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LLM ComparisonGPT-5.2Claude 4.1 Opus

GPT-5.2 vs Claude 4.1 Opus

Compare GPT-5.2 and Claude 4.1 Opus. Build AI products powered by either model on Appaca.

Model Comparison

FeatureGPT-5.2Claude 4.1 Opus
ProviderOpenAIAnthropic
Model Typetexttext
Context Window400,000 tokens1,000,000 tokens
Input Cost
$1.75/ 1M tokens
$15.00/ 1M tokens
Output Cost
$14.00/ 1M tokens
$75.00/ 1M tokens

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Strengths & Best Use Cases

GPT-5.2

OpenAI

1. Advanced Reasoning for Diverse Domains

  • Built to tackle coding and agentic workflows across multiple industries, with configurable reasoning support.

2. Multi-Modal & Long-Form Capabilities

  • Handles both text and image inputs, producing text output.
  • Allows up to 128 k output tokens for lengthy responses.

3. Large Context & Updated Knowledge

  • 400 k token context window accommodates extensive codebases or documents.
  • Knowledge cut-off of Aug 31 2025 keeps it current with recent developments.

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.