o1 vs Claude 3 Opus

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. Intelligence & Reasoning

• Highest capability in the Claude 3 family
• Near-human comprehension and fluency
• Excels at MMLU, GPQA, GSM8K, advanced reasoning tasks

2. Complex Problem Solving

• Best for research, strategy, multi-step planning
• Handles ambiguous, open-ended tasks with ease

3. Vision & Multimodal Capabilities

• Strong chart/graph understanding
• Processes documents, technical diagrams, and dense visual data

4. Recall & Long-Context Reasoning

• Near-perfect recall (>99% on NIAH benchmark)
• Handles very large documents and multi-file workflows

5. Enterprise-Grade Accuracy

• Significantly reduced hallucinations
• High correctness rate for factual queries