1. Maximum-compute o-series model

• Uses significantly more compute per query compared to o1.
• Produces deeper, more reliable reasoning chains.
• Best suited for high-stakes tasks that need correctness over speed.

2. Trained with reinforcement learning for deliberate thinking

• Explicit "think-before-answer" architecture.
• Excels at complex reasoning requiring multi-step analysis.

3. Very strong at math, science, coding, and technical proofs

• Handles long derivations, algorithm design, and difficult logic problems.
• Produces structured and explainable reasoning trails.

4. Great for multi-turn reasoning workflows

• Responses API optimized: can think over multiple internal turns before responding.
• Ideal for agentic reasoning pipelines.

5. Large context window

• 200,000-token context for large documents, multi-file review, and long reasoning traces.

6. Multimodal input (text + image)

• Can analyze images for mathematical diagrams, charts, handwritten content, UI layouts, etc.
• Output is text only.

7. Consistency, reliability, and depth

• Designed for situations where accuracy matters more than latency or cost.
• Strong error-checking and self-correction abilities.

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