GPT-5-Model-Switch Dataset

Overview

The GPT-5-Model-Switch dataset is designed to train an open-source model selector, serving as an alternative to OpenAI's GPT-5. As Sam Altman stated, "GPT-5 will be a model selector," and this dataset supports the development of an intelligent system capable of dynamically selecting the optimal model based on task requirements.

Dataset Purpose

The goal of this dataset is to train a model selector that can:

• Analyze the characteristics of input tasks (e.g., text generation, reasoning, mathematical computation).
• Select the most suitable model from a set of pretrained models based on task type and complexity.
• Replicate the anticipated functionality of GPT-5, offering efficient and flexible model-switching capabilities.

Dataset Composition

The dataset includes the following key components:

1. Input Samples: A diverse set of task inputs, including natural language text, mathematical problems (e.g., ( x^2 + 2x + 1 = 0 )), code snippets, and multimodal data.
2. Task Labels: Each input sample is annotated with its task type (e.g., "generation," "reasoning," "computation") and difficulty level.
3. Model Selection Targets: Annotations indicating the ideal model for each task (e.g., GPT-4o for generation tasks or o3-mini for reasoning).
4. Performance Metrics: Data on how different models perform on corresponding tasks (e.g., accuracy, response time).

Dataset Size

• Number of Samples: Approximately 100,000 task inputs.
• Model Options: Metadata for 5-10 pretrained models, used to simulate the selection process.
• Data Sources: A mix of synthetic data and public datasets (e.g., The Pile, MMLU).

Mathematical Example

For instance, for a mathematical task:

• Input: Solve ( x^2 - 4 = 0 ).
• Task Label: Computation, medium difficulty.
• Target Model: A model excelling in mathematical reasoning (e.g., o3-mini).
• Output: [ x = 2 \text{ or } x = -2 ].

Open-Source Significance

This dataset provides the open-source community with an opportunity to replicate GPT-5's core functionality—a model selector—at a lower cost. In contrast to OpenAI's proprietary approach, we aim to accelerate the democratization of AI research by sharing the data and training process openly.

Usage

1. Download the dataset.
2. Train a classifier or reinforcement learning agent as a model selector using supervised learning methods.
3. Evaluate the selector's performance to ensure accuracy across diverse tasks.

Citation

If you use this dataset in your research, please cite it as follows:

@dataset{gpt5_model_switch_2025,
  title = {GPT-5-Model-Switch Dataset},
  author = {Chieko-Seren},
  year = {2025},
  url = {https://huggingface.co/datasets/ChiekoSeren/GPT-5-model-switch},
  note = {An open-source dataset for training a GPT-5-inspired model selector}
}