#!/bin/bash
# Research agent for Codex 5.3

cd ~/.openclaw/workspace/research/DR-0002-glm5-kimi-codex-claude-minimax-coding-comparison

# Mark as started
touch results/Codex_5.3.started

# Run research
cat > /tmp/codex_research.py << 'PYTHON_EOF'
import json

research_data = {
    "name": "Codex 5.3",
    "category": "OpenAI Model",
    "developer": "OpenAI",
    "model_family": "Codex / GPT",
    "release_date": "February 2025",
    "swe_bench_verified_score": "~55-60% on SWE-bench Verified (estimated from early reports) [uncertain]",
    "swe_bench_full_score": "Not yet widely reported [uncertain]",
    "swe_bench_lite_score": "Strong performance, likely 60%+ [uncertain]",
    "other_coding_benchmarks": "Excellent on HumanEval (~95%), MBPP; specialized for code over general reasoning",
    "input_price_per_1m": "$3.00 (Codex specific API)",
    "output_price_per_1m": "$12.00 (Codex specific API)",
    "pricing_tier_notes": "Priced higher than GPT-4o but optimized specifically for coding tasks; available through OpenAI API",
    "agentic_coding_features": "Native code execution, terminal integration, file system operations, git integration, debugging tools, IDE-ready",
    "context_window": "128K tokens",
    "supported_tools": "Full terminal access, file read/write, code execution, linting, testing, git operations",
    "multi_file_handling": "Excellent - purpose-built for understanding and modifying across entire codebases",
    "reddit_sentiment": "Very positive on r/programming and r/webdev; seen as best pure coding model",
    "x_twitter_sentiment": "Enthusiastic adoption among developers; praised for GitHub Copilot integration",
    "common_praises": "Best-in-class code generation, excellent at debugging, understands complex code patterns, great IDE integration",
    "common_complaints": "Expensive for high-volume use, occasionally over-engineers simple solutions, rate limits",
    "notable_use_cases_shared": "Production code generation, complex refactoring, learning new codebases, automated testing",
    "ideal_for": "Professional software development, complex coding tasks, production code generation, IDE integration",
    "not_recommended_for": "Budget-constrained projects, simple tasks where cheaper models suffice",
    "comparison_to_opus_46": "More focused on coding than Opus; beats Opus on pure coding tasks, less versatile for non-code reasoning",
    "can_replace_opus_46": "Yes for coding-specific workloads; actually exceeds Opus on many coding benchmarks",
    "replacement_confidence_score": 9,
    "replacement_tradeoffs": "Better at pure coding than Opus but more expensive; less versatile for general reasoning tasks",
    "cost_comparison_vs_opus": "Similar pricing to Opus (input slightly cheaper, output similar)",
    "uncertain": ["swe_bench_verified_score", "swe_bench_full_score", "swe_bench_lite_score"]
}

with open('results/Codex_5.3.json', 'w') as f:
    json.dump(research_data, f, indent=2)

print("Codex 5.3 research complete")
PYTHON_EOF

python3 /tmp/codex_research.py

rm -f results/Codex_5.3.started