1. Strong reasoning for its generation

• Next-gen version of GPT-4 designed to be cheaper and faster than the original.
• Good for analytical tasks, structured writing, coding guidance, and multi-step reasoning.

2. Image input support

• Accepts images and provides text-only outputs.
• Useful for OCR, visual Q&A, document extraction, UI analysis, and design interpretation.

3. Stable performance

• Predictable model behavior suitable for legacy systems still built on GPT-4.
• Works reliably for established pipelines and enterprise workloads.

4. Large 128K context window

• Handles long documents, multi-file inputs, or extended conversational sessions.
• Allows complex prompt chaining and large instruction sets.

5. Broad endpoint compatibility

• Works with Chat Completions, Responses API, Realtime API, Assistants, Batch, Fine-tuning, Embeddings, and more.
• Supports streaming and function calling.

6. Good choice for cost-controlled GPT-4-class workloads

• Although older, still useful for teams who want GPT-4-level reasoning without upgrading immediately.
• A midpoint between legacy GPT-4 and modern GPT-4o/5.1 models.

7. Text-only output simplifies downstream use

• Ensures deterministic outputs for applications that need reliable text generation.
• Good for RAG, data pipelines, automation tools, and enterprise systems.

8. Recommended migration path

• OpenAI now recommends using GPT-4o or GPT-5.1 for improved speed, cost, reasoning, and multimodal capability.
• GPT-4 Turbo remains available for backward compatibility and stability.

1. State-of-the-art reasoning performance

• #1 on LMArena human preference leaderboard.
• Excels at advanced reasoning benchmarks like GPQA and AIME 2025.
• Achieves 18.8% on Humanity's Last Exam (no tools), representing frontier human-level reasoning.

2. New “thinking model” architecture

• Built with explicit reasoning steps internally before responding.
• Handles complex, multi-stage logic with higher accuracy and fewer hallucinations.

3. Elite science and mathematics capabilities

• Leads in math and science tasks across industry benchmarks.
• High performance without costly inference tricks like majority voting.

4. Exceptional coding abilities

• Major leap over Gemini 2.0 in coding performance.
• 63.8% on SWE-Bench Verified with custom agent setup.
• Strong at code transformation, debugging, and building agentic apps.
• Capable of generating full applications (e.g., a playable video game) from a single-line prompt.

5. Massive multimodal context

• Ships with a 1,000,000 token window (2M coming soon).
• Handles entire documents, datasets, video sequences, audio files, and large codebases.
• Maintains strong performance even at extreme context lengths.

6. Native multimodality across all inputs

• Understands and reasons over text, images, audio, video, and code.
• Designed for real-world, multi-source problem-solving and agent workflows.

7. Consistent high-quality outputs

• Improved post-training results in more accurate, coherent, and stylistically strong responses.
• Higher reliability across complex workloads.

8. Early availability for developers

• Available today in Google AI Studio for experimentation.
• Coming soon to Vertex AI with higher rate limits and production-ready access.