Claude 4.6 Sonnet vs Claude 4.5 Opus

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.

1. Maximum capability with more practical pricing

• Anthropic introduced Opus 4.5 as its most intelligent model, combining maximum capability with practical performance.
• It was positioned as the best model in the world for coding, agents, and computer use at launch, with pricing reduced to $5/M input and $25/M output.

2. Step-change gains for coding and advanced agent work

• Anthropic describes Opus 4.5 as state-of-the-art on real-world software engineering tests.
• It also improved everyday knowledge-work tasks like deep research, slides, and spreadsheets while staying strong on long-horizon agent workflows.

3. Better control over reasoning depth

• Opus 4.5 introduced the effort parameter, letting developers trade off response thoroughness against token efficiency.
• This made it easier to use one flagship model across both high-depth analysis and more cost-sensitive production workloads.

4. Stronger computer use and continuity

• Added enhanced computer use with a zoom action for inspecting detailed screen regions.
• Preserves prior thinking blocks across turns, helping the model maintain reasoning continuity in extended multi-step tasks.