o1 vs Qwen-Flash

1. Full-scale reasoning model

• Uses reinforcement learning to generate long internal chains of thought.
• Suitable for tasks requiring deep logic, multi-step planning, and rich analytical reasoning.

2. Strong performance across domains

• Excellent at math, science, coding, and structured analytical work.
• Handles multi-step workflows and complex problem-solving with high consistency.

3. High output capacity (100K tokens)

• Enables long, detailed explanations, large documents, and multi-part analyses.

4. Image-understanding capable

• Accepts text + image inputs for visual reasoning and mixed-modality tasks.
• Output is text only, optimized for clear explanations.

5. Advanced API compatibility

• Works with Chat Completions, Responses, Realtime, Assistants, and more.
• Supports streaming, function calling, and structured outputs.

6. Stable long-context performance

• 200K-token context window supports large files, multi-document analysis, and extended conversations.

7. Designed for correctness-oriented workloads

• Prioritizes rigorous reasoning over speed.
• Useful in auditing, verification, scientific thinking, policy analysis, and legal-style reasoning.

8. Powerful but expensive

• High token costs make it suitable for selective, mission-critical reasoning rather than high-volume usage.

1. Ultra-fast, ultra-cheap

• Designed for mass-scale workloads.
• Excellent for rewriting, extraction, classification.

2. Limited reasoning but great utility

• High throughput, low latency.

3. Optional thinking mode

• Adds chain-of-thought when needed.

4. Supports context cache & batch calls

• Very cost-effective system design.