o3 vs Claude 4.6 Sonnet

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. Most capable Sonnet model yet

• Anthropic describes Sonnet 4.6 as its most capable Sonnet model.
• It is a full upgrade across coding, computer use, long-context reasoning, agent planning, knowledge work, and design.

2. Stronger coding and professional task performance at Sonnet pricing

• Pricing remains at $3/M input and $15/M output, matching Sonnet 4.5.
• Anthropic says early-access developers strongly preferred it to Sonnet 4.5, and often even to Opus 4.5 for practical work.

3. Long-context, agent-friendly reasoning

• Supports up to a 1M token context window in beta.
• Anthropic reports better consistency, fewer false claims of success, fewer hallucinations, and more reliable follow-through on multi-step tasks.

4. Modern API controls for adaptive work

• Supports adaptive thinking and the effort parameter for balancing speed, cost, and depth.
• Gains dynamic filtering for web search and web fetch, helping agent workflows keep only relevant information in context.