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.

1. State-of-the-art reasoning

• Top performance across academic reasoning, scientific knowledge, math, and complex problem-solving.
• Excels at long-horizon, multi-step workflows and deep logical interpretation.

2. World-leading multimodal capabilities

• Natively understands text, images, videos, audio, and code.
• Ranked highest on benchmarks like MMMU-Pro, Video-MMMU, ScreenSpot-Pro.

3. Exceptional coding + agentic workflows

• Strong in competitive coding and real-world agentic tasks (SWE-Bench Verified, Terminal-Bench, LiveCodeBench).
• Improved tool calling, planning, and execution for autonomous or semi-autonomous agents.

4. Powerful for long-context tasks

• Effective at 128K-1M context windows with high retrieval accuracy.
• Ideal for document-heavy workflows, research, analysis, multi-file coding, and multi-document reasoning.

5. Strong information synthesis and interpretation

• Outperforms peers in chart reasoning, OCR, structured extraction, and screen understanding.
• Excellent at combining multimodal inputs into coherent, concise answers.

6. High reliability for enterprise tasks

• Benchmarks show superior factuality, grounding, and parametric knowledge.
• Strong multilingual accuracy and global commonsense performance.

7. Optimized for production agents

• Designed for complex multi-step planning, simultaneous task execution, and improved consistency.
• Works across coding, research, creative workflows, UI generation, and data-heavy applications.