DevOps AI Toolkit

{ "metadata": { "reportType": "comparative-evaluation", "evaluationType": "pattern", "generated": "2025-10-16T16:01:57.111Z", "scenariosAnalyzed": 1, "modelsEvaluated": 10, "totalDatasets": 11, "tool": "Pattern AI Model Comparison Report" }, "modelMetadata": { "claude-sonnet-4-5-20250929": { "provider": "Anthropic", "pricing": { "input_cost_per_million_tokens": 3, "output_cost_per_million_tokens": 15 }, "context_window": 1000000, "supports_function_calling": true }, "claude-haiku-4-5-20251001": { "provider": "Anthropic", "pricing": { "input_cost_per_million_tokens": 1, "output_cost_per_million_tokens": 5 }, "context_window": 200000, "supports_function_calling": true }, "gpt-5": { "provider": "OpenAI", "pricing": { "input_cost_per_million_tokens": 1.25, "output_cost_per_million_tokens": 10 }, "context_window": 272000, "supports_function_calling": true }, "gpt-5-pro": { "provider": "OpenAI", "pricing": { "input_cost_per_million_tokens": 15, "output_cost_per_million_tokens": 120 }, "context_window": 272000, "supports_function_calling": true }, "gemini-2.5-pro": { "provider": "Google", "pricing": { "input_cost_per_million_tokens": 4, "output_cost_per_million_tokens": 20 }, "context_window": 1048576, "supports_function_calling": true }, "gemini-2.5-flash": { "provider": "Google", "pricing": { "input_cost_per_million_tokens": 0.3, "output_cost_per_million_tokens": 2.5 }, "context_window": 1048576, "supports_function_calling": true }, "grok-4": { "provider": "xAI", "pricing": { "input_cost_per_million_tokens": 3, "output_cost_per_million_tokens": 15 }, "context_window": 256000, "supports_function_calling": true }, "grok-4-fast-reasoning": { "provider": "xAI", "pricing": { "input_cost_per_million_tokens": 0.2, "output_cost_per_million_tokens": 0.5 }, "context_window": 2000000, "supports_function_calling": true }, "mistral-large-latest": { "provider": "Mistral", "pricing": { "input_cost_per_million_tokens": 2, "output_cost_per_million_tokens": 6 }, "context_window": 128000, "supports_function_calling": true }, "deepseek-reasoner": { "provider": "DeepSeek", "pricing": { "input_cost_per_million_tokens": 0.55, "output_cost_per_million_tokens": 2.19 }, "context_window": 128000, "supports_function_calling": false } }, "overallAssessment": { "assessment_summary": "Comprehensive cross-scenario analysis of 10 models across 1 Kubernetes pattern trigger identification scenario reveals stark reliability differences. 9 models successfully participated with varying performance levels (51-85%), while 1 model (GPT-5-Pro) experienced complete catastrophic failure (timeout). The evaluation demonstrates clear performance tiers with 42x speed variance and significant quality differences in Kubernetes-specific knowledge.", "models_analyzed": [ "vercel_gpt-5", "vercel_claude-haiku-4-5-20251001", "vercel_claude-sonnet-4-5-20250929", "vercel_mistral-large-latest", "vercel_gemini-2.5-pro", "vercel_grok-4-fast-reasoning", "vercel_gemini-2.5-flash", "vercel_grok-4", "vercel_deepseek-reasoner", "vercel_gpt-5-pro" ], "detailed_analysis": { "vercel_claude-sonnet-4-5-20250929": { "participation_rate": 1, "scenarios_participated": [ "pattern-comparative_pattern_triggers_step" ], "scenarios_failed": [], "average_score": 0.85, "consistency_score": 1, "reliability_score": 1, "strengths": "Highest quality score (0.85) with comprehensive Kubernetes resource coverage including StatefulSets and PVCs. Deep K8s domain knowledge evident in pattern trigger identification. Participated successfully in all scenarios with excellent technical accuracy.", "weaknesses": "No observable weaknesses - complete participation with top-tier performance. Represents ideal balance of quality and reliability.", "production_readiness": "primary" }, "vercel_claude-haiku-4-5-20251001": { "participation_rate": 1, "scenarios_participated": [ "pattern-comparative_pattern_triggers_step" ], "scenarios_failed": [], "average_score": 0.85, "consistency_score": 1, "reliability_score": 1, "strengths": "Tied for highest quality score (0.85) with exceptional speed (1.4s response time - 42x faster than slowest model). Best cost-performance balance for high-throughput workflows. Complete K8s resource coverage with sub-2-second responses.", "weaknesses": "No observable weaknesses - perfect participation with top-tier performance and industry-leading speed.", "production_readiness": "primary" }, "vercel_gpt-5": { "participation_rate": 1, "scenarios_participated": [ "pattern-comparative_pattern_triggers_step" ], "scenarios_failed": [], "average_score": 0.8, "consistency_score": 1, "reliability_score": 1, "strengths": "High quality score (0.8) with maximum thoroughness in trigger identification. Comprehensive pattern coverage demonstrating strong reliability. Successfully participated in all scenarios.", "weaknesses": "Slightly lower quality score (0.8 vs 0.85) compared to Claude models, suggesting marginally less comprehensive K8s-specific resource coverage.", "production_readiness": "primary" }, "vercel_mistral-large-latest": { "participation_rate": 1, "scenarios_participated": [ "pattern-comparative_pattern_triggers_step" ], "scenarios_failed": [], "average_score": 0.83, "consistency_score": 1, "reliability_score": 1, "strengths": "Strong quality score (0.83) placing it in top tier. Efficient trigger generation with comprehensive coverage. 100% participation rate with solid reliability.", "weaknesses": "Marginally below Claude models in quality (0.83 vs 0.85), indicating slightly less comprehensive K8s domain expertise.", "production_readiness": "primary" }, "vercel_gemini-2.5-pro": { "participation_rate": 1, "scenarios_participated": [ "pattern-comparative_pattern_triggers_step" ], "scenarios_failed": [], "average_score": 0.79, "consistency_score": 1, "reliability_score": 1, "strengths": "Good quality score (0.79) with complete participation. Acceptable for most production scenarios with reliable execution.", "weaknesses": "Mid-tier performance indicates potential for missing edge cases in complex K8s environments. Speed-comprehensiveness tradeoffs evident.", "production_readiness": "secondary" }, "vercel_grok-4-fast-reasoning": { "participation_rate": 1, "scenarios_participated": [ "pattern-comparative_pattern_triggers_step" ], "scenarios_failed": [], "average_score": 0.76, "consistency_score": 1, "reliability_score": 1, "strengths": "Participated in all scenarios with acceptable quality (0.76). Reasonable speed-quality balance for less complex environments.", "weaknesses": "Mid-tier quality suggests limitations in comprehensive pattern coverage. May miss edge cases in sophisticated K8s deployments.", "production_readiness": "secondary" }, "vercel_gemini-2.5-flash": { "participation_rate": 1, "scenarios_participated": [ "pattern-comparative_pattern_triggers_step" ], "scenarios_failed": [], "average_score": 0.71, "consistency_score": 1, "reliability_score": 1, "strengths": "Full participation with functional performance. Suitable for simpler pattern identification tasks with basic requirements.", "weaknesses": "Lower quality score (0.71) indicates significant gaps in comprehensive trigger identification. Limited K8s-specific resource awareness.", "production_readiness": "limited" }, "vercel_grok-4": { "participation_rate": 1, "scenarios_participated": [ "pattern-comparative_pattern_triggers_step" ], "scenarios_failed": [], "average_score": 0.67, "consistency_score": 1, "reliability_score": 1, "strengths": "Completed all scenarios without failures. Demonstrates basic functional capability.", "weaknesses": "Severe performance issues with extremely slow response time (78.6s - 42x slower than fastest model). Lower quality (0.67) doesn't justify the performance cost. Operational responsiveness concerns.", "production_readiness": "limited" }, "vercel_deepseek-reasoner": { "participation_rate": 1, "scenarios_participated": [ "pattern-comparative_pattern_triggers_step" ], "scenarios_failed": [], "average_score": 0.51, "consistency_score": 1, "reliability_score": 1, "strengths": "Participated in all scenarios without catastrophic failures. Maintains basic operational reliability.", "weaknesses": "Severe quality issues (0.51 score - lowest among participating models). Performance problems that outweigh any potential benefits. Inadequate for production K8s pattern management.", "production_readiness": "avoid" }, "vercel_gpt-5-pro": { "participation_rate": 0, "scenarios_participated": [], "scenarios_failed": [ "pattern-comparative_pattern_triggers_step" ], "average_score": 0, "consistency_score": 0, "reliability_score": 0, "strengths": "None observable - model failed to participate in any scenarios.", "weaknesses": "Complete catastrophic failure with 0% participation rate. Total timeout failure demonstrates critical reliability issues. Even potentially high-quality capabilities are worthless without ability to deliver results within practical time constraints. Represents maximum production risk.", "production_readiness": "avoid" } }, "overall_assessment": { "winner": "vercel_claude-sonnet-4-5-20250929", "rationale": "Claude Sonnet emerges as the overall winner based on optimal balance of reliability, quality, and production readiness. With 100% participation rate, tied-highest quality score (0.85), and comprehensive Kubernetes-specific knowledge (including StatefulSets, PVCs), it represents the most dependable choice for production pattern management. While Claude Haiku matches the quality score with superior speed (1.4s), Sonnet's evaluation explicitly notes 'best K8s resource coverage' making it optimal for quality-critical scenarios. The key differentiator is domain expertise depth - Sonnet demonstrates superior K8s-specific understanding which correlates with better pattern management capabilities. Perfect reliability score (1.0) with zero failures across all scenarios eliminates catastrophic risk. For production environments where comprehensive accuracy is paramount and operational responsiveness requirements are reasonable, Claude Sonnet provides maximum confidence with proven consistency.", "reliability_ranking": [ { "model": "vercel_claude-sonnet-4-5-20250929", "reliability_score": 1, "reliability_notes": "100% participation | 100% success rate | Perfect consistency | Top quality (0.85) | Best K8s domain expertise" }, { "model": "vercel_claude-haiku-4-5-20251001", "reliability_score": 1, "reliability_notes": "100% participation | 100% success rate | Perfect consistency | Top quality (0.85) | Industry-leading speed (1.4s)" }, { "model": "vercel_mistral-large-latest", "reliability_score": 1, "reliability_notes": "100% participation | 100% success rate | Perfect consistency | Strong quality (0.83)" }, { "model": "vercel_gpt-5", "reliability_score": 1, "reliability_notes": "100% participation | 100% success rate | Perfect consistency | High quality (0.8) | Maximum thoroughness" }, { "model": "vercel_gemini-2.5-pro", "reliability_score": 1, "reliability_notes": "100% participation | 100% success rate | Perfect consistency | Good quality (0.79) | Mid-tier performance" }, { "model": "vercel_grok-4-fast-reasoning", "reliability_score": 1, "reliability_notes": "100% participation | 100% success rate | Perfect consistency | Acceptable quality (0.76)" }, { "model": "vercel_gemini-2.5-flash", "reliability_score": 1, "reliability_notes": "100% participation | 100% success rate | Perfect consistency | Lower quality (0.71) | Gaps in coverage" }, { "model": "vercel_grok-4", "reliability_score": 1, "reliability_notes": "100% participation | 100% success rate | Perfect consistency | Low quality (0.67) | Severe speed issues (78.6s)" }, { "model": "vercel_deepseek-reasoner", "reliability_score": 1, "reliability_notes": "100% participation | 100% success rate | Perfect consistency | Critical quality issues (0.51)" }, { "model": "vercel_gpt-5-pro", "reliability_score": 0, "reliability_notes": "0% participation | Complete catastrophic failure | Timeout issues | Maximum production risk" } ], "production_recommendations": { "primary": "vercel_claude-sonnet-4-5-20250929", "secondary": "vercel_claude-haiku-4-5-20251001", "avoid": [ "vercel_gpt-5-pro", "vercel_deepseek-reasoner" ], "specialized_use": { "high_throughput_workflows": "vercel_claude-haiku-4-5-20251001 - Sub-2-second responses with top-tier quality for volume scenarios", "maximum_thoroughness": "vercel_gpt-5 - Comprehensive trigger identification when deep analysis is required", "cost_sensitive_deployments": "vercel_mistral-large-latest - Strong quality (0.83) with efficient resource usage", "simple_environments": "vercel_gemini-2.5-pro - Adequate for basic K8s setups without complex edge cases" } }, "key_insights": "This evaluation reveals that reliability and quality are not mutually exclusive in pattern management tools. All top-tier models (Claude Haiku/Sonnet, GPT-5, Mistral Large) achieved perfect reliability (100% participation, zero failures) while maintaining high quality scores (0.8-0.85). The catastrophic failure of GPT-5-Pro underscores a critical production principle: theoretical capability means nothing without operational delivery. The 42x speed variance between models highlights that performance optimization must balance technical accuracy with responsiveness - Grok-4's 78.6s response time makes it operationally unsuitable despite participation. Kubernetes-specific domain knowledge emerged as a key quality differentiator - models demonstrating deep K8s resource awareness (StatefulSets, PVCs) consistently scored higher. The clear performance tiers enable precision model selection based on workload characteristics: quality-critical scenarios demand Claude Sonnet, high-volume workflows benefit from Haiku's speed, while Deepseek's severe quality issues (0.51) make it unsuitable regardless of reliability metrics. Production deployments should prioritize the top four models (Claude Sonnet/Haiku, Mistral Large, GPT-5) which combine proven reliability with strong domain expertise." } }, "results": [ { "key": "pattern-comparative_pattern_triggers_step", "score": 0.8, "comment": "This evaluation reveals clear performance tiers for Kubernetes pattern trigger identification. **Top Tier** (Claude Haiku/Sonnet, GPT-5, Mistral Large) demonstrates that comprehensive, accurate trigger lists can be generated efficiently, with Claude models offering best cost-performance balance and GPT-5 providing maximum thoroughness. **Mid Tier** (Gemini Pro, Grok-4-Fast) shows trade-offs between speed and comprehensiveness - acceptable for simpler environments but may miss edge cases. **Lower Tier** (Deepseek, Grok-4) exhibits severe performance issues that outweigh quality benefits. **Critical Finding**: GPT-5-Pro's complete timeout failure highlights reliability as paramount - even potentially high-quality models are worthless if they can't deliver results within practical time constraints. For production Kubernetes pattern management, Claude Sonnet emerges as optimal for quality-critical scenarios (best K8s resource coverage including StatefulSets), while Claude Haiku excels for high-throughput workflows requiring sub-2-second responses. The 42x speed difference between fastest (Haiku: 1.4s) and slowest successful model (Grok-4: 78.6s) underscores that model selection must balance technical accuracy with operational responsiveness. Notably, models that included Kubernetes-specific resources (StatefulSets, PVCs) scored higher on quality, indicating deep K8s knowledge correlates with better pattern management capabilities.", "confidence": 0.9, "modelRankings": [ { "rank": 1, "model": "vercel_gpt-5", "score": 0.8 }, { "rank": 2, "model": "vercel_claude-haiku-4-5-20251001", "score": 0.85 }, { "rank": 3, "model": "vercel_claude-sonnet-4-5-20250929", "score": 0.85 }, { "rank": 4, "model": "vercel_mistral-large-latest", "score": 0.83 }, { "rank": 5, "model": "vercel_gemini-2.5-pro", "score": 0.79 }, { "rank": 6, "model": "vercel_grok-4-fast-reasoning", "score": 0.76 }, { "rank": 7, "model": "vercel_gemini-2.5-flash", "score": 0.71 }, { "rank": 8, "model": "vercel_grok-4", "score": 0.67 }, { "rank": 9, "model": "vercel_deepseek-reasoner", "score": 0.51 }, { "rank": 10, "model": "vercel_gpt-5-pro", "score": 0 } ], "bestModel": "vercel_gpt-5", "modelCount": 10 } ], "summary": { "totalDatasets": 11, "availableModels": [ "vercel_claude-haiku-4-5-20251001_2025-10-15", "vercel_claude-sonnet-4-5-20250929_2025-10-13", "vercel_deepseek-reasoner_2025-10-13", "vercel_gemini-2.5-flash_2025-10-14", "vercel_gemini-2.5-pro_2025-10-14", "vercel_gpt-5-pro_2025-10-14", "vercel_gpt-5_2025-10-14", "vercel_grok-4-fast-reasoning_2025-10-14", "vercel_grok-4_2025-10-14", "vercel_mistral-large-latest_2025-10-14" ], "scenariosWithMultipleModels": 1, "interactionTypes": [ "triggers_step" ] } }