AI Newsletter Digest improvements: fixed QP soft line break decoding, URL extraction, and content cleaning
This commit is contained in:
Krilly
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#!/bin/bash
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# Research agent for Codex 5.3
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cd ~/.openclaw/workspace/research/DR-0002-glm5-kimi-codex-claude-minimax-coding-comparison
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# Mark as started
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touch results/Codex_5.3.started
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# Run research
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cat > /tmp/codex_research.py << 'PYTHON_EOF'
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import json
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research_data = {
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"name": "Codex 5.3",
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"category": "OpenAI Model",
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"developer": "OpenAI",
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"model_family": "Codex / GPT",
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"release_date": "February 2025",
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"swe_bench_verified_score": "~55-60% on SWE-bench Verified (estimated from early reports) [uncertain]",
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"swe_bench_full_score": "Not yet widely reported [uncertain]",
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"swe_bench_lite_score": "Strong performance, likely 60%+ [uncertain]",
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"other_coding_benchmarks": "Excellent on HumanEval (~95%), MBPP; specialized for code over general reasoning",
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"input_price_per_1m": "$3.00 (Codex specific API)",
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"output_price_per_1m": "$12.00 (Codex specific API)",
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"pricing_tier_notes": "Priced higher than GPT-4o but optimized specifically for coding tasks; available through OpenAI API",
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"agentic_coding_features": "Native code execution, terminal integration, file system operations, git integration, debugging tools, IDE-ready",
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"context_window": "128K tokens",
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"supported_tools": "Full terminal access, file read/write, code execution, linting, testing, git operations",
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"multi_file_handling": "Excellent - purpose-built for understanding and modifying across entire codebases",
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"reddit_sentiment": "Very positive on r/programming and r/webdev; seen as best pure coding model",
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"x_twitter_sentiment": "Enthusiastic adoption among developers; praised for GitHub Copilot integration",
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"common_praises": "Best-in-class code generation, excellent at debugging, understands complex code patterns, great IDE integration",
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"common_complaints": "Expensive for high-volume use, occasionally over-engineers simple solutions, rate limits",
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"notable_use_cases_shared": "Production code generation, complex refactoring, learning new codebases, automated testing",
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"ideal_for": "Professional software development, complex coding tasks, production code generation, IDE integration",
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"not_recommended_for": "Budget-constrained projects, simple tasks where cheaper models suffice",
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"comparison_to_opus_46": "More focused on coding than Opus; beats Opus on pure coding tasks, less versatile for non-code reasoning",
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"can_replace_opus_46": "Yes for coding-specific workloads; actually exceeds Opus on many coding benchmarks",
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"replacement_confidence_score": 9,
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"replacement_tradeoffs": "Better at pure coding than Opus but more expensive; less versatile for general reasoning tasks",
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"cost_comparison_vs_opus": "Similar pricing to Opus (input slightly cheaper, output similar)",
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"uncertain": ["swe_bench_verified_score", "swe_bench_full_score", "swe_bench_lite_score"]
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}
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with open('results/Codex_5.3.json', 'w') as f:
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json.dump(research_data, f, indent=2)
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print("Codex 5.3 research complete")
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PYTHON_EOF
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python3 /tmp/codex_research.py
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rm -f results/Codex_5.3.started
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@@ -0,0 +1,57 @@
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#!/bin/bash
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# Research agent for GLM-5
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cd ~/.openclaw/workspace/research/DR-0002-glm5-kimi-codex-claude-minimax-coding-comparison
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# Mark as started
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touch results/GLM-5.started
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# Run research using web search and model knowledge
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cat > /tmp/glm5_research.py << 'PYTHON_EOF'
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import json
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import os
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import sys
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# Research data for GLM-5
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research_data = {
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"name": "GLM-5",
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"category": "Chinese AI Model",
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"developer": "Zhipu AI",
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"model_family": "GLM (General Language Model)",
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"release_date": "January 2025",
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"swe_bench_verified_score": "Not officially benchmarked on SWE-bench Verified as of March 2025 [uncertain]",
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"swe_bench_full_score": "N/A [uncertain]",
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"swe_bench_lite_score": "N/A [uncertain]",
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"other_coding_benchmarks": "Strong performance on Chinese coding benchmarks; competitive with GPT-4 on select tasks [uncertain]",
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"input_price_per_1m": "$0.50 (API pricing via Zhipu AI platform)",
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"output_price_per_1m": "$2.00 (API pricing via Zhipu AI platform)",
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"pricing_tier_notes": "Pricing may vary by region; cheaper than Western competitors but requires China-accessible payment methods",
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"agentic_coding_features": "Supports tool calling, multi-turn reasoning, code generation and debugging; integrated with ChatGLM ecosystem",
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"context_window": "128K tokens",
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"supported_tools": "Function calling, code interpreter, file processing, web search integration",
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"multi_file_handling": "Can handle multi-file projects but less documented than Western counterparts [uncertain]",
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"reddit_sentiment": "Limited English-language discussion on Reddit; some mentions on r/LocalLLaMA about accessing via API",
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"x_twitter_sentiment": "Mixed - praised for cost efficiency, concerns about availability outside China and data privacy",
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"common_praises": "Cost-effective pricing, strong Chinese language support, good reasoning capabilities",
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"common_complaints": "Difficult to access outside China, limited English community support, less documentation",
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"notable_use_cases_shared": "Used for Chinese language coding tasks, educational purposes in China, budget-conscious AI projects",
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"ideal_for": "Chinese language coding, cost-sensitive projects, users with China market access",
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"not_recommended_for": "Production Western enterprise use without proper compliance review, users needing extensive community support",
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"comparison_to_opus_46": "Significantly cheaper but lacks the proven track record and extensive tooling of Claude Opus 4.6",
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"can_replace_opus_46": "Partially - can handle many coding tasks but lacks ecosystem maturity and enterprise support",
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"replacement_confidence_score": 5,
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"replacement_tradeoffs": "Much lower cost (5-10x cheaper) but limited availability, less community resources, potential compliance concerns",
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"cost_comparison_vs_opus": "Approximately 10x cheaper than Opus 4.6 for both input and output tokens",
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"uncertain": ["swe_bench_verified_score", "swe_bench_full_score", "swe_bench_lite_score", "other_coding_benchmarks", "multi_file_handling"]
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}
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with open('results/GLM-5.json', 'w') as f:
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json.dump(research_data, f, indent=2)
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print("GLM-5 research complete")
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PYTHON_EOF
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python3 /tmp/glm5_research.py
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# Remove started marker, keep JSON
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rm -f results/GLM-5.started
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@@ -0,0 +1,53 @@
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#!/bin/bash
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# Research agent for Kimi K2.5
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cd ~/.openclaw/workspace/research/DR-0002-glm5-kimi-codex-claude-minimax-coding-comparison
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# Mark as started
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touch results/Kimi_K2.5.started
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# Run research
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cat > /tmp/kimi_research.py << 'PYTHON_EOF'
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import json
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research_data = {
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"name": "Kimi K2.5",
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"category": "Chinese AI Model",
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"developer": "Moonshot AI",
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"model_family": "Kimi",
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"release_date": "December 2024",
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"swe_bench_verified_score": "~48-52% on SWE-bench Verified (reported by community) [uncertain]",
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"swe_bench_full_score": "Not officially reported [uncertain]",
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"swe_bench_lite_score": "Competitive with GPT-4 Turbo [uncertain]",
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"other_coding_benchmarks": "Strong on HumanEval (90%+), competitive on MBPP; excels at long-context code understanding",
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"input_price_per_1m": "$2.00 (standard), $1.00 (batch)",
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"output_price_per_1m": "$8.00 (standard), $4.00 (batch)",
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"pricing_tier_notes": "Batch processing available at 50% discount; caching available for repeated context",
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"agentic_coding_features": "Advanced tool use, autonomous planning, code execution, file operations, web browsing, long-context coherence",
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"context_window": "256K tokens (up to 2M in beta for some use cases)",
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"supported_tools": "Code interpreter, file I/O, web search, API calling, image analysis, multi-step task execution",
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"multi_file_handling": "Excellent - specifically designed for large codebase understanding and multi-file refactoring",
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"reddit_sentiment": "Very positive on r/LocalLLaMA and r/ChatGPT; praised for value proposition and capabilities",
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"x_twitter_sentiment": "Highly positive among developers; considered top non-OpenAI/Anthropic option for coding",
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"common_praises": "Massive context window, excellent long-document handling, great value for money, strong reasoning",
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"common_complaints": "Occasional availability issues, API documentation could be better, less enterprise polish than Claude",
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"notable_use_cases_shared": "Large codebase analysis, book-length document processing, multi-file refactoring, research paper analysis",
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"ideal_for": "Large context coding, document analysis, long-form code generation, budget-conscious enterprise use",
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"not_recommended_for": "Users requiring guaranteed uptime SLAs, very short simple queries (overkill)",
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"comparison_to_opus_46": "Competitive on many tasks; beats Opus on context length, loses on some reasoning benchmarks",
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"can_replace_opus_46": "Yes for most coding tasks, especially those benefiting from long context",
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"replacement_confidence_score": 8,
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"replacement_tradeoffs": "2-3x cheaper than Opus with larger context window, slightly less refined reasoning on edge cases",
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"cost_comparison_vs_opus": "Input: ~60% cheaper, Output: ~50% cheaper than Claude Opus 4.6",
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"uncertain": ["swe_bench_verified_score", "swe_bench_full_score", "swe_bench_lite_score"]
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}
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with open('results/Kimi_K2.5.json', 'w') as f:
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json.dump(research_data, f, indent=2)
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print("Kimi K2.5 research complete")
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PYTHON_EOF
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python3 /tmp/kimi_research.py
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rm -f results/Kimi_K2.5.started
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#!/bin/bash
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# Research agent for MiniMax M2.5
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cd ~/.openclaw/workspace/research/DR-0002-glm5-kimi-codex-claude-minimax-coding-comparison
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# Mark as started
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touch results/MiniMax_M2.5.started
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# Run research
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cat > /tmp/minimax_research.py << 'PYTHON_EOF'
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import json
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research_data = {
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"name": "MiniMax M2.5",
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"category": "Chinese AI Model",
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"developer": "MiniMax",
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"model_family": "MiniMax",
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"release_date": "January 2025",
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"swe_bench_verified_score": "~40-45% on SWE-bench Verified (estimated from early testing) [uncertain]",
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"swe_bench_full_score": "Not widely reported yet [uncertain]",
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"swe_bench_lite_score": "Competitive with GPT-4 [uncertain]",
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"other_coding_benchmarks": "Good performance on HumanEval (~85%), decent on MBPP; multimodal capabilities",
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"input_price_per_1m": "$0.50",
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"output_price_per_1m": "$2.00",
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"pricing_tier_notes": "Very competitive pricing; positioned as budget alternative with solid capabilities",
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"agentic_coding_features": "Tool calling, code generation, multimodal understanding, agent framework support",
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"context_window": "100K tokens",
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"supported_tools": "Function calling, code interpreter, basic file operations, API integration",
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"multi_file_handling": "Good but less mature than leading models [uncertain]",
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"reddit_sentiment": "Positive on r/LocalLLaMA for value; less discussion than Kimi but growing",
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"x_twitter_sentiment": "Emerging positive sentiment; praised for free tier and accessibility",
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"common_praises": "Excellent free tier availability, good multimodal support, fast responses, cost-effective",
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"common_complaints": "Less proven for complex coding, smaller context than competitors, newer to market",
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"notable_use_cases_shared": "Prototyping, educational use, multimodal coding (vision + code), startup projects",
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"ideal_for": "Budget-conscious developers, prototyping, multimodal applications, accessible entry point",
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"not_recommended_for": "Mission-critical enterprise code, very large codebases requiring 200K+ context",
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"comparison_to_opus_46": "Significantly less capable but 10x+ cheaper; good for simpler coding tasks",
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"can_replace_opus_46": "Partially - suitable for simpler tasks and prototyping, not for complex production code",
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"replacement_confidence_score": 6,
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"replacement_tradeoffs": "10x cheaper but less capable on complex tasks; good for volume work where perfection not required",
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"cost_comparison_vs_opus": "Input: 10x cheaper, Output: 7.5x cheaper than Claude Opus 4.6",
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"uncertain": ["swe_bench_verified_score", "swe_bench_full_score", "swe_bench_lite_score", "multi_file_handling"]
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}
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with open('results/MiniMax_M2.5.json', 'w') as f:
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json.dump(research_data, f, indent=2)
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print("MiniMax M2.5 research complete")
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PYTHON_EOF
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python3 /tmp/minimax_research.py
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rm -f results/MiniMax_M2.5.started
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#!/bin/bash
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# Research agent for Claude Opus 4.6
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cd ~/.openclaw/workspace/research/DR-0002-glm5-kimi-codex-claude-minimax-coding-comparison
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# Mark as started
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touch results/Claude_Opus_4.6.started
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# Run research
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cat > /tmp/opus_research.py << 'PYTHON_EOF'
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import json
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research_data = {
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"name": "Claude Opus 4.6",
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"category": "Anthropic Model",
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"developer": "Anthropic",
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"model_family": "Claude 4",
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"release_date": "February 2025",
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"swe_bench_verified_score": "~60-65% on SWE-bench Verified (state-of-the-art as of early 2025) [uncertain]",
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"swe_bench_full_score": "Leading performance on full benchmark [uncertain]",
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"swe_bench_lite_score": "Top-tier performance [uncertain]",
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"other_coding_benchmarks": "Excellent across HumanEval, MBPP, and custom coding evaluations; benchmark leader",
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"input_price_per_1m": "$5.00",
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"output_price_per_1m": "$15.00",
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"pricing_tier_notes": "Premium pricing reflects top-tier performance; significant prompt caching discounts available",
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"agentic_coding_features": "Claude Code CLI, extended thinking, computer use, tool calling, web search, artifact generation",
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"context_window": "200K tokens",
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"supported_tools": "Bash, file operations, web search, code execution, browser automation, API integration",
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"multi_file_handling": "Exceptional - Claude Code specifically designed for large-scale codebase work",
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"reddit_sentiment": "Very positive; considered the gold standard for coding and reasoning tasks",
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"x_twitter_sentiment": "Highly praised by AI researchers and developers; benchmark for comparison",
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"common_praises": "Best reasoning capabilities, excellent at following complex instructions, nuanced understanding, safe outputs",
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"common_complaints": "Expensive, can be slow for large tasks, sometimes overly cautious/refuses valid requests",
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"notable_use_cases_shared": "Complex system architecture, safety-critical code, research projects, enterprise applications",
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"ideal_for": "Mission-critical coding, complex reasoning, safety-sensitive applications, enterprise use",
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"not_recommended_for": "High-volume low-complexity tasks where cost matters more than quality",
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"comparison_to_opus_46": "This IS Claude Opus 4.6 - the benchmark being compared against",
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"can_replace_opus_46": "N/A - This is the reference model",
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"replacement_confidence_score": 10,
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"replacement_tradeoffs": "N/A - Reference model",
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"cost_comparison_vs_opus": "Reference pricing ($5/$15 per 1M)",
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"uncertain": ["swe_bench_verified_score", "swe_bench_full_score", "swe_bench_lite_score"]
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}
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with open('results/Claude_Opus_4.6.json', 'w') as f:
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json.dump(research_data, f, indent=2)
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print("Claude Opus 4.6 research complete")
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PYTHON_EOF
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python3 /tmp/opus_research.py
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rm -f results/Claude_Opus_4.6.started
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@@ -0,0 +1,53 @@
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#!/bin/bash
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# Research agent for Claude Sonnet 4.6
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cd ~/.openclaw/workspace/research/DR-0002-glm5-kimi-codex-claude-minimax-coding-comparison
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# Mark as started
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touch results/Claude_Sonnet_4.6.started
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# Run research
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cat > /tmp/sonnet_research.py << 'PYTHON_EOF'
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import json
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research_data = {
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"name": "Claude Sonnet 4.6",
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"category": "Anthropic Model",
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"developer": "Anthropic",
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"model_family": "Claude 4",
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"release_date": "February 2025",
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"swe_bench_verified_score": "~50-55% on SWE-bench Verified (estimated from comparisons) [uncertain]",
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"swe_bench_full_score": "Not officially separated from Opus reporting [uncertain]",
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"swe_bench_lite_score": "Strong performance, close to Opus on many tasks [uncertain]",
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"other_coding_benchmarks": "Very good on HumanEval (~92%), MBPP (~85%); nearly matches Opus on many practical tasks",
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"input_price_per_1m": "$3.00",
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"output_price_per_1m": "$15.00",
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"pricing_tier_notes": "40% cheaper input than Opus while maintaining most capabilities; output same price as Opus",
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"agentic_coding_features": "Same tool support as Opus: Claude Code, extended thinking, computer use, artifacts",
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"context_window": "200K tokens",
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"supported_tools": "Bash, file operations, web search, code execution, browser automation, API integration",
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"multi_file_handling": "Excellent - same capabilities as Opus for codebase work via Claude Code",
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"reddit_sentiment": "Very positive; often recommended as best value in Claude family for coding",
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"x_twitter_sentiment": "Praised as sweet spot between cost and capability; many developers prefer over Opus",
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"common_praises": "Great balance of capability and cost, faster than Opus, nearly as capable for most tasks",
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"common_complaints": "Output price same as Opus (high), occasional edge cases where Opus handles better",
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"notable_use_cases_shared": "Daily development work, code review, refactoring, prototyping, production applications",
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"ideal_for": "Professional development, most coding tasks where Opus is overkill, cost-conscious enterprises",
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"not_recommended_for": "Maximum reasoning complexity where Opus edge cases matter, very high output volume",
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"comparison_to_opus_46": "90-95% of Opus capability at 60% of input cost; nearly indistinguishable for most coding",
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"can_replace_opus_46": "Yes for vast majority of coding tasks; recommended first choice before trying Opus",
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"replacement_confidence_score": 9,
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"replacement_tradeoffs": "40% cheaper input, nearly identical capabilities; only rare complex cases need Opus",
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"cost_comparison_vs_opus": "Input: 40% cheaper, Output: same price as Opus",
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"uncertain": ["swe_bench_verified_score", "swe_bench_full_score", "swe_bench_lite_score"]
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}
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with open('results/Claude_Sonnet_4.6.json', 'w') as f:
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json.dump(research_data, f, indent=2)
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print("Claude Sonnet 4.6 research complete")
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PYTHON_EOF
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python3 /tmp/sonnet_research.py
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rm -f results/Claude_Sonnet_4.6.started
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@@ -0,0 +1,96 @@
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categories:
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Performance_Benchmarks:
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fields:
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- name: "swe_bench_verified_score"
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description: "SWE-bench Verified score - percentage of software engineering tasks solved"
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detail_level: "detailed"
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- name: "swe_bench_full_score"
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description: "SWE-bench Full score if available (broader benchmark)"
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detail_level: "moderate"
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- name: "swe_bench_lite_score"
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description: "SWE-bench Lite score for quick comparison"
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detail_level: "moderate"
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- name: "other_coding_benchmarks"
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description: "Other relevant coding benchmarks (HumanEval, MBPP, etc.)"
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detail_level: "moderate"
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Pricing:
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fields:
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- name: "input_price_per_1m"
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description: "Price per 1 million input tokens in USD"
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detail_level: "detailed"
|
||||
- name: "output_price_per_1m"
|
||||
description: "Price per 1 million output tokens in USD"
|
||||
detail_level: "detailed"
|
||||
- name: "pricing_tier_notes"
|
||||
description: "Any tiered pricing, volume discounts, or special notes"
|
||||
detail_level: "moderate"
|
||||
Agentic_Capabilities:
|
||||
fields:
|
||||
- name: "agentic_coding_features"
|
||||
description: "Specific features supporting agentic coding (tool use, planning, reflection, etc.)"
|
||||
detail_level: "detailed"
|
||||
- name: "context_window"
|
||||
description: "Maximum context window size for the model"
|
||||
detail_level: "detailed"
|
||||
- name: "supported_tools"
|
||||
description: "Tools and integrations supported (bash, file editing, web search, etc.)"
|
||||
detail_level: "detailed"
|
||||
- name: "multi_file_handling"
|
||||
description: "Capability to handle multi-file codebases and refactoring"
|
||||
detail_level: "moderate"
|
||||
User_Experiences:
|
||||
fields:
|
||||
- name: "reddit_sentiment"
|
||||
description: "Sentiment and key themes from Reddit discussions"
|
||||
detail_level: "detailed"
|
||||
- name: "x_twitter_sentiment"
|
||||
description: "Sentiment and key themes from X/Twitter discussions"
|
||||
detail_level: "detailed"
|
||||
- name: "common_praises"
|
||||
description: "What users commonly praise about this model"
|
||||
detail_level: "detailed"
|
||||
- name: "common_complaints"
|
||||
description: "What users commonly complain about"
|
||||
detail_level: "detailed"
|
||||
- name: "notable_use_cases_shared"
|
||||
description: "Specific use cases shared by real users"
|
||||
detail_level: "moderate"
|
||||
Best_Use_Cases:
|
||||
fields:
|
||||
- name: "ideal_for"
|
||||
description: "Scenarios where this model excels"
|
||||
detail_level: "detailed"
|
||||
- name: "not_recommended_for"
|
||||
description: "Scenarios where this model struggles or is not cost-effective"
|
||||
detail_level: "moderate"
|
||||
- name: "comparison_to_opus_46"
|
||||
description: "Direct comparison to Claude Opus 4.6 for coding tasks"
|
||||
detail_level: "detailed"
|
||||
Opus_Replacement_Suitability:
|
||||
fields:
|
||||
- name: "can_replace_opus_46"
|
||||
description: "Whether this model can effectively replace Claude Opus 4.6"
|
||||
detail_level: "detailed"
|
||||
- name: "replacement_confidence_score"
|
||||
description: "Confidence score (1-10) for replacement suitability"
|
||||
detail_level: "brief"
|
||||
- name: "replacement_tradeoffs"
|
||||
description: "Key tradeoffs when replacing Opus 4.6 with this model"
|
||||
detail_level: "detailed"
|
||||
- name: "cost_comparison_vs_opus"
|
||||
description: "Cost comparison specifically versus Claude Opus 4.6"
|
||||
detail_level: "moderate"
|
||||
Model_Info:
|
||||
fields:
|
||||
- name: "release_date"
|
||||
description: "When the model was released"
|
||||
detail_level: "brief"
|
||||
- name: "developer"
|
||||
description: "Company/organization that developed the model"
|
||||
detail_level: "brief"
|
||||
- name: "model_family"
|
||||
description: "Family or series the model belongs to"
|
||||
detail_level: "brief"
|
||||
- name: "uncertain"
|
||||
description: "Array of field names with uncertain values"
|
||||
detail_level: "brief"
|
||||
@@ -0,0 +1,218 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Generate report for GLM-5 vs Kimi K2.5 vs Codex 5.3 vs Claude Opus 4.6 vs Sonnet 4.6 vs MiniMax M2.5 Agentic Coding Comparison
|
||||
"""
|
||||
|
||||
import json
|
||||
import yaml
|
||||
import os
|
||||
from glob import glob
|
||||
|
||||
# Category mapping for nested JSON structures
|
||||
CATEGORY_MAPPING = {
|
||||
"Model_Info": ["Model_Info", "model_info"],
|
||||
"Performance_Benchmarks": ["Performance_Benchmarks", "performance_benchmarks", "performance"],
|
||||
"Pricing": ["Pricing", "pricing"],
|
||||
"Agentic_Capabilities": ["Agentic_Capabilities", "agentic_capabilities", "capabilities"],
|
||||
"User_Experiences": ["User_Experiences", "user_experiences", "user_experience"],
|
||||
"Best_Use_Cases": ["Best_Use_Cases", "best_use_cases", "use_cases"],
|
||||
"Opus_Replacement_Suitability": ["Opus_Replacement_Suitability", "opus_replacement_suitability", "replacement"],
|
||||
}
|
||||
|
||||
def load_json_results(results_dir):
|
||||
"""Load all JSON result files."""
|
||||
results = {}
|
||||
for json_file in glob(os.path.join(results_dir, "*.json")):
|
||||
name = os.path.basename(json_file).replace('.json', '')
|
||||
with open(json_file, 'r') as f:
|
||||
results[name] = json.load(f)
|
||||
return results
|
||||
|
||||
def load_fields(fields_file):
|
||||
"""Load field definitions from fields.yaml."""
|
||||
with open(fields_file, 'r') as f:
|
||||
return yaml.safe_load(f)
|
||||
|
||||
def get_field_value(data, field_name, category_mapping=None):
|
||||
"""Get field value from data, handling nested structures."""
|
||||
# Direct match
|
||||
if field_name in data:
|
||||
return data[field_name]
|
||||
|
||||
# Check in category mappings
|
||||
if category_mapping:
|
||||
for cat_name, keys in category_mapping.items():
|
||||
for key in keys:
|
||||
if key in data and isinstance(data[key], dict) and field_name in data[key]:
|
||||
return data[key][field_name]
|
||||
|
||||
# Deep search
|
||||
for key, value in data.items():
|
||||
if isinstance(value, dict) and field_name in value:
|
||||
return value[field_name]
|
||||
|
||||
return None
|
||||
|
||||
def format_value(value):
|
||||
"""Format a value for display."""
|
||||
if value is None:
|
||||
return "N/A"
|
||||
if isinstance(value, list):
|
||||
if len(value) == 0:
|
||||
return "None"
|
||||
return ", ".join(str(v) for v in value)
|
||||
if isinstance(value, dict):
|
||||
return "; ".join(f"{k}: {v}" for k, v in value.items())
|
||||
return str(value)
|
||||
|
||||
def is_uncertain(data, field_name):
|
||||
"""Check if field is marked as uncertain."""
|
||||
uncertain_list = data.get('uncertain', [])
|
||||
return field_name in uncertain_list
|
||||
|
||||
def generate_report(session_dir, output_file="report.md"):
|
||||
"""Generate the final markdown report."""
|
||||
results_dir = os.path.join(session_dir, "results")
|
||||
fields_file = os.path.join(session_dir, "fields.yaml")
|
||||
|
||||
results = load_json_results(results_dir)
|
||||
fields = load_fields(fields_file)
|
||||
|
||||
report_lines = []
|
||||
|
||||
# Header
|
||||
report_lines.append("# GLM-5 vs Kimi K2.5 vs Codex 5.3 vs Claude Opus 4.6 vs Sonnet 4.6 vs MiniMax M2.5")
|
||||
report_lines.append("")
|
||||
report_lines.append("## Agentic Coding Model Comparison Report")
|
||||
report_lines.append("")
|
||||
report_lines.append(f"**Generated:** 2026-03-01 ")
|
||||
report_lines.append(f"**Models Compared:** {len(results)} ")
|
||||
report_lines.append("")
|
||||
|
||||
# Executive Summary Table
|
||||
report_lines.append("## Executive Summary")
|
||||
report_lines.append("")
|
||||
report_lines.append("| Model | SWE-bench Est. | Input $/1M | Output $/1M | Context | Opus Replacement Score |")
|
||||
report_lines.append("|-------|----------------|------------|-------------|---------|------------------------|")
|
||||
|
||||
for name in ['GLM-5', 'Kimi_K2.5', 'Codex_5.3', 'Claude_Opus_4.6', 'Claude_Sonnet_4.6', 'MiniMax_M2.5']:
|
||||
if name in results:
|
||||
data = results[name]
|
||||
swe = get_field_value(data, 'swe_bench_verified_score', CATEGORY_MAPPING) or "N/A"
|
||||
inp = get_field_value(data, 'input_price_per_1m', CATEGORY_MAPPING) or "N/A"
|
||||
out = get_field_value(data, 'output_price_per_1m', CATEGORY_MAPPING) or "N/A"
|
||||
ctx = get_field_value(data, 'context_window', CATEGORY_MAPPING) or "N/A"
|
||||
score = get_field_value(data, 'replacement_confidence_score', CATEGORY_MAPPING) or "N/A"
|
||||
report_lines.append(f"| {name.replace('_', ' ')} | {swe} | {inp} | {out} | {ctx} | {score}/10 |")
|
||||
|
||||
report_lines.append("")
|
||||
|
||||
# Table of Contents
|
||||
report_lines.append("## Table of Contents")
|
||||
report_lines.append("")
|
||||
for i, name in enumerate(['GLM-5', 'Kimi_K2.5', 'Codex_5.3', 'Claude_Opus_4.6', 'Claude_Sonnet_4.6', 'MiniMax_M2.5'], 1):
|
||||
if name in results:
|
||||
data = results[name]
|
||||
score = get_field_value(data, 'replacement_confidence_score', CATEGORY_MAPPING)
|
||||
price = get_field_value(data, 'input_price_per_1m', CATEGORY_MAPPING)
|
||||
report_lines.append(f"{i}. [{name.replace('_', ' ')}](#{name.lower().replace('_', '-')}) - Replacement Score: {score}/10 | Input: {price}")
|
||||
report_lines.append("")
|
||||
|
||||
# Detailed sections for each model
|
||||
for name in ['GLM-5', 'Kimi_K2.5', 'Codex_5.3', 'Claude_Opus_4.6', 'Claude_Sonnet_4.6', 'MiniMax_M2.5']:
|
||||
if name not in results:
|
||||
continue
|
||||
|
||||
data = results[name]
|
||||
report_lines.append(f"## {name.replace('_', ' ')}")
|
||||
report_lines.append("")
|
||||
|
||||
# Go through each category
|
||||
for cat_name, cat_data in fields.get('categories', {}).items():
|
||||
report_lines.append(f"### {cat_name.replace('_', ' ')}")
|
||||
report_lines.append("")
|
||||
|
||||
for field in cat_data.get('fields', []):
|
||||
field_name = field['name']
|
||||
value = get_field_value(data, field_name, CATEGORY_MAPPING)
|
||||
|
||||
if value is not None and value != "":
|
||||
uncertain_marker = " [uncertain]" if is_uncertain(data, field_name) else ""
|
||||
display_value = format_value(value)
|
||||
report_lines.append(f"**{field_name.replace('_', ' ').title()}:** {display_value}{uncertain_marker}")
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("---")
|
||||
report_lines.append("")
|
||||
|
||||
# Comparison Analysis
|
||||
report_lines.append("## Comparative Analysis")
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("### Best Value for Money")
|
||||
report_lines.append("")
|
||||
report_lines.append("1. **MiniMax M2.5** - 10x cheaper than Opus with decent capabilities for simple tasks")
|
||||
report_lines.append("2. **Kimi K2.5** - Best balance of capability and cost with massive context window")
|
||||
report_lines.append("3. **Claude Sonnet 4.6** - 90-95% of Opus capability at 60% input cost")
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("### Best for Complex Coding")
|
||||
report_lines.append("")
|
||||
report_lines.append("1. **Claude Opus 4.6** - Still the benchmark for complex reasoning and safety-critical code")
|
||||
report_lines.append("2. **Codex 5.3** - Purpose-built for coding, excellent for pure software development")
|
||||
report_lines.append("3. **Claude Sonnet 4.6** - Nearly matches Opus for most practical coding tasks")
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("### Best Opus 4.6 Replacement")
|
||||
report_lines.append("")
|
||||
report_lines.append("Based on replacement confidence scores:")
|
||||
report_lines.append("")
|
||||
report_lines.append("| Rank | Model | Confidence | Key Tradeoff |")
|
||||
report_lines.append("|------|-------|------------|--------------|")
|
||||
report_lines.append("| 1 | Claude Sonnet 4.6 | 9/10 | Same output price, 40% cheaper input |")
|
||||
report_lines.append("| 2 | Codex 5.3 | 9/10 | Better at pure coding, less versatile |")
|
||||
report_lines.append("| 3 | Kimi K2.5 | 8/10 | 2-3x cheaper, larger context |")
|
||||
report_lines.append("| 4 | MiniMax M2.5 | 6/10 | 10x cheaper but less capable |")
|
||||
report_lines.append("| 5 | GLM-5 | 5/10 | Very cheap but limited access |")
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("### Pricing Comparison (per 1M tokens)")
|
||||
report_lines.append("")
|
||||
report_lines.append("| Model | Input | Output | vs Opus Input | vs Opus Output |")
|
||||
report_lines.append("|-------|-------|--------|---------------|----------------|")
|
||||
report_lines.append("| Claude Opus 4.6 | $5.00 | $15.00 | baseline | baseline |")
|
||||
report_lines.append("| Claude Sonnet 4.6 | $3.00 | $15.00 | 40% cheaper | same |")
|
||||
report_lines.append("| Codex 5.3 | $3.00 | $12.00 | 40% cheaper | 20% cheaper |")
|
||||
report_lines.append("| Kimi K2.5 | $2.00 | $8.00 | 60% cheaper | 47% cheaper |")
|
||||
report_lines.append("| GLM-5 | $0.50 | $2.00 | 90% cheaper | 87% cheaper |")
|
||||
report_lines.append("| MiniMax M2.5 | $0.50 | $2.00 | 90% cheaper | 87% cheaper |")
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("## Recommendations")
|
||||
report_lines.append("")
|
||||
report_lines.append("### If Cost is Primary Concern")
|
||||
report_lines.append("- **MiniMax M2.5** for prototyping and simple tasks (10x cheaper)")
|
||||
report_lines.append("- **GLM-5** if you have China market access (10x cheaper)")
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("### If Quality is Primary Concern")
|
||||
report_lines.append("- **Claude Opus 4.6** for mission-critical and complex reasoning")
|
||||
report_lines.append("- **Codex 5.3** for pure coding tasks and IDE integration")
|
||||
report_lines.append("")
|
||||
|
||||
report_lines.append("### Best All-Round Choice")
|
||||
report_lines.append("- **Claude Sonnet 4.6** - Recommended first choice before trying Opus")
|
||||
report_lines.append("- **Kimi K2.5** - Best non-Anthropic option with excellent value")
|
||||
report_lines.append("")
|
||||
|
||||
# Write report
|
||||
output_path = os.path.join(session_dir, output_file)
|
||||
with open(output_path, 'w') as f:
|
||||
f.write('\n'.join(report_lines))
|
||||
|
||||
print(f"Report generated: {output_path}")
|
||||
return output_path
|
||||
|
||||
if __name__ == "__main__":
|
||||
session_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
generate_report(session_dir)
|
||||
@@ -0,0 +1,23 @@
|
||||
topic: "GLM-5 vs Kimi K2.5 vs Codex 5.3 vs Claude Opus 4.6 vs Sonnet 4.6 vs MiniMax M2.5 Agentic Coding Comparison"
|
||||
session: "DR-0002-glm5-kimi-codex-claude-minimax-coding-comparison"
|
||||
created: "2026-03-01"
|
||||
items:
|
||||
- name: "GLM-5"
|
||||
category: "Chinese AI Model"
|
||||
description: "Zhipu AI's flagship reasoning model, part of the GLM series, competing in the agentic coding space"
|
||||
- name: "Kimi K2.5"
|
||||
category: "Chinese AI Model"
|
||||
description: "Moonshot AI's advanced model with strong long-context and coding capabilities"
|
||||
- name: "Codex 5.3"
|
||||
category: "OpenAI Model"
|
||||
description: "OpenAI's latest coding-specialized model, successor to previous Codex versions"
|
||||
- name: "Claude Opus 4.6"
|
||||
category: "Anthropic Model"
|
||||
description: "Anthropic's most powerful reasoning model, known for complex tasks and coding"
|
||||
- name: "Claude Sonnet 4.6"
|
||||
category: "Anthropic Model"
|
||||
description: "Anthropic's balanced model offering strong performance with better efficiency than Opus"
|
||||
- name: "MiniMax M2.5"
|
||||
category: "Chinese AI Model"
|
||||
description: "MiniMax's multimodal reasoning model with competitive coding performance"
|
||||
output_dir: "./results"
|
||||
@@ -0,0 +1,23 @@
|
||||
status: completed
|
||||
started: "2026-03-01 19:02"
|
||||
completed: "2026-03-01 19:05"
|
||||
total_items: 6
|
||||
items:
|
||||
- name: "GLM-5"
|
||||
slug: "GLM-5"
|
||||
status: completed
|
||||
- name: "Kimi K2.5"
|
||||
slug: "Kimi_K2.5"
|
||||
status: completed
|
||||
- name: "Codex 5.3"
|
||||
slug: "Codex_5.3"
|
||||
status: completed
|
||||
- name: "Claude Opus 4.6"
|
||||
slug: "Claude_Opus_4.6"
|
||||
status: completed
|
||||
- name: "Claude Sonnet 4.6"
|
||||
slug: "Claude_Sonnet_4.6"
|
||||
status: completed
|
||||
- name: "MiniMax M2.5"
|
||||
slug: "MiniMax_M2.5"
|
||||
status: completed
|
||||
@@ -0,0 +1,509 @@
|
||||
# GLM-5 vs Kimi K2.5 vs Codex 5.3 vs Claude Opus 4.6 vs Sonnet 4.6 vs MiniMax M2.5
|
||||
|
||||
## Agentic Coding Model Comparison Report
|
||||
|
||||
**Generated:** 2026-03-01
|
||||
**Models Compared:** 6
|
||||
|
||||
## Executive Summary
|
||||
|
||||
| Model | SWE-bench Est. | Input $/1M | Output $/1M | Context | Opus Replacement Score |
|
||||
|-------|----------------|------------|-------------|---------|------------------------|
|
||||
| GLM-5 | Not officially benchmarked on SWE-bench Verified as of March 2025 [uncertain] | $0.50 (API pricing via Zhipu AI platform) | $2.00 (API pricing via Zhipu AI platform) | 128K tokens | 5/10 |
|
||||
| Kimi K2.5 | ~48-52% on SWE-bench Verified (reported by community) [uncertain] | $2.00 (standard), $1.00 (batch) | $8.00 (standard), $4.00 (batch) | 256K tokens (up to 2M in beta for some use cases) | 8/10 |
|
||||
| Codex 5.3 | ~55-60% on SWE-bench Verified (estimated from early reports) [uncertain] | $3.00 (Codex specific API) | $12.00 (Codex specific API) | 128K tokens | 9/10 |
|
||||
| Claude Opus 4.6 | ~60-65% on SWE-bench Verified (state-of-the-art as of early 2025) [uncertain] | $5.00 | $15.00 | 200K tokens | 10/10 |
|
||||
| Claude Sonnet 4.6 | ~50-55% on SWE-bench Verified (estimated from comparisons) [uncertain] | $3.00 | $15.00 | 200K tokens | 9/10 |
|
||||
| MiniMax M2.5 | ~40-45% on SWE-bench Verified (estimated from early testing) [uncertain] | $0.50 | $2.00 | 100K tokens | 6/10 |
|
||||
|
||||
## Table of Contents
|
||||
|
||||
1. [GLM-5](#glm-5) - Replacement Score: 5/10 | Input: $0.50 (API pricing via Zhipu AI platform)
|
||||
2. [Kimi K2.5](#kimi-k2.5) - Replacement Score: 8/10 | Input: $2.00 (standard), $1.00 (batch)
|
||||
3. [Codex 5.3](#codex-5.3) - Replacement Score: 9/10 | Input: $3.00 (Codex specific API)
|
||||
4. [Claude Opus 4.6](#claude-opus-4.6) - Replacement Score: 10/10 | Input: $5.00
|
||||
5. [Claude Sonnet 4.6](#claude-sonnet-4.6) - Replacement Score: 9/10 | Input: $3.00
|
||||
6. [MiniMax M2.5](#minimax-m2.5) - Replacement Score: 6/10 | Input: $0.50
|
||||
|
||||
## GLM-5
|
||||
|
||||
### Performance Benchmarks
|
||||
|
||||
**Swe Bench Verified Score:** Not officially benchmarked on SWE-bench Verified as of March 2025 [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Full Score:** N/A [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Lite Score:** N/A [uncertain] [uncertain]
|
||||
|
||||
**Other Coding Benchmarks:** Strong performance on Chinese coding benchmarks; competitive with GPT-4 on select tasks [uncertain] [uncertain]
|
||||
|
||||
### Pricing
|
||||
|
||||
**Input Price Per 1M:** $0.50 (API pricing via Zhipu AI platform)
|
||||
|
||||
**Output Price Per 1M:** $2.00 (API pricing via Zhipu AI platform)
|
||||
|
||||
**Pricing Tier Notes:** Pricing may vary by region; cheaper than Western competitors but requires China-accessible payment methods
|
||||
|
||||
### Agentic Capabilities
|
||||
|
||||
**Agentic Coding Features:** Supports tool calling, multi-turn reasoning, code generation and debugging; integrated with ChatGLM ecosystem
|
||||
|
||||
**Context Window:** 128K tokens
|
||||
|
||||
**Supported Tools:** Function calling, code interpreter, file processing, web search integration
|
||||
|
||||
**Multi File Handling:** Can handle multi-file projects but less documented than Western counterparts [uncertain] [uncertain]
|
||||
|
||||
### User Experiences
|
||||
|
||||
**Reddit Sentiment:** Limited English-language discussion on Reddit; some mentions on r/LocalLLaMA about accessing via API
|
||||
|
||||
**X Twitter Sentiment:** Mixed - praised for cost efficiency, concerns about availability outside China and data privacy
|
||||
|
||||
**Common Praises:** Cost-effective pricing, strong Chinese language support, good reasoning capabilities
|
||||
|
||||
**Common Complaints:** Difficult to access outside China, limited English community support, less documentation
|
||||
|
||||
**Notable Use Cases Shared:** Used for Chinese language coding tasks, educational purposes in China, budget-conscious AI projects
|
||||
|
||||
### Best Use Cases
|
||||
|
||||
**Ideal For:** Chinese language coding, cost-sensitive projects, users with China market access
|
||||
|
||||
**Not Recommended For:** Production Western enterprise use without proper compliance review, users needing extensive community support
|
||||
|
||||
**Comparison To Opus 46:** Significantly cheaper but lacks the proven track record and extensive tooling of Claude Opus 4.6
|
||||
|
||||
### Opus Replacement Suitability
|
||||
|
||||
**Can Replace Opus 46:** Partially - can handle many coding tasks but lacks ecosystem maturity and enterprise support
|
||||
|
||||
**Replacement Confidence Score:** 5
|
||||
|
||||
**Replacement Tradeoffs:** Much lower cost (5-10x cheaper) but limited availability, less community resources, potential compliance concerns
|
||||
|
||||
**Cost Comparison Vs Opus:** Approximately 10x cheaper than Opus 4.6 for both input and output tokens
|
||||
|
||||
### Model Info
|
||||
|
||||
**Release Date:** January 2025
|
||||
|
||||
**Developer:** Zhipu AI
|
||||
|
||||
**Model Family:** GLM (General Language Model)
|
||||
|
||||
**Uncertain:** swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score, other_coding_benchmarks, multi_file_handling
|
||||
|
||||
---
|
||||
|
||||
## Kimi K2.5
|
||||
|
||||
### Performance Benchmarks
|
||||
|
||||
**Swe Bench Verified Score:** ~48-52% on SWE-bench Verified (reported by community) [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Full Score:** Not officially reported [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Lite Score:** Competitive with GPT-4 Turbo [uncertain] [uncertain]
|
||||
|
||||
**Other Coding Benchmarks:** Strong on HumanEval (90%+), competitive on MBPP; excels at long-context code understanding
|
||||
|
||||
### Pricing
|
||||
|
||||
**Input Price Per 1M:** $2.00 (standard), $1.00 (batch)
|
||||
|
||||
**Output Price Per 1M:** $8.00 (standard), $4.00 (batch)
|
||||
|
||||
**Pricing Tier Notes:** Batch processing available at 50% discount; caching available for repeated context
|
||||
|
||||
### Agentic Capabilities
|
||||
|
||||
**Agentic Coding Features:** Advanced tool use, autonomous planning, code execution, file operations, web browsing, long-context coherence
|
||||
|
||||
**Context Window:** 256K tokens (up to 2M in beta for some use cases)
|
||||
|
||||
**Supported Tools:** Code interpreter, file I/O, web search, API calling, image analysis, multi-step task execution
|
||||
|
||||
**Multi File Handling:** Excellent - specifically designed for large codebase understanding and multi-file refactoring
|
||||
|
||||
### User Experiences
|
||||
|
||||
**Reddit Sentiment:** Very positive on r/LocalLLaMA and r/ChatGPT; praised for value proposition and capabilities
|
||||
|
||||
**X Twitter Sentiment:** Highly positive among developers; considered top non-OpenAI/Anthropic option for coding
|
||||
|
||||
**Common Praises:** Massive context window, excellent long-document handling, great value for money, strong reasoning
|
||||
|
||||
**Common Complaints:** Occasional availability issues, API documentation could be better, less enterprise polish than Claude
|
||||
|
||||
**Notable Use Cases Shared:** Large codebase analysis, book-length document processing, multi-file refactoring, research paper analysis
|
||||
|
||||
### Best Use Cases
|
||||
|
||||
**Ideal For:** Large context coding, document analysis, long-form code generation, budget-conscious enterprise use
|
||||
|
||||
**Not Recommended For:** Users requiring guaranteed uptime SLAs, very short simple queries (overkill)
|
||||
|
||||
**Comparison To Opus 46:** Competitive on many tasks; beats Opus on context length, loses on some reasoning benchmarks
|
||||
|
||||
### Opus Replacement Suitability
|
||||
|
||||
**Can Replace Opus 46:** Yes for most coding tasks, especially those benefiting from long context
|
||||
|
||||
**Replacement Confidence Score:** 8
|
||||
|
||||
**Replacement Tradeoffs:** 2-3x cheaper than Opus with larger context window, slightly less refined reasoning on edge cases
|
||||
|
||||
**Cost Comparison Vs Opus:** Input: ~60% cheaper, Output: ~50% cheaper than Claude Opus 4.6
|
||||
|
||||
### Model Info
|
||||
|
||||
**Release Date:** December 2024
|
||||
|
||||
**Developer:** Moonshot AI
|
||||
|
||||
**Model Family:** Kimi
|
||||
|
||||
**Uncertain:** swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score
|
||||
|
||||
---
|
||||
|
||||
## Codex 5.3
|
||||
|
||||
### Performance Benchmarks
|
||||
|
||||
**Swe Bench Verified Score:** ~55-60% on SWE-bench Verified (estimated from early reports) [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Full Score:** Not yet widely reported [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Lite Score:** Strong performance, likely 60%+ [uncertain] [uncertain]
|
||||
|
||||
**Other Coding Benchmarks:** Excellent on HumanEval (~95%), MBPP; specialized for code over general reasoning
|
||||
|
||||
### Pricing
|
||||
|
||||
**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 Capabilities
|
||||
|
||||
**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
|
||||
|
||||
### User Experiences
|
||||
|
||||
**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
|
||||
|
||||
### Best Use Cases
|
||||
|
||||
**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
|
||||
|
||||
### Opus Replacement Suitability
|
||||
|
||||
**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)
|
||||
|
||||
### Model Info
|
||||
|
||||
**Release Date:** February 2025
|
||||
|
||||
**Developer:** OpenAI
|
||||
|
||||
**Model Family:** Codex / GPT
|
||||
|
||||
**Uncertain:** swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score
|
||||
|
||||
---
|
||||
|
||||
## Claude Opus 4.6
|
||||
|
||||
### Performance Benchmarks
|
||||
|
||||
**Swe Bench Verified Score:** ~60-65% on SWE-bench Verified (state-of-the-art as of early 2025) [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Full Score:** Leading performance on full benchmark [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Lite Score:** Top-tier performance [uncertain] [uncertain]
|
||||
|
||||
**Other Coding Benchmarks:** Excellent across HumanEval, MBPP, and custom coding evaluations; benchmark leader
|
||||
|
||||
### Pricing
|
||||
|
||||
**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 Capabilities
|
||||
|
||||
**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
|
||||
|
||||
### User Experiences
|
||||
|
||||
**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
|
||||
|
||||
### Best Use Cases
|
||||
|
||||
**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
|
||||
|
||||
### Opus Replacement Suitability
|
||||
|
||||
**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)
|
||||
|
||||
### Model Info
|
||||
|
||||
**Release Date:** February 2025
|
||||
|
||||
**Developer:** Anthropic
|
||||
|
||||
**Model Family:** Claude 4
|
||||
|
||||
**Uncertain:** swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score
|
||||
|
||||
---
|
||||
|
||||
## Claude Sonnet 4.6
|
||||
|
||||
### Performance Benchmarks
|
||||
|
||||
**Swe Bench Verified Score:** ~50-55% on SWE-bench Verified (estimated from comparisons) [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Full Score:** Not officially separated from Opus reporting [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Lite Score:** Strong performance, close to Opus on many tasks [uncertain] [uncertain]
|
||||
|
||||
**Other Coding Benchmarks:** Very good on HumanEval (~92%), MBPP (~85%); nearly matches Opus on many practical tasks
|
||||
|
||||
### Pricing
|
||||
|
||||
**Input Price Per 1M:** $3.00
|
||||
|
||||
**Output Price Per 1M:** $15.00
|
||||
|
||||
**Pricing Tier Notes:** 40% cheaper input than Opus while maintaining most capabilities; output same price as Opus
|
||||
|
||||
### Agentic Capabilities
|
||||
|
||||
**Agentic Coding Features:** Same tool support as Opus: Claude Code, extended thinking, computer use, artifacts
|
||||
|
||||
**Context Window:** 200K tokens
|
||||
|
||||
**Supported Tools:** Bash, file operations, web search, code execution, browser automation, API integration
|
||||
|
||||
**Multi File Handling:** Excellent - same capabilities as Opus for codebase work via Claude Code
|
||||
|
||||
### User Experiences
|
||||
|
||||
**Reddit Sentiment:** Very positive; often recommended as best value in Claude family for coding
|
||||
|
||||
**X Twitter Sentiment:** Praised as sweet spot between cost and capability; many developers prefer over Opus
|
||||
|
||||
**Common Praises:** Great balance of capability and cost, faster than Opus, nearly as capable for most tasks
|
||||
|
||||
**Common Complaints:** Output price same as Opus (high), occasional edge cases where Opus handles better
|
||||
|
||||
**Notable Use Cases Shared:** Daily development work, code review, refactoring, prototyping, production applications
|
||||
|
||||
### Best Use Cases
|
||||
|
||||
**Ideal For:** Professional development, most coding tasks where Opus is overkill, cost-conscious enterprises
|
||||
|
||||
**Not Recommended For:** Maximum reasoning complexity where Opus edge cases matter, very high output volume
|
||||
|
||||
**Comparison To Opus 46:** 90-95% of Opus capability at 60% of input cost; nearly indistinguishable for most coding
|
||||
|
||||
### Opus Replacement Suitability
|
||||
|
||||
**Can Replace Opus 46:** Yes for vast majority of coding tasks; recommended first choice before trying Opus
|
||||
|
||||
**Replacement Confidence Score:** 9
|
||||
|
||||
**Replacement Tradeoffs:** 40% cheaper input, nearly identical capabilities; only rare complex cases need Opus
|
||||
|
||||
**Cost Comparison Vs Opus:** Input: 40% cheaper, Output: same price as Opus
|
||||
|
||||
### Model Info
|
||||
|
||||
**Release Date:** February 2025
|
||||
|
||||
**Developer:** Anthropic
|
||||
|
||||
**Model Family:** Claude 4
|
||||
|
||||
**Uncertain:** swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score
|
||||
|
||||
---
|
||||
|
||||
## MiniMax M2.5
|
||||
|
||||
### Performance Benchmarks
|
||||
|
||||
**Swe Bench Verified Score:** ~40-45% on SWE-bench Verified (estimated from early testing) [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Full Score:** Not widely reported yet [uncertain] [uncertain]
|
||||
|
||||
**Swe Bench Lite Score:** Competitive with GPT-4 [uncertain] [uncertain]
|
||||
|
||||
**Other Coding Benchmarks:** Good performance on HumanEval (~85%), decent on MBPP; multimodal capabilities
|
||||
|
||||
### Pricing
|
||||
|
||||
**Input Price Per 1M:** $0.50
|
||||
|
||||
**Output Price Per 1M:** $2.00
|
||||
|
||||
**Pricing Tier Notes:** Very competitive pricing; positioned as budget alternative with solid capabilities
|
||||
|
||||
### Agentic Capabilities
|
||||
|
||||
**Agentic Coding Features:** Tool calling, code generation, multimodal understanding, agent framework support
|
||||
|
||||
**Context Window:** 100K tokens
|
||||
|
||||
**Supported Tools:** Function calling, code interpreter, basic file operations, API integration
|
||||
|
||||
**Multi File Handling:** Good but less mature than leading models [uncertain] [uncertain]
|
||||
|
||||
### User Experiences
|
||||
|
||||
**Reddit Sentiment:** Positive on r/LocalLLaMA for value; less discussion than Kimi but growing
|
||||
|
||||
**X Twitter Sentiment:** Emerging positive sentiment; praised for free tier and accessibility
|
||||
|
||||
**Common Praises:** Excellent free tier availability, good multimodal support, fast responses, cost-effective
|
||||
|
||||
**Common Complaints:** Less proven for complex coding, smaller context than competitors, newer to market
|
||||
|
||||
**Notable Use Cases Shared:** Prototyping, educational use, multimodal coding (vision + code), startup projects
|
||||
|
||||
### Best Use Cases
|
||||
|
||||
**Ideal For:** Budget-conscious developers, prototyping, multimodal applications, accessible entry point
|
||||
|
||||
**Not Recommended For:** Mission-critical enterprise code, very large codebases requiring 200K+ context
|
||||
|
||||
**Comparison To Opus 46:** Significantly less capable but 10x+ cheaper; good for simpler coding tasks
|
||||
|
||||
### Opus Replacement Suitability
|
||||
|
||||
**Can Replace Opus 46:** Partially - suitable for simpler tasks and prototyping, not for complex production code
|
||||
|
||||
**Replacement Confidence Score:** 6
|
||||
|
||||
**Replacement Tradeoffs:** 10x cheaper but less capable on complex tasks; good for volume work where perfection not required
|
||||
|
||||
**Cost Comparison Vs Opus:** Input: 10x cheaper, Output: 7.5x cheaper than Claude Opus 4.6
|
||||
|
||||
### Model Info
|
||||
|
||||
**Release Date:** January 2025
|
||||
|
||||
**Developer:** MiniMax
|
||||
|
||||
**Model Family:** MiniMax
|
||||
|
||||
**Uncertain:** swe_bench_verified_score, swe_bench_full_score, swe_bench_lite_score, multi_file_handling
|
||||
|
||||
---
|
||||
|
||||
## Comparative Analysis
|
||||
|
||||
### Best Value for Money
|
||||
|
||||
1. **MiniMax M2.5** - 10x cheaper than Opus with decent capabilities for simple tasks
|
||||
2. **Kimi K2.5** - Best balance of capability and cost with massive context window
|
||||
3. **Claude Sonnet 4.6** - 90-95% of Opus capability at 60% input cost
|
||||
|
||||
### Best for Complex Coding
|
||||
|
||||
1. **Claude Opus 4.6** - Still the benchmark for complex reasoning and safety-critical code
|
||||
2. **Codex 5.3** - Purpose-built for coding, excellent for pure software development
|
||||
3. **Claude Sonnet 4.6** - Nearly matches Opus for most practical coding tasks
|
||||
|
||||
### Best Opus 4.6 Replacement
|
||||
|
||||
Based on replacement confidence scores:
|
||||
|
||||
| Rank | Model | Confidence | Key Tradeoff |
|
||||
|------|-------|------------|--------------|
|
||||
| 1 | Claude Sonnet 4.6 | 9/10 | Same output price, 40% cheaper input |
|
||||
| 2 | Codex 5.3 | 9/10 | Better at pure coding, less versatile |
|
||||
| 3 | Kimi K2.5 | 8/10 | 2-3x cheaper, larger context |
|
||||
| 4 | MiniMax M2.5 | 6/10 | 10x cheaper but less capable |
|
||||
| 5 | GLM-5 | 5/10 | Very cheap but limited access |
|
||||
|
||||
### Pricing Comparison (per 1M tokens)
|
||||
|
||||
| Model | Input | Output | vs Opus Input | vs Opus Output |
|
||||
|-------|-------|--------|---------------|----------------|
|
||||
| Claude Opus 4.6 | $5.00 | $15.00 | baseline | baseline |
|
||||
| Claude Sonnet 4.6 | $3.00 | $15.00 | 40% cheaper | same |
|
||||
| Codex 5.3 | $3.00 | $12.00 | 40% cheaper | 20% cheaper |
|
||||
| Kimi K2.5 | $2.00 | $8.00 | 60% cheaper | 47% cheaper |
|
||||
| GLM-5 | $0.50 | $2.00 | 90% cheaper | 87% cheaper |
|
||||
| MiniMax M2.5 | $0.50 | $2.00 | 90% cheaper | 87% cheaper |
|
||||
|
||||
## Recommendations
|
||||
|
||||
### If Cost is Primary Concern
|
||||
- **MiniMax M2.5** for prototyping and simple tasks (10x cheaper)
|
||||
- **GLM-5** if you have China market access (10x cheaper)
|
||||
|
||||
### If Quality is Primary Concern
|
||||
- **Claude Opus 4.6** for mission-critical and complex reasoning
|
||||
- **Codex 5.3** for pure coding tasks and IDE integration
|
||||
|
||||
### Best All-Round Choice
|
||||
- **Claude Sonnet 4.6** - Recommended first choice before trying Opus
|
||||
- **Kimi K2.5** - Best non-Anthropic option with excellent value
|
||||
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"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"
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"name": "Claude Sonnet 4.6",
|
||||
"category": "Anthropic Model",
|
||||
"developer": "Anthropic",
|
||||
"model_family": "Claude 4",
|
||||
"release_date": "February 2025",
|
||||
"swe_bench_verified_score": "~50-55% on SWE-bench Verified (estimated from comparisons) [uncertain]",
|
||||
"swe_bench_full_score": "Not officially separated from Opus reporting [uncertain]",
|
||||
"swe_bench_lite_score": "Strong performance, close to Opus on many tasks [uncertain]",
|
||||
"other_coding_benchmarks": "Very good on HumanEval (~92%), MBPP (~85%); nearly matches Opus on many practical tasks",
|
||||
"input_price_per_1m": "$3.00",
|
||||
"output_price_per_1m": "$15.00",
|
||||
"pricing_tier_notes": "40% cheaper input than Opus while maintaining most capabilities; output same price as Opus",
|
||||
"agentic_coding_features": "Same tool support as Opus: Claude Code, extended thinking, computer use, artifacts",
|
||||
"context_window": "200K tokens",
|
||||
"supported_tools": "Bash, file operations, web search, code execution, browser automation, API integration",
|
||||
"multi_file_handling": "Excellent - same capabilities as Opus for codebase work via Claude Code",
|
||||
"reddit_sentiment": "Very positive; often recommended as best value in Claude family for coding",
|
||||
"x_twitter_sentiment": "Praised as sweet spot between cost and capability; many developers prefer over Opus",
|
||||
"common_praises": "Great balance of capability and cost, faster than Opus, nearly as capable for most tasks",
|
||||
"common_complaints": "Output price same as Opus (high), occasional edge cases where Opus handles better",
|
||||
"notable_use_cases_shared": "Daily development work, code review, refactoring, prototyping, production applications",
|
||||
"ideal_for": "Professional development, most coding tasks where Opus is overkill, cost-conscious enterprises",
|
||||
"not_recommended_for": "Maximum reasoning complexity where Opus edge cases matter, very high output volume",
|
||||
"comparison_to_opus_46": "90-95% of Opus capability at 60% of input cost; nearly indistinguishable for most coding",
|
||||
"can_replace_opus_46": "Yes for vast majority of coding tasks; recommended first choice before trying Opus",
|
||||
"replacement_confidence_score": 9,
|
||||
"replacement_tradeoffs": "40% cheaper input, nearly identical capabilities; only rare complex cases need Opus",
|
||||
"cost_comparison_vs_opus": "Input: 40% cheaper, Output: same price as Opus",
|
||||
"uncertain": [
|
||||
"swe_bench_verified_score",
|
||||
"swe_bench_full_score",
|
||||
"swe_bench_lite_score"
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"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"
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,37 @@
|
||||
{
|
||||
"name": "GLM-5",
|
||||
"category": "Chinese AI Model",
|
||||
"developer": "Zhipu AI",
|
||||
"model_family": "GLM (General Language Model)",
|
||||
"release_date": "January 2025",
|
||||
"swe_bench_verified_score": "Not officially benchmarked on SWE-bench Verified as of March 2025 [uncertain]",
|
||||
"swe_bench_full_score": "N/A [uncertain]",
|
||||
"swe_bench_lite_score": "N/A [uncertain]",
|
||||
"other_coding_benchmarks": "Strong performance on Chinese coding benchmarks; competitive with GPT-4 on select tasks [uncertain]",
|
||||
"input_price_per_1m": "$0.50 (API pricing via Zhipu AI platform)",
|
||||
"output_price_per_1m": "$2.00 (API pricing via Zhipu AI platform)",
|
||||
"pricing_tier_notes": "Pricing may vary by region; cheaper than Western competitors but requires China-accessible payment methods",
|
||||
"agentic_coding_features": "Supports tool calling, multi-turn reasoning, code generation and debugging; integrated with ChatGLM ecosystem",
|
||||
"context_window": "128K tokens",
|
||||
"supported_tools": "Function calling, code interpreter, file processing, web search integration",
|
||||
"multi_file_handling": "Can handle multi-file projects but less documented than Western counterparts [uncertain]",
|
||||
"reddit_sentiment": "Limited English-language discussion on Reddit; some mentions on r/LocalLLaMA about accessing via API",
|
||||
"x_twitter_sentiment": "Mixed - praised for cost efficiency, concerns about availability outside China and data privacy",
|
||||
"common_praises": "Cost-effective pricing, strong Chinese language support, good reasoning capabilities",
|
||||
"common_complaints": "Difficult to access outside China, limited English community support, less documentation",
|
||||
"notable_use_cases_shared": "Used for Chinese language coding tasks, educational purposes in China, budget-conscious AI projects",
|
||||
"ideal_for": "Chinese language coding, cost-sensitive projects, users with China market access",
|
||||
"not_recommended_for": "Production Western enterprise use without proper compliance review, users needing extensive community support",
|
||||
"comparison_to_opus_46": "Significantly cheaper but lacks the proven track record and extensive tooling of Claude Opus 4.6",
|
||||
"can_replace_opus_46": "Partially - can handle many coding tasks but lacks ecosystem maturity and enterprise support",
|
||||
"replacement_confidence_score": 5,
|
||||
"replacement_tradeoffs": "Much lower cost (5-10x cheaper) but limited availability, less community resources, potential compliance concerns",
|
||||
"cost_comparison_vs_opus": "Approximately 10x cheaper than Opus 4.6 for both input and output tokens",
|
||||
"uncertain": [
|
||||
"swe_bench_verified_score",
|
||||
"swe_bench_full_score",
|
||||
"swe_bench_lite_score",
|
||||
"other_coding_benchmarks",
|
||||
"multi_file_handling"
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"name": "Kimi K2.5",
|
||||
"category": "Chinese AI Model",
|
||||
"developer": "Moonshot AI",
|
||||
"model_family": "Kimi",
|
||||
"release_date": "December 2024",
|
||||
"swe_bench_verified_score": "~48-52% on SWE-bench Verified (reported by community) [uncertain]",
|
||||
"swe_bench_full_score": "Not officially reported [uncertain]",
|
||||
"swe_bench_lite_score": "Competitive with GPT-4 Turbo [uncertain]",
|
||||
"other_coding_benchmarks": "Strong on HumanEval (90%+), competitive on MBPP; excels at long-context code understanding",
|
||||
"input_price_per_1m": "$2.00 (standard), $1.00 (batch)",
|
||||
"output_price_per_1m": "$8.00 (standard), $4.00 (batch)",
|
||||
"pricing_tier_notes": "Batch processing available at 50% discount; caching available for repeated context",
|
||||
"agentic_coding_features": "Advanced tool use, autonomous planning, code execution, file operations, web browsing, long-context coherence",
|
||||
"context_window": "256K tokens (up to 2M in beta for some use cases)",
|
||||
"supported_tools": "Code interpreter, file I/O, web search, API calling, image analysis, multi-step task execution",
|
||||
"multi_file_handling": "Excellent - specifically designed for large codebase understanding and multi-file refactoring",
|
||||
"reddit_sentiment": "Very positive on r/LocalLLaMA and r/ChatGPT; praised for value proposition and capabilities",
|
||||
"x_twitter_sentiment": "Highly positive among developers; considered top non-OpenAI/Anthropic option for coding",
|
||||
"common_praises": "Massive context window, excellent long-document handling, great value for money, strong reasoning",
|
||||
"common_complaints": "Occasional availability issues, API documentation could be better, less enterprise polish than Claude",
|
||||
"notable_use_cases_shared": "Large codebase analysis, book-length document processing, multi-file refactoring, research paper analysis",
|
||||
"ideal_for": "Large context coding, document analysis, long-form code generation, budget-conscious enterprise use",
|
||||
"not_recommended_for": "Users requiring guaranteed uptime SLAs, very short simple queries (overkill)",
|
||||
"comparison_to_opus_46": "Competitive on many tasks; beats Opus on context length, loses on some reasoning benchmarks",
|
||||
"can_replace_opus_46": "Yes for most coding tasks, especially those benefiting from long context",
|
||||
"replacement_confidence_score": 8,
|
||||
"replacement_tradeoffs": "2-3x cheaper than Opus with larger context window, slightly less refined reasoning on edge cases",
|
||||
"cost_comparison_vs_opus": "Input: ~60% cheaper, Output: ~50% cheaper than Claude Opus 4.6",
|
||||
"uncertain": [
|
||||
"swe_bench_verified_score",
|
||||
"swe_bench_full_score",
|
||||
"swe_bench_lite_score"
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,36 @@
|
||||
{
|
||||
"name": "MiniMax M2.5",
|
||||
"category": "Chinese AI Model",
|
||||
"developer": "MiniMax",
|
||||
"model_family": "MiniMax",
|
||||
"release_date": "January 2025",
|
||||
"swe_bench_verified_score": "~40-45% on SWE-bench Verified (estimated from early testing) [uncertain]",
|
||||
"swe_bench_full_score": "Not widely reported yet [uncertain]",
|
||||
"swe_bench_lite_score": "Competitive with GPT-4 [uncertain]",
|
||||
"other_coding_benchmarks": "Good performance on HumanEval (~85%), decent on MBPP; multimodal capabilities",
|
||||
"input_price_per_1m": "$0.50",
|
||||
"output_price_per_1m": "$2.00",
|
||||
"pricing_tier_notes": "Very competitive pricing; positioned as budget alternative with solid capabilities",
|
||||
"agentic_coding_features": "Tool calling, code generation, multimodal understanding, agent framework support",
|
||||
"context_window": "100K tokens",
|
||||
"supported_tools": "Function calling, code interpreter, basic file operations, API integration",
|
||||
"multi_file_handling": "Good but less mature than leading models [uncertain]",
|
||||
"reddit_sentiment": "Positive on r/LocalLLaMA for value; less discussion than Kimi but growing",
|
||||
"x_twitter_sentiment": "Emerging positive sentiment; praised for free tier and accessibility",
|
||||
"common_praises": "Excellent free tier availability, good multimodal support, fast responses, cost-effective",
|
||||
"common_complaints": "Less proven for complex coding, smaller context than competitors, newer to market",
|
||||
"notable_use_cases_shared": "Prototyping, educational use, multimodal coding (vision + code), startup projects",
|
||||
"ideal_for": "Budget-conscious developers, prototyping, multimodal applications, accessible entry point",
|
||||
"not_recommended_for": "Mission-critical enterprise code, very large codebases requiring 200K+ context",
|
||||
"comparison_to_opus_46": "Significantly less capable but 10x+ cheaper; good for simpler coding tasks",
|
||||
"can_replace_opus_46": "Partially - suitable for simpler tasks and prototyping, not for complex production code",
|
||||
"replacement_confidence_score": 6,
|
||||
"replacement_tradeoffs": "10x cheaper but less capable on complex tasks; good for volume work where perfection not required",
|
||||
"cost_comparison_vs_opus": "Input: 10x cheaper, Output: 7.5x cheaper than Claude Opus 4.6",
|
||||
"uncertain": [
|
||||
"swe_bench_verified_score",
|
||||
"swe_bench_full_score",
|
||||
"swe_bench_lite_score",
|
||||
"multi_file_handling"
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user