o3 vs Qwen-Omni-Turbo

1. Advanced reasoning capability

• Designed for multi-step thinking across text, code, and visual inputs.
• Excels at math, science, logic puzzles, and complex analytical workflows.

2. Strong performance across domains

• Highly capable in technical writing, data analysis, and structured problem-solving.
• Useful for research, engineering tasks, and intricate instruction-following.

3. Visual reasoning support

• Accepts image inputs, enabling tasks such as diagram analysis, chart interpretation, and visual logic assessments.

4. High output capacity

• Up to 100,000 output tokens, supporting long-form content, technical breakdowns, and multi-part solutions.

5. Excellent instruction following

• Produces detailed, step-by-step responses for tasks requiring precision and clarity.
• Ideal for educational explanations, system design reasoning, and code walkthroughs.

6. Large 200K context window

• Handles long documents, multi-file reasoning, or extended conversations with minimal loss of context.

7. Broad API support

• Works with Chat Completions, Responses, Realtime, Assistants, Batch, Embeddings, Image Generation, and more.
• Supports streaming and function calling for advanced workflows.

8. Positioned as a legacy reasoning model

• Remains extremely capable but formally succeeded by GPT-5, which offers stronger reasoning and performance.

1. Fast multimodal understanding

• Handles text, audio, images.

2. Supports text+audio outputs

• Great for assistants and education.

3. Strong cross-modal alignment

• Solid for recognition, instructions, and conversion tasks.