Tasks)

Arithmetic (sum, multiplication, subtraction, division, absolute_difference), Statistics (mean, median, mode), Counting (odd_count, even_count, count_negative, count_unique, and more), Extrema (find_maximum, find_minimum, second_maximum, range, and more), Sequences (sorting, longest_increasing_subsequence, alternating_sum, sum_of_digits), Comparison

Medium Suite (5 Tasks, 49 Variations)

Fibonacci Sequence (6 variations), Algebraic Sequence (10), Geometric Sequence (10), Prime Sequence (11), Complex Pattern (12)

Hard Suite (10 Tasks, 68 Variations)

Tower of Hanoi, N-Queens, Graph Coloring, Boolean SAT, Sudoku, Cryptarithmetic, Matrix Chain, Modular Systems, Constraint Optimization, Logic Grid Puzzles

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Leaderboard (Top 5)

Rank	Model	Overall	Instruction Following
1	GPT-5*	83.56%	96.15%
2	GPT-5-Nano*	82.04%	93.58%
3	GPT-5-Mini*	81.67%	94.23%
4	o3*	80.36%	94.96%
5	o4-Mini*	79.04%	95.30%

*Models use reasoning/thinking tokens. Full results for 101+ models on the leaderboard.

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Environment Variables

export OPENAI_API_KEY="sk-..."
export GEMINI_API_KEY="..."
export ANTHROPIC_API_KEY="sk-ant-..."

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Citation

If you use BeyondBench in your research, please cite our paper (accepted at ICLR 2026):

@misc{srivastava2025beyondbenchbenchmarkfreeevaluationreasoning,
      title={BeyondBench: Contamination-Resistant Evaluation of Reasoning in Language Models},
      author={Gaurav Srivastava and Aafiya Hussain and Zhenyu Bi and Swastik Roy and Priya Pitre and Meng Lu and Morteza Ziyadi and Xuan Wang},
      year={2025},
      eprint={2509.24210},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.24210},
}

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Links

• Leaderboard: ctrl-gaurav.github.io/BeyondBench
• GitHub: github.com/ctrl-gaurav/BeyondBench
• Paper: arXiv:2509.24210
• Documentation: Full Docs | Usage Guide
• Issues: GitHub Issues
• Email: [e-mail], [e-mail]

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Made with care by the BeyondBench Team | Virginia Tech, Department of Computer Science | Amazon AGI

Advancing the frontier of AI reasoning evaluation, one benchmark at a time.

License: Apache-2.0