1. State-of-the-Art Image Generation

• Produces high-quality, detailed images optimized for realism, style control and prompt fidelity.
• Designed to handle complex visual scenes, compositions and lighting conditions.

2. Natively Multimodal Architecture

• Understands and reasons over both text and images as inputs.
• Ideal for workflows like editing based on reference images, expanding sketches or mockups and visual concept development.

3. Flexible Output Resolutions & Quality Levels

• Supports multiple resolutions including 1024x1024, 1024x1536 and 1536x1024.
• Offers three quality tiers (Low, Medium, High) to balance cost, speed and maximum detail.

4. Multiple Pricing Models

• Pay-per-token for multimodal input: text tokens and image tokens.
• Pay-per-image generation for final output: low, medium and high quality tiers.
• Enables businesses to balance cost and output needs.

5. Broad Use Cases

• Product photography and marketing assets.
• Illustration, concept art and creative ideation.
• UX/UI mockups.
• Style-guided image creation.
• Generating reference images for design or storytelling.

6. Supported Across Major API Endpoints

• Available via Chat Completions, Responses, Realtime, Assistants and Images (generations/edits) endpoints.
• Allows tight integration into automated creative pipelines or user-facing apps.

7. Simplified Model Behavior for Stability

• No streaming, function calling, structured outputs or fine-tuning; focused solely on high-quality image generation.

8. Consistent Results via Snapshots

• Supports snapshots for version locking to ensure long-term reproducibility.

9. Ideal For

• Designers, marketers and creatives.
• Product teams needing image assets.
• App builders integrating image generation workflows.
• Agencies producing visual content at scale.

1. Extremely low-cost text model

• One of the cheapest legacy models available.
• Suitable for very high-volume workloads with simple requirements.

2. Good for lightweight NLP tasks

• Classification, summarization, rewriting, paraphrasing, intent detection.
• Works for simple logic tasks and short reasoning sequences.

3. Works well for basic chatbots

• Optimized for Chat Completions API, originally powering early ChatGPT use cases.
• Good for rule-based or templated conversation flows.

4. Stable and predictable outputs

• Legacy behavior makes it suitable for systems built years ago that rely on its quirks.
• Good for backward compatibility or long-term enterprise pipelines.

5. Supports fine-tuning

• Useful for teams maintaining older fine-tuned GPT-3.5 models.
• Allows domain-specific compression of older datasets.

6. Limited capabilities compared to newer models

• No vision, no audio, no streaming, and no function calling.
• Much weaker reasoning and correctness vs GPT-4o mini or GPT-5.1.

7. Small context window (16K)

• Limited for multi-document tasks or long conversations.
• Best used for short, simple prompts or structured tasks.

8. Recommended migration path

• OpenAI explicitly recommends using GPT-4o mini instead.
• 4o mini is cheaper, smarter, faster, multimodal, and far more capable.