How it Works
simple-evals is a lightweight Python library designed to evaluate language models using standardized benchmarks in a zero-shot, chain-of-thought setting. Unlike many existing evaluation frameworks, it avoids few-shot or role-playing prompts, which can introduce variability and are less reflective of real-world usage. Instead, it employs straightforward instructions such as 'Solve the following multiple-choice problem' to assess model performance more transparently.
The library is structured to support a minimalist yet effective evaluation process. It retrieves benchmark datasets—such as MMLU, GPQA, MATH, HumanEval, and SimpleQA—and processes them through language model APIs. This approach ensures reproducibility and clarity in benchmarking, making it easier to interpret headline scores published alongside new model releases.
The evaluation process relies on chain-of-thought prompting, where models are encouraged to generate intermediate reasoning steps before arriving at a final answer. This method is particularly useful for tasks requiring logical deduction, such as mathematical problem-solving or complex reasoning questions.
The library does not require extensive configuration, making it accessible for researchers and practitioners who need a quick yet reliable way to benchmark models against established standards. However, it is important to note that the library is not designed to replace more comprehensive evaluation suites like OpenAI’s evals repository, which includes a broader range of benchmarks and use cases.
The repository also includes reference implementations for HealthBench, BrowseComp, and SimpleQA, ensuring that these specific benchmarks remain available for future reference. While the library will no longer be updated for new models or benchmarks after July 2025, it will continue to host these implementations, providing a static yet valuable resource for evaluating model capabilities in these domains.
Use Cases
simple-evals is primarily intended for researchers, developers, and organizations seeking a transparent and reproducible way to benchmark language models against widely recognized standards. Its zero-shot, chain-of-thought approach makes it particularly useful for assessing models in scenarios where minimal prompting is preferred, such as in production environments where prompt complexity can introduce variability.
For example, the library has been used to report scores for OpenAI’s o3, o4-mini, and GPT-4 series, as well as models from other providers like Claude, Llama, and Gemini. These comparisons help stakeholders understand how models perform across different tasks, from mathematical reasoning (MATH) to coding (HumanEval) and factual accuracy (SimpleQA).
The library is also valuable for red-teaming exercises, where security researchers assess model vulnerabilities or unintended behaviors.
Key Features & Capabilities
simple-evals supports a curated set of benchmarks designed to measure different aspects of language model performance.
- MMLU (Massive Multitask Language Understanding): Evaluates a model’s ability to handle a wide range of tasks, from mathematics to ethics and law.
- MATH: A dataset of mathematical problems designed to evaluate a model’s ability to solve grade-school to college-level math questions. The benchmark includes problems from competitions like the American Invitational Mathematics Examination (AIME) and requires step-by-step reasoning, often involving chain-of-thought prompting.
The library also includes adapters for integrating with language model APIs, currently supporting OpenAI’s platform.
One of the library’s standout features is its emphasis on zero-shot evaluation with chain-of-thought prompting. This approach reduces the variability introduced by prompt engineering, which can significantly alter reported scores. By standardizing the evaluation process, simple-evals provides a more reliable baseline for comparing models, particularly in settings where few-shot or role-playing prompts might skew results.
The repository also hosts reference implementations for HealthBench, BrowseComp, and SimpleQA, ensuring that these specialized benchmarks remain accessible.
simple-evals is licensed under the MIT License, which permits broad use, modification, and distribution. This permissive licensing makes it accessible to both academic and commercial entities, fostering collaboration and innovation in the field of AI evaluation. However, it is important to note that the library is not actively maintained for new benchmarks or models after July 2025, limiting its long-term extensibility.