> ## Documentation Index > Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt > Use this file to discover all available pages before exploring further. # OpenAI Model Variants > OpenAI’s API offers various model families and variants optimized for specific use cases, balancing performance, cost, and accuracy. OpenAI’s API provides a rich set of model families and variants, each optimized for specific use cases—from code completion to conversational AI. Choosing the right model can help you balance performance, cost, and accuracy. ## Model Families and Variants A **model family** groups related variants by their primary function. Each **variant** carries a version identifier reflecting its training improvements and feature set. Below is a high-level overview: | Model Family (Use Case) | Variant | Optimized For | | --------------------------- | ------------------ | --------------------------------------- | | Davinci (Natural Language) | `text-davinci-002` | Instruction following | | | `text-davinci-003` | Advanced NLU and generation | | Codex (Code Completion) | `code-davinci-002` | Code autocomplete and synthesis | | GPT-3.5 (Chat & Completion) | `gpt-3.5-turbo` | Low-latency chat and multi-turn support | ### GPT-3.5 Variants Overview The GPT-3.5 series balances capability and cost with varying token limits and data cutoffs: | Variant | Max Tokens | Training Data Through | Primary Use Case | | ------------------ | ---------- | --------------------- | ---------------------- | | `text-davinci-003` | 4,096 | June 2021 | Natural language tasks | | `gpt-3.5-turbo` | 4,096 | September 2021 | Chat-style interfaces | ![The image is a table listing GPT-3.5 model variants, including their descriptions, maximum tokens, and training data. It categorizes models into "Chat" and "Word/Code Completion" types.](https://kodekloud.com/kk-media/image/upload/v1752881517/notes-assets/images/Mastering-Generative-AI-with-OpenAI-OpenAI-Model-Variants/gpt-3-5-model-variants-table.jpg) `gpt-3.5-turbo` is optimized for multi-turn dialogues and often delivers lower latency and cost for chat applications. OpenAI continually refines these variants, improving response quality, speed, and cost-effectiveness. ## Fine-Tuning Models Fine-tuning allows you to customize a base model on your domain-specific dataset. Benefits include: * Domain-tailored responses with higher relevance * Shorter prompts, reducing token usage and cost * Faster inference, since context is baked into the model ![The image illustrates the process of model fine-tuning, showing a flow from custom data to a fine-tuned model that generates responses. It highlights benefits like higher quality results, token savings, and lower latency.](https://kodekloud.com/kk-media/image/upload/v1752881519/notes-assets/images/Mastering-Generative-AI-with-OpenAI-OpenAI-Model-Variants/model-fine-tuning-process-diagram.jpg) ### Fine-Tuning Workflow 1. Format your dataset in JSONL with prompt–completion pairs. 2. Upload data and create a fine-tuning job via the CLI or API. 3. Monitor training, evaluate performance, and deploy your custom endpoint. Ensure your training data is clean, balanced, and representative. Low-quality data can degrade performance. ## Embeddings and Semantic Search Embeddings convert text into vector representations for advanced applications: | Use Case | Description | | --------------------- | ---------------------------------------------- | | Semantic Search | Retrieve documents based on contextual meaning | | Recommendation | Suggest similar content or products | | Entity Classification | Categorize text into predefined classes | OpenAI’s `text-embedding-ada-002` model generates high-quality embeddings at scale. ![The image illustrates an embedding model process where the text "anatine amigos" is converted into a vector using the "text-embedding-ada-002" model.](https://kodekloud.com/kk-media/image/upload/v1752881520/notes-assets/images/Mastering-Generative-AI-with-OpenAI-OpenAI-Model-Variants/anatine-amigos-text-embedding-vector.jpg) ## Other Model Families OpenAI’s ecosystem extends beyond GPT and Codex: * [DALL·E 2](https://platform.openai.com/docs/models/images/dall-e-2) – Image generation (beta) * [Whisper 1](https://platform.openai.com/docs/models/speech-to-text) – Speech-to-text (beta) *** ## Links and References * [OpenAI API Documentation](https://platform.openai.com/docs/) * [Fine-tuning Guide](https://platform.openai.com/docs/guides/fine-tuning) * [Embeddings Overview](https://platform.openai.com/docs/guides/embeddings) * [DALL·E 2 Documentation](https://platform.openai.com/docs/models/images/dall-e-2)