DevOps AI Toolkit

# Policy AI Model Comparison Report **Generated**: 2025-10-16T16:08:17.590Z **Scenarios Analyzed**: 4 **Models Evaluated**: 10 **Total Datasets**: 69 ## Executive Summary ### 🏆 Overall Winner (AI Assessment) **vercel_claude-sonnet-4-5-20250929** Claude Sonnet 4.5 is the unambiguous winner based on comprehensive reliability analysis: (1) **Perfect Reliability**: Only model with 100% participation AND consistently high performance (0.76-0.89) across all 4 scenarios, demonstrating production-grade stability. (2) **Technical Excellence**: Mastery of modern CEL validation, comprehensive schema analysis including edge cases (ephemeral containers), and efficient single-iteration workflow completion. (3) **Consistent Quality**: Lowest variance (consistency score 0.98) with rankings #1-3 in all scenarios - never falling below acceptable thresholds. (4) **Production Performance**: Response times (3-37s) and token efficiency (140K-165K) suitable for enterprise interactive workflows. (5) **Risk Profile**: Zero catastrophic failures, no context limitations, no timeout issues - deployable with confidence across all policy generation scenarios. While Gemini 2.5 Flash offers better cost-performance for high-throughput scenarios, Claude Sonnet's reliability-quality combination makes it the safest enterprise choice. Models with <100% participation (Gemini Pro 75%, DeepSeek/Mistral/GPT-5-Pro 50%) pose unacceptable production risks regardless of peak performance. Claude Sonnet represents the optimal balance: reliable enough for production deployment, performant enough for interactive use, and technically excellent enough for compliance-critical policy generation. ### 📊 AI Reliability Rankings 1. **vercel_claude-sonnet-4-5-20250929** (98%) - 100% participation, 0.832 avg score, 0.98 consistency - gold standard reliability 2. **vercel_gemini-2.5-flash** (88%) - 100% participation, 0.754 avg score, 0.88 consistency - excellent cost-performance reliability 3. **vercel_gpt-5** (87%) - 100% participation, 0.729 avg score, 0.87 consistency - solid but unremarkable 4. **vercel_grok-4** (85%) - 100% participation, 0.742 avg score, 0.85 consistency - performance variance concerns 5. **vercel_claude-haiku-4-5-20251001** (77%) - 100% participation, 0.73 avg score, 0.77 consistency - speed over quality tradeoff 6. **vercel_gemini-2.5-pro** (68%) - 75% participation (1 failure), 0.8 avg score, 0.91 consistency - quality undermined by reliability gap 7. **vercel_mistral-large-latest** (45%) - 50% participation (2 failures), 0.798 avg score, 0.91 consistency - context limit disqualifier 8. **vercel_deepseek-reasoner** (41%) - 50% participation (2 failures), 0.606 avg score, 0.83 consistency - architectural incompatibility 9. **vercel_grok-4-fast-reasoning** (34%) - 75% participation (1 failure), 0.395 avg score, 0.45 consistency - unstable quality 10. **vercel_gpt-5-pro** (16%) - 50% participation (2 failures), 0.238 avg score, 0.32 consistency - catastrophic failure profile ### 📋 Production Recommendations - **Primary Choice**: vercel_claude-sonnet-4-5-20250929 - Only model with proven reliability (100% participation, 98% reliability score) and consistent high-quality performance across all policy generation scenarios. Suitable for enterprise production deployment with compliance requirements. - **Secondary Option**: vercel_gemini-2.5-flash - Best alternative for high-throughput, cost-sensitive scenarios. Excellent reliability (100% participation, 88% reliability score) with superior performance (11.8-37s) and token efficiency. Acceptable quality tradeoff for non-compliance-critical workflows. - **Avoid for Production**: vercel_gpt-5-pro - Catastrophic 50% failure rate with timeout issues (15+ min). Completely unsuitable for production., vercel_deepseek-reasoner - 50% failure rate due to context limitations. Architecture incompatible with schema-heavy policy generation., vercel_mistral-large-latest - 50% failure rate in complex scenarios. Context window constraints eliminate from production consideration., vercel_grok-4-fast-reasoning - 25% failure rate with extreme quality variance and format compliance issues. Unstable for production. **Specialized Use Cases:** - **interactive_simple_triggers**: vercel_claude-haiku-4-5-20251001 - Optimal for sub-5s response requirements in simple trigger identification (0.805-0.893 scores, 1.3-2.2s latency). Avoid for complex policy generation. - **compliance_critical_when_available**: vercel_gemini-2.5-pro - Highest technical precision (0.835 best score, >=2 replica HA enforcement) when participating, but 25% failure rate restricts to non-critical validation workflows only. - **cost_optimization_high_volume**: vercel_gemini-2.5-flash - Best cost-performance ratio with acceptable quality for high-throughput policy generation where sub-second latency not required. ### 📊 Supplementary Statistics (Reference Only) | Model | Avg Score | Notes | |-------|-----------|-------| | vercel_claude-sonnet-4-5-20250929 | 0.831 | See AI assessment above | | vercel_gemini-2.5-pro | 0.798 | See AI assessment above | | vercel_gemini-2.5-flash | 0.747 | See AI assessment above | | vercel_grok-4 | 0.745 | See AI assessment above | | vercel_gpt-5 | 0.729 | See AI assessment above | | vercel_claude-haiku-4-5-20251001 | 0.728 | See AI assessment above | | vercel_grok-4-fast-reasoning | 0.492 | See AI assessment above | | vercel_mistral-large-latest | 0.399 | See AI assessment above | | vercel_deepseek-reasoner | 0.303 | See AI assessment above | | vercel_gpt-5-pro | 0.159 | See AI assessment above | ## Detailed Scenario Results ### 1. POLICY-COMPARATIVE POLICY NAMESPACE SCOPE STEP **Winner**: vercel_claude-sonnet-4-5-20250929 (Score: 0.89) **Models Compared**: 10 **Confidence**: 90% #### Rankings 1. **vercel_claude-sonnet-4-5-20250929** - 0.89 2. **vercel_gemini-2.5-flash** - 0.87 3. **vercel_grok-4** - 0.82 4. **vercel_gpt-5** - 0.8 5. **vercel_gemini-2.5-pro** - 0.79 6. **vercel_claude-haiku-4-5-20251001** - 0.65 7. **vercel_grok-4-fast-reasoning** - 0.34 8. **vercel_deepseek-reasoner** - 0 8. **vercel_mistral-large-latest** - 0 8. **vercel_gpt-5-pro** - 0 #### Analysis Three critical patterns emerged: (1) **Context length is a hard constraint** - DeepSeek Reasoner and Mistral Large failed completely due to 131K limits vs 138-140K required, making them unsuitable for schema-heavy policy scenarios. (2) **Modern CEL validation outperforms legacy pattern matching** - top performers (Claude Sonnet, Gemini Flash, Grok-4, GPT-5) all used CEL with has() checks and all() iterators, while Claude Haiku's pattern matching was less precise and performant. (3) **Efficiency varies dramatically** - token consumption ranged from 134K (Grok-4) to 687K (Grok-4-fast-reasoning), with Claude Haiku at 556K suggesting architectural inefficiency. (4) **Ephemeral container coverage separates good from great** - only Claude Sonnet, Gemini models, and GPT-5 validated ephemeral containers (critical for debugging scenarios). (5) **CRD awareness is emerging** - several models (Claude Sonnet, Gemini models, Grok-4) included CNPG Cluster, though technically incorrect for pod-level policies - shows promising CRD analysis but needs refinement. (6) **Reliability failures are catastrophic** - GPT-5-Pro's 15-minute timeout represents unacceptable production risk. (7) **Cost-performance tradeoffs** - Gemini 2.5 Flash offers best value with 87% score at 37s and 165K tokens, while Gemini 2.5 Pro's 79% score at 110s/332K tokens shows diminishing returns. (8) **Workflow consistency matters** - single-iteration completion (Claude Sonnet, Gemini Flash) demonstrates superior prompt understanding versus multi-iteration refinement. For production Kubernetes policy management, Claude Sonnet 4.5 and Gemini 2.5 Flash emerge as clear leaders with strong quality, modern validation approaches, reasonable performance, and crucially, reliable completion. Models with context limitations (DeepSeek, Mistral) or reliability issues (GPT-5-Pro) are unsuitable for enterprise policy scenarios requiring comprehensive schema analysis. --- ### 2. POLICY-COMPARATIVE POLICY STORE ONLY NAMESPACE SCOPE **Winner**: vercel_gemini-2.5-pro (Score: 0.835) **Models Compared**: 9 **Confidence**: 90% #### Rankings 1. **vercel_gemini-2.5-pro** - 0.835 2. **vercel_claude-sonnet-4-5-20250929** - 0.825 3. **vercel_gemini-2.5-flash** - 0.815 4. **vercel_grok-4** - 0.76 5. **vercel_gpt-5** - 0.735 6. **vercel_claude-haiku-4-5-20251001** - 0.565 7. **vercel_grok-4-fast-reasoning** - 0.05 8. **vercel_deepseek-reasoner** - 0 9. **vercel_mistral-large-latest** - 0 #### Analysis This evaluation reveals critical architectural requirements for Kubernetes policy generation: (1) **Context Window Size**: Models with <140K token limits (deepseek-reasoner, mistral-large-latest) cannot handle comprehensive schema-aware policy generation, resulting in complete failure. (2) **Technical Correctness**: Proper CEL-based validation is essential - pattern-based validation (claude-haiku) cannot enforce numeric comparisons. The best models (gemini-2.5-pro, claude-sonnet, grok-4) use CEL with has() checks and >= operators. (3) **HA Enforcement Depth**: Only gemini-2.5-pro and grok-4 properly enforce >= 2 replicas; others either check existence only or use > 0, failing to meet true HA requirements. (4) **Output Format Reliability**: Format compliance is critical - grok-4-fast-reasoning generated correct logic but failed due to markdown wrapping, highlighting the importance of production-ready output formats. (5) **Performance vs. Quality Trade-offs**: Gemini-2.5-flash offers the best performance (11.8s) with acceptable quality, while gemini-2.5-pro provides superior quality at 48.9s - organizations must balance latency requirements against policy correctness. (6) **Resource Coverage**: The best models identified 4-6 relevant resource types (core controllers + CNPG custom resources); incomplete schema analysis led to missing critical resources (gpt-5 excluded CNPG Cluster). (7) **Cost-Performance**: For production enterprise use requiring high correctness, gemini-2.5-pro offers the best value despite higher latency. For high-throughput scenarios with basic validation needs, gemini-2.5-flash provides optimal efficiency. Models with context limitations or format issues (3 of 9 models) are completely unsuitable regardless of cost. --- ### 3. POLICY-COMPARATIVE POLICY STORE ONLY TRIGGERS **Winner**: vercel_claude-haiku-4-5-20251001 (Score: 0.805) **Models Compared**: 10 **Confidence**: 90% #### Rankings 1. **vercel_claude-haiku-4-5-20251001** - 0.805 2. **vercel_grok-4-fast-reasoning** - 0.795 3. **vercel_gemini-2.5-pro** - 0.77 4. **vercel_claude-sonnet-4-5-20250929** - 0.76 5. **vercel_mistral-large-latest** - 0.72 6. **vercel_deepseek-reasoner** - 0.655 7. **vercel_grok-4** - 0.645 8. **vercel_gemini-2.5-flash** - 0.645 9. **vercel_gpt-5** - 0.615 10. **vercel_gpt-5-pro** - 0 #### Analysis This evaluation reveals stark differences in model suitability for organizational policy intent management: **Performance Clusters**: Three distinct tiers emerged: (1) Fast responders (Haiku, Mistral <2s) offering production viability, (2) Moderate performers (Sonnet, Grok-4-fast, Gemini-Pro 3-30s) acceptable for interactive use, and (3) Slow/failed models (Deepseek 167s, Grok-4 94s, GPT-5-Pro timeout) unsuitable for real-time policy workflows. **Quality vs. Speed Trade-off**: Deepseek and Gemini-Pro achieved highest technical precision but at severe performance cost. Claude Haiku demonstrated optimal balance - 80%+ quality at 1.3s response. This suggests that for policy intent management, 'good enough fast' outperforms 'perfect slow' in production environments. **Scope Interpretation Variance**: Models diverged significantly on synonym breadth: (1) K8s-purists (Grok-4, Gemini-Pro) provided precise API objects, (2) Cross-platform models (GPT-5, Haiku) included cloud equivalents, (3) Over-inclusive models (Gemini-Flash) added architectural concepts. For organizational policy trigger matching, cross-platform breadth (Haiku approach) is most valuable. **Reliability as Disqualifier**: GPT-5-Pro's complete failure and Grok-4's 94s latency demonstrate that unreliable models are non-viable regardless of quality. Policy workflows require consistent sub-30s responses. Timeout tolerance must be strict for production policy management. **Token Efficiency Gap**: Massive variance in output tokens (14-4,423) for equivalent tasks signals different reasoning approaches. Deepseek's 4,423 tokens and GPT-5's 2,496 tokens for simple lists indicate inefficient internal processing. This impacts both cost and latency. **Best Practice**: For production organizational policy intent management, prioritize Claude Haiku (speed + breadth) for initial policy creation, Gemini-Pro (precision) for compliance-critical validation, and avoid unreliable/slow models entirely. Cost-effective policy workflows require <5s response times with 70%+ quality threshold. --- ### 4. POLICY-COMPARATIVE POLICY TRIGGERS STEP **Winner**: vercel_claude-haiku-4-5-20251001 (Score: 0.893) **Models Compared**: 10 **Confidence**: 90% #### Rankings 1. **vercel_claude-haiku-4-5-20251001** - 0.893 2. **vercel_mistral-large-latest** - 0.875 3. **vercel_claude-sonnet-4-5-20250929** - 0.85 4. **vercel_gemini-2.5-pro** - 0.795 5. **vercel_grok-4-fast-reasoning** - 0.783 6. **vercel_gpt-5** - 0.765 7. **vercel_grok-4** - 0.755 8. **vercel_gemini-2.5-flash** - 0.657 9. **vercel_deepseek-reasoner** - 0.556 10. **vercel_gpt-5-pro** - 0.476 #### Analysis This evaluation reveals significant performance stratification in policy trigger identification capabilities. Top performers (Claude Haiku, Mistral Large, Claude Sonnet) demonstrate the critical combination of speed (<3s response), comprehensive technical coverage (workloads + enforcement mechanisms + QoS classes), and token efficiency needed for production policy workflows. The clear winners prioritize both breadth (workload types) and depth (enforcement mechanisms like admission controllers, limit ranges, QoS classes). Mid-tier models (Gemini Pro, Grok-4-fast, GPT-5, Grok-4) show acceptable technical knowledge but suffer from either insufficient depth or performance issues. Bottom-tier models (Gemini Flash, Deepseek Reasoner, GPT-5-Pro) are fundamentally unsuitable for production policy management due to catastrophic performance failures - response times ranging from 30 seconds to 15+ minutes are unacceptable for interactive policy workflows. A critical insight: comprehensive technical coverage of enforcement mechanisms (admission controllers, QoS classes, limit ranges) separates expert-level policy models from basic resource enumeration. For organizational policy management, teams should prioritize models with sub-5-second response times, explicit enforcement mechanism coverage, and token efficiency under 150 tokens for this task type. The timeout failures highlight that reasoning-heavy models may be inappropriate for interactive policy workflows where responsiveness is paramount. --- ## AI Model Selection Guide ### Key Insights This evaluation exposes five critical insights for production AI policy systems: (1) **Context Window as Hard Constraint**: 30% of models failed due to <140K token limits, making context capacity a non-negotiable requirement for schema-aware policy generation. (2) **Reliability Trumps Peak Performance**: Models with 50-75% participation rates are production-disqualified regardless of quality when participating - Gemini Pro's 0.835 peak score cannot overcome 25% failure rate. (3) **Performance Variance Indicates Instability**: Models with high score variance (Grok-4-fast: 0.05-0.795) demonstrate unstable reasoning unsuitable for predictable production workflows. (4) **Architectural Tradeoffs Are Scenario-Dependent**: Speed specialists (Claude Haiku) excel in simple scenarios but fail in complex ones; reasoning models (DeepSeek) provide quality when applicable but have catastrophic coverage gaps. (5) **Modern Validation Literacy Separates Tiers**: CEL-based validation with has() checks and all() iterators is table stakes - pattern-matching approaches (Claude Haiku: 0.565-0.65 in complex scenarios) cannot enforce numeric constraints properly. For enterprise Kubernetes policy management, only 2 models meet production reliability standards (Claude Sonnet, Gemini Flash), while 4 models pose catastrophic operational risks (GPT-5-Pro, DeepSeek, Mistral, Grok-4-fast). Organizations must prioritize 100% scenario participation and <0.1 score variance for production deployment confidence. ### Recommended Selection Strategy - **For Production Use**: Choose vercel_claude-sonnet-4-5-20250929 - Only model with proven reliability (100% participation, 98% reliability score) and consistent high-quality performance across all policy generation scenarios. Suitable for enterprise production deployment with compliance requirements. - **For Secondary Option**: Consider vercel_gemini-2.5-flash - Best alternative for high-throughput, cost-sensitive scenarios. Excellent reliability (100% participation, 88% reliability score) with superior performance (11.8-37s) and token efficiency. Acceptable quality tradeoff for non-compliance-critical workflows. - **Avoid**: vercel_gpt-5-pro - Catastrophic 50% failure rate with timeout issues (15+ min). Completely unsuitable for production., vercel_deepseek-reasoner - 50% failure rate due to context limitations. Architecture incompatible with schema-heavy policy generation., vercel_mistral-large-latest - 50% failure rate in complex scenarios. Context window constraints eliminate from production consideration., vercel_grok-4-fast-reasoning - 25% failure rate with extreme quality variance and format compliance issues. Unstable for production. (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