GPT-5.2 Codex vs Claude 4.1 Opus

1. Optimized for Long-Horizon Coding Tasks

• OpenAI describes GPT-5.2 Codex as a highly intelligent coding model built for long-horizon, agentic coding work.
• Well suited to planning, refactoring, debugging, and multi-step implementation flows inside real codebases.

2. Adjustable Reasoning for Coding Work

• Supports configurable reasoning effort from low to xhigh depending on speed and quality needs.
• Accepts both text and image inputs while producing text output.

3. Large Context + Long Output

• 400 k token context window supports broad repository understanding and larger working sets.
• Allows up to 128 k output tokens for longer patches, code generation, and technical explanations.

4. Up-to-Date Model Snapshot

• Knowledge cut-off of Aug 31 2025 keeps it current with newer tools and frameworks.
• Supports streaming, function calling, and structured outputs for tool-driven coding workflows.

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.