DevOps AI Toolkit

# Remediation AI Model Comparison Report **Generated**: 2025-10-16T15:45:18.466Z **Scenarios Analyzed**: 3 **Models Evaluated**: 10 **Total Datasets**: 36 ## Executive Summary ### 🏆 Overall Winner (AI Assessment) **vercel_claude-haiku-4-5-20251001** Claude Haiku 4.5 is the clear overall winner based on exceptional cross-scenario reliability and production readiness. It achieved: (1) 100% participation rate - zero failures across all scenarios, (2) Highest average score (0.948) with minimal variance (0.94-0.965), (3) #1 ranking in all three scenarios - unprecedented consistency, (4) Optimal production characteristics: 14-32s completion times, $3/1M cost efficiency, and zero timeout risks. Compared to alternatives: Gemini Flash failed 33% of scenarios despite lower cost; premium models (GPT-5 Pro, Gemini Pro) showed worse performance at 10-20x higher costs; and Mistral completely failed. Haiku demonstrates the critical principle that reliability and consistency matter more than peak performance - it never fails, always delivers top-tier quality, and operates within production constraints. For Kubernetes remediation workflows requiring diagnostic accuracy, tool execution reliability, and structured output formatting, Haiku provides the safest, most dependable choice with zero observed failure modes across automatic and manual workflows. ### 📊 AI Reliability Rankings 1. **vercel_claude-haiku-4-5-20251001** (99%) - 100% participation, 0.948 average score, 98.7% consistency - perfect reliability profile 2. **vercel_gpt-5** (98%) - 100% participation, 0.867 average score, 98.3% consistency - reliable but expensive 3. **vercel_grok-4** (98%) - 100% participation, 0.85 average score, 97.7% consistency - solid secondary option 4. **vercel_claude-sonnet-4-5-20250929** (97%) - 100% participation, 0.902 average score, 96.9% consistency - premium alternative to Haiku 5. **vercel_grok-4-fast-reasoning** (96%) - 100% participation, 0.875 average score, 96.0% consistency - best budget option 6. **vercel_deepseek-reasoner** (94%) - 100% participation, 0.808 average score, 94.4% consistency - budget-friendly with slower speeds 7. **vercel_gemini-2.5-flash** (66%) - 67% participation, 0.92 average score when successful - critical tool execution failure 8. **vercel_gemini-2.5-pro** (60%) - 67% participation, 0.552 average score - formatting and execution failures 9. **vercel_gpt-5-pro** (99%) - 100% participation, 0.733 average score - reliable completion but catastrophic latency and cost 10. **vercel_mistral-large-latest** (0%) - 0% participation - complete failure due to rate limiting ### 📋 Production Recommendations - **Primary Choice**: vercel_claude-haiku-4-5-20251001 - Best overall reliability (98.7%), optimal cost-performance ratio ($3/1M), fastest completion times (14-32s), zero failure modes. Recommended for all production Kubernetes remediation workflows. - **Secondary Option**: vercel_claude-sonnet-4-5-20250929 - Premium alternative with deeper diagnostics (96.9% reliability, $15/1M). Use when diagnostic depth justifies 5x cost increase over Haiku. vercel_grok-4-fast-reasoning - Budget alternative with strong reliability (96% reliability, $0.35/1M). Use for cost-sensitive deployments accepting 92% of Haiku's quality at 1/9th cost. - **Avoid for Production**: vercel_mistral-large-latest - 100% failure rate, completely non-functional, vercel_gemini-2.5-pro - 33% failure rate with formatting issues, unreliable at premium cost, vercel_gpt-5-pro - Catastrophic latency (10-22 minutes), timeout risks, poor cost justification ($67.50/1M for 7th place performance), vercel_gemini-2.5-flash - Tool execution failures in 33% of scenarios despite speed advantages **Specialized Use Cases:** - **budget_constrained_environments**: vercel_grok-4-fast-reasoning - $0.35/1M with 96% reliability and strong execution capabilities - **deep_diagnostic_analysis**: vercel_claude-sonnet-4-5-20250929 - Superior diagnostic depth with prompt caching efficiency - **cost_insensitive_workflows**: vercel_claude-haiku-4-5-20251001 - Even without cost constraints, Haiku provides best reliability and speed - **read_only_analysis**: vercel_gemini-2.5-flash - Only for analysis scenarios without tool execution requirements ($1.40/1M, 14-20s) ### 📊 Supplementary Statistics (Reference Only) | Model | Avg Score | Notes | |-------|-----------|-------| | vercel_claude-haiku-4-5-20251001 | 0.948 | See AI assessment above | | vercel_claude-sonnet-4-5-20250929 | 0.902 | See AI assessment above | | vercel_grok-4-fast-reasoning | 0.878 | See AI assessment above | | vercel_gpt-5 | 0.867 | See AI assessment above | | vercel_grok-4 | 0.85 | See AI assessment above | | vercel_deepseek-reasoner | 0.792 | See AI assessment above | | vercel_gpt-5-pro | 0.733 | See AI assessment above | | vercel_gemini-2.5-flash | 0.627 | See AI assessment above | | vercel_gemini-2.5-pro | 0.552 | See AI assessment above | | vercel_mistral-large-latest | 0 | See AI assessment above | ## Detailed Scenario Results ### 1. REMEDIATE AUTOMATIC ANALYZE EXECUTE **Winner**: vercel_claude-haiku-4-5-20251001 (Score: 0.94) **Models Compared**: 10 **Confidence**: 90% #### Rankings 1. **vercel_claude-haiku-4-5-20251001** - 0.94 2. **vercel_gemini-2.5-flash** - 0.92 3. **vercel_claude-sonnet-4-5-20250929** - 0.91 4. **vercel_grok-4-fast-reasoning** - 0.9 5. **vercel_gpt-5** - 0.85 6. **vercel_grok-4** - 0.84 7. **vercel_gpt-5-pro** - 0.73 8. **vercel_deepseek-reasoner** - 0.72 9. **vercel_gemini-2.5-pro** - 0.57 10. **vercel_mistral-large-latest** - 0 #### Analysis This Kubernetes troubleshooting scenario revealed significant performance stratification among models. Claude Haiku 4.5 emerged as the clear winner, delivering premium quality at budget pricing ($3.00/1M) with exceptional speed (32s) - proving that smaller, optimized models can outperform larger alternatives. Gemini 2.5 Flash provided excellent value at the lowest cost ($1.40/1M) with strong quality, while premium models like GPT-5 Pro ($67.50/1M) failed to justify their pricing with a critical 22-minute completion time approaching timeout constraints. The scenario exposed three critical failure modes: infrastructure validation failures (DeepSeek), output formatting failures (Gemini Pro), and complete rate limit failures (Mistral). For production Kubernetes troubleshooting, the results strongly favor cost-efficient models (Haiku, Flash, Grok-Fast-Reasoning) that balance speed, accuracy, and reliability over premium models that sacrifice performance for marginal quality improvements. The 20-30 minute timeout constraint proved critical - models exceeding ~2-3 minutes showed diminishing returns, while the fastest models (30-70s) provided optimal user experience without sacrificing diagnostic accuracy. All successful models correctly identified the OOMKilled root cause and proposed appropriate delete/recreate workflows, but efficiency varied dramatically from 32s to 1,344s - a 42x performance difference that fundamentally impacts production viability. --- ### 2. REMEDIATE MANUAL ANALYZE **Winner**: vercel_claude-haiku-4-5-20251001 (Score: 0.965) **Models Compared**: 10 **Confidence**: 90% #### Rankings 1. **vercel_claude-haiku-4-5-20251001** - 0.965 2. **vercel_gemini-2.5-flash** - 0.94 3. **vercel_claude-sonnet-4-5-20250929** - 0.925 4. **vercel_gpt-5** - 0.88 5. **vercel_grok-4** - 0.88 6. **vercel_deepseek-reasoner** - 0.865 7. **vercel_grok-4-fast-reasoning** - 0.855 8. **vercel_gpt-5-pro** - 0.73 9. **vercel_gemini-2.5-pro** - 0.534 10. **vercel_mistral-large-latest** - 0 #### Analysis Clear performance tiers emerged: (1) Efficient leaders (Haiku, Gemini Flash) excel at speed, cost, and quality balance; (2) Quality-focused models (Sonnet, GPT-5, Grok-4) provide superior diagnostics at higher cost/time; (3) Budget options (DeepSeek) sacrifice speed for low cost; (4) Unreliable models (GPT-5-Pro timeout risk, Gemini-Pro formatting failure, Mistral rate limiting) are unsuitable despite potential capabilities. Key finding: For straightforward OOMKilled scenarios, faster models (14-22s) achieve equivalent diagnostic accuracy to slower models (47-54s), making speed/cost optimization the primary differentiator. Premium pricing ($67.50/1M for GPT-5-Pro, $12/1M for Gemini-Pro) is not justified by quality gains. Sweet spot is $1.40-$3.00/1M tokens with sub-20s completion times. Prompt caching (Sonnet, GPT-5, DeepSeek) provides measurable efficiency gains. All successful models correctly identified the 128Mi vs 250M memory mismatch - suggesting this scenario has clear diagnostic signals that don't require premium model reasoning capabilities. Critical reliability differentiator: models must complete full workflow including structured JSON response generation - investigation quality alone is insufficient if output formatting fails. --- ### 3. REMEDIATE MANUAL EXECUTE **Winner**: vercel_claude-haiku-4-5-20251001 (Score: 0.94) **Models Compared**: 8 **Confidence**: 90% #### Rankings 1. **vercel_claude-haiku-4-5-20251001** - 0.94 2. **vercel_grok-4-fast-reasoning** - 0.88 3. **vercel_claude-sonnet-4-5-20250929** - 0.87 4. **vercel_gpt-5** - 0.87 5. **vercel_grok-4** - 0.83 6. **vercel_deepseek-reasoner** - 0.79 7. **vercel_gpt-5-pro** - 0.74 8. **vercel_gemini-2.5-flash** - 0.02 #### Analysis This scenario reveals that premium pricing does not correlate with better troubleshooting performance. Claude Haiku ($3/1M) delivered the best overall results, while GPT-5-Pro ($67.50/1M) ranked 7th due to extreme latency. The most cost-efficient model, Grok-4-Fast-Reasoning ($0.35/1M), achieved 2nd place with 94% of Haiku's quality at 1/9th the cost - demonstrating exceptional value. Models optimized for speed/efficiency (Haiku, Grok-Fast) significantly outperformed their premium siblings. The massive performance gap between models (2.5s to 626s completion times) shows that response latency should be a primary selection criterion for interactive troubleshooting workflows. Gemini-Flash's complete failure to execute any tool calls highlights that some 'flash' models may lack fundamental capabilities despite their speed, requiring validation before production deployment. For production Kubernetes troubleshooting: (1) Haiku offers best quality-cost-speed balance, (2) Grok-Fast provides unmatched cost efficiency for budget scenarios, (3) premium models showed no advantage and often performed worse, and (4) tool execution capability should be verified before model selection. --- ## AI Model Selection Guide ### Key Insights Three critical patterns emerged: (1) Premium pricing inversely correlates with performance - the most expensive models (GPT-5 Pro $67.50/1M, Gemini Pro $12/1M) ranked 7th and 9th, while top performers cost $0.35-$3/1M, (2) Speed optimization outperforms reasoning depth - models completing in 14-70s achieved equivalent diagnostic accuracy to those taking 10-22 minutes, suggesting Kubernetes OOMKilled scenarios have clear diagnostic signals not requiring extensive reasoning, (3) Tool execution capability is the critical differentiator - 'flash' models failed when execution was required despite strong analysis capabilities. Reliability analysis revealed catastrophic failure modes: complete rate limiting (Mistral 0% participation), tool execution failures (Gemini Flash 33% failure rate), and formatting breakdowns (Gemini Pro). The 42x performance variance (2.5s to 1,344s) shows response latency should be a primary selection criterion. For production Kubernetes remediation: prioritize models with 100% participation rates, sub-60s completion times, and proven tool execution reliability. The winner (Haiku) succeeded by never failing rather than occasionally excelling - demonstrating that consistency and reliability trump peak performance in production environments. ### Recommended Selection Strategy - **For Production Use**: Choose vercel_claude-haiku-4-5-20251001 - Best overall reliability (98.7%), optimal cost-performance ratio ($3/1M), fastest completion times (14-32s), zero failure modes. Recommended for all production Kubernetes remediation workflows. - **For Secondary Option**: Consider vercel_claude-sonnet-4-5-20250929 - Premium alternative with deeper diagnostics (96.9% reliability, $15/1M). Use when diagnostic depth justifies 5x cost increase over Haiku. vercel_grok-4-fast-reasoning - Budget alternative with strong reliability (96% reliability, $0.35/1M). Use for cost-sensitive deployments accepting 92% of Haiku's quality at 1/9th cost. - **Avoid**: vercel_mistral-large-latest - 100% failure rate, completely non-functional, vercel_gemini-2.5-pro - 33% failure rate with formatting issues, unreliable at premium cost, vercel_gpt-5-pro - Catastrophic latency (10-22 minutes), timeout risks, poor cost justification ($67.50/1M for 7th place performance), vercel_gemini-2.5-flash - Tool execution failures in 33% of scenarios despite speed advantages (reliability concerns) ### Decision Framework The AI assessment prioritizes **reliability and consistency** over peak performance. Models that fail completely in any scenario are heavily penalized, ensuring production-ready recommendations. --- ## Report Attribution Report generated by DevOps AI Toolkit Comparative Evaluation System