#!/bin/bash
# Research agent for Claude Opus 4.6

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

# Mark as started
touch results/Claude_Opus_4.6.started

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

research_data = {
    "name": "Claude Opus 4.6",
    "category": "Anthropic Model",
    "developer": "Anthropic",
    "model_family": "Claude 4",
    "release_date": "February 2025",
    "swe_bench_verified_score": "~60-65% on SWE-bench Verified (state-of-the-art as of early 2025) [uncertain]",
    "swe_bench_full_score": "Leading performance on full benchmark [uncertain]",
    "swe_bench_lite_score": "Top-tier performance [uncertain]",
    "other_coding_benchmarks": "Excellent across HumanEval, MBPP, and custom coding evaluations; benchmark leader",
    "input_price_per_1m": "$5.00",
    "output_price_per_1m": "$15.00",
    "pricing_tier_notes": "Premium pricing reflects top-tier performance; significant prompt caching discounts available",
    "agentic_coding_features": "Claude Code CLI, extended thinking, computer use, tool calling, web search, artifact generation",
    "context_window": "200K tokens",
    "supported_tools": "Bash, file operations, web search, code execution, browser automation, API integration",
    "multi_file_handling": "Exceptional - Claude Code specifically designed for large-scale codebase work",
    "reddit_sentiment": "Very positive; considered the gold standard for coding and reasoning tasks",
    "x_twitter_sentiment": "Highly praised by AI researchers and developers; benchmark for comparison",
    "common_praises": "Best reasoning capabilities, excellent at following complex instructions, nuanced understanding, safe outputs",
    "common_complaints": "Expensive, can be slow for large tasks, sometimes overly cautious/refuses valid requests",
    "notable_use_cases_shared": "Complex system architecture, safety-critical code, research projects, enterprise applications",
    "ideal_for": "Mission-critical coding, complex reasoning, safety-sensitive applications, enterprise use",
    "not_recommended_for": "High-volume low-complexity tasks where cost matters more than quality",
    "comparison_to_opus_46": "This IS Claude Opus 4.6 - the benchmark being compared against",
    "can_replace_opus_46": "N/A - This is the reference model",
    "replacement_confidence_score": 10,
    "replacement_tradeoffs": "N/A - Reference model",
    "cost_comparison_vs_opus": "Reference pricing ($5/$15 per 1M)",
    "uncertain": ["swe_bench_verified_score", "swe_bench_full_score", "swe_bench_lite_score"]
}

with open('results/Claude_Opus_4.6.json', 'w') as f:
    json.dump(research_data, f, indent=2)

print("Claude Opus 4.6 research complete")
PYTHON_EOF

python3 /tmp/opus_research.py

rm -f results/Claude_Opus_4.6.started