o1-pro vs Claude 4.5 Sonnet

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. Best-in-class coding performance

• #1 on SWE-bench Verified (77.2% standard, 82.0% high-compute).
• Excels at debugging, architecture, and multi-file code generation.
• Maintains coherence for extremely long tasks (30+ hours).

2. State-of-the-art computer use & agents

• Leads OSWorld at 61.4%.
• Strongest model for agentic workflows, multi-step tool use, and real computer control.
• Powering Claude Code, the new Claude Agent SDK, and Chrome agent actions.

3. Advanced reasoning & math

• Large improvements across reasoning-heavy benchmarks (AIME, MMMLU, τ2-bench, Terminal-Bench).
• Deep multi-step reasoning with extended or interleaved thinking.

4. High alignment & safety

• Most aligned Claude model to date with reduced deception, hallucinations, sycophancy, and harmful compliance.
• Strong protections against prompt injection for agentic tasks (ASL-3 safeguards).

5. Domain-expert performance

• Notable gains in finance, law, medicine, and STEM tasks.
• Trusted by early customers for long-context legal analysis, multi-file engineering, security research, and red-teaming.