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LLM ComparisonClaude 4.6 SonnetClaude 4.1 Opus

Claude 4.6 Sonnet vs Claude 4.1 Opus

Compare Claude 4.6 Sonnet and Claude 4.1 Opus. Build AI products powered by either model on Appaca.

Model Comparison

FeatureClaude 4.6 SonnetClaude 4.1 Opus
ProviderAnthropicAnthropic
Model Typetexttext
Context Window1,000,000 tokens1,000,000 tokens
Input Cost
$3.00/ 1M tokens
$15.00/ 1M tokens
Output Cost
$15.00/ 1M tokens
$75.00/ 1M tokens

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Strengths & Best Use Cases

Claude 4.6 Sonnet

Anthropic

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.

Claude 4.1 Opus

Anthropic

1. Advanced Coding Performance

  • Achieves 74.5% on SWE-bench Verified, improving the Claude family's state-of-the-art coding abilities.

  • Stronger at:

    • Multi-file code refactoring
    • Large codebase debugging
    • Pinpointing exact corrections without unnecessary edits
  • Outperforms Opus 4 and shows gains comparable to jumps seen in past major releases.

2. Improved Agentic & Research Capabilities

  • Better at maintaining detail accuracy in long research tasks.
  • Enhanced agentic search and step-by-step problem solving.
  • Performs reliably across complex multi-turn reasoning tasks.

3. Validated by Real-World Users

  • GitHub: Better multi-file refactoring and code adjustments.
  • Rakuten Group: High precision debugging with minimal collateral changes.
  • Windsurf: One standard deviation improvement on their junior dev benchmark - similar magnitude to Sonnet 3.7 → Sonnet 4.

4. Hybrid-Reasoning Benchmark Improvements

  • Improvements across TAU-bench, GPQA Diamond, MMMLU, MMMU, AIME (with extended thinking).
  • Stronger robustness in long-context reasoning tasks.

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