performance-scaling

Claude Sonnet 4.5 Performance Profile

```json

{

"model": "claude-sonnet-4.5",

"base_performance": {

"execution_speed": "fast",

"reasoning_depth": "high",

"context_switching": "excellent",

"adaptability": "very_high"

},

"scaling_factors": {

"time_multiplier": 1.0,

"quality_target": 90,

"complexity_handling": 0.9,

"parallel_processing": 1.2

},

"optimization_strategies": [

"context_merging",

"predictive_delegation",

"pattern_weighting",

"adaptive_quality_thresholds"

]

}

```

Claude Haiku 4.5 Performance Profile

```json

{

"model": "claude-haiku-4.5",

"base_performance": {

"execution_speed": "very_fast",

"reasoning_depth": "medium",

"context_switching": "good",

"adaptability": "high"

},

"scaling_factors": {

"time_multiplier": 0.8,

"quality_target": 88,

"complexity_handling": 1.1,

"parallel_processing": 1.0

},

"optimization_strategies": [

"fast_execution",

"selective_processing",

"efficient_delegation",

"streamlined_quality_checks"

]

}

```

Claude Opus 4.1 Performance Profile

```json

{

"model": "claude-opus-4.1",

"base_performance": {

"execution_speed": "very_fast",

"reasoning_depth": "very_high",

"context_switching": "excellent",

"adaptability": "maximum"

},

"scaling_factors": {

"time_multiplier": 0.9,

"quality_target": 95,

"complexity_handling": 0.8,

"parallel_processing": 1.4

},

"optimization_strategies": [

"anticipatory_execution",

"enhanced_parallelization",

"predictive_caching",

"advanced_pattern_recognition"

]

}

```

GLM-4.6 Performance Profile

```json

{

"model": "glm-4.6",

"base_performance": {

"execution_speed": "moderate",

"reasoning_depth": "medium",

"context_switching": "good",

"adaptability": "medium"

},

"scaling_factors": {

"time_multiplier": 1.25,

"quality_target": 88,

"complexity_handling": 1.2,

"parallel_processing": 0.8

},

"optimization_strategies": [

"structured_sequencing",

"explicit_instruction_optimization",

"step_by_step_validation",

"clear_handoff_protocols"

]

}

```

Time-Based Scaling

Execution Time Allocation:

```javascript

function scaleExecutionTime(baseTime, model, complexity) {

const profiles = {

'claude-sonnet': { multiplier: 1.0, complexity_factor: 0.9 },

'claude-4.5': { multiplier: 0.9, complexity_factor: 0.8 },

'glm-4.6': { multiplier: 1.25, complexity_factor: 1.2 },

'fallback': { multiplier: 1.5, complexity_factor: 1.4 }

};

const profile = profiles[model] || profiles.fallback;

return baseTime profile.multiplier (1 + complexity * profile.complexity_factor);

}

```

Timeout Adjustments:

• Claude Sonnet: Standard timeouts with 10% buffer
• Claude 4.5: Reduced timeouts with 5% buffer
• GLM-4.6: Extended timeouts with 25% buffer
• Fallback: Conservative timeouts with 50% buffer

Quality Target Scaling

Model-Specific Quality Targets:

```javascript

function getQualityTarget(model, taskType) {

const baseTargets = {

'claude-sonnet': { simple: 85, complex: 90, critical: 95 },

'claude-4.5': { simple: 88, complex: 92, critical: 96 },

'glm-4.6': { simple: 82, complex: 88, critical: 92 },

'fallback': { simple: 80, complex: 85, critical: 90 }

};

return baseTargets[model]?.[taskType] || baseTargets.fallback.complex;

}

```

Quality Assessment Adaptation:

• Claude Models: Emphasize contextual understanding and pattern recognition
• GLM Models: Emphasize structured accuracy and procedural correctness

Resource Scaling

Memory Management:

```javascript

function scaleMemoryUsage(model, taskSize) {

const profiles = {

'claude-sonnet': { base_memory: 'medium', scaling_factor: 1.1 },

'claude-4.5': { base_memory: 'medium', scaling_factor: 1.0 },

'glm-4.6': { base_memory: 'high', scaling_factor: 1.3 },

'fallback': { base_memory: 'high', scaling_factor: 1.5 }

};

const profile = profiles[model] || profiles.fallback;

return allocateMemory(profile.base_memory, taskSize * profile.scaling_factor);

}

```

Concurrent Task Limits:

• Claude Sonnet: 3-4 concurrent tasks
• Claude 4.5: 4-5 concurrent tasks
• GLM-4.6: 2-3 concurrent tasks
• Fallback: 1-2 concurrent tasks

Dynamic Performance Adjustment

Real-Time Performance Monitoring:

```javascript

function monitorPerformance(model, currentMetrics) {

const baseline = getPerformanceBaseline(model);

const variance = calculateVariance(currentMetrics, baseline);

if (variance > 0.2) {

// Performance deviating significantly from baseline

return adjustPerformanceParameters(model, currentMetrics);

}

return currentMetrics;

}

```

Automatic Parameter Tuning:

```javascript

function tuneParameters(model, taskHistory) {

const performance = analyzeTaskPerformance(taskHistory);

const adjustments = calculateOptimalAdjustments(model, performance);

return {

timeout_adjustments: adjustments.timeouts,

quality_thresholds: adjustments.quality,

resource_allocation: adjustments.resources,

delegation_strategy: adjustments.delegation

};

}

```

Learning-Based Optimization

Pattern Recognition for Performance:

```javascript

function learnPerformancePatterns(executionHistory) {

const patterns = {

successful_executions: extractSuccessPatterns(executionHistory),

failed_executions: extractFailurePatterns(executionHistory),

optimization_opportunities: identifyOptimizations(executionHistory)

};

return generatePerformanceRecommendations(patterns);

}

```

Model-Specific Learning:

• Claude Models: Learn from nuanced patterns and contextual factors
• GLM Models: Learn from structured procedures and clear success/failure patterns

Core Performance Indicators

Execution Metrics:

• Task Completion Time: Time from task start to completion
• Quality Achievement: Final quality score vs target
• Resource Efficiency: Memory and CPU usage efficiency
• Error Rate: Frequency of errors requiring recovery

Model-Specific KPIs:

```javascript

const modelKPIs = {

'claude-sonnet': {

'context_switching_efficiency': '>= 90%',

'pattern_recognition_accuracy': '>= 85%',

'adaptive_decision_quality': '>= 88%'

},

'claude-4.5': {

'predictive_accuracy': '>= 80%',

'anticipatory_optimization': '>= 75%',

'enhanced_reasoning_utilization': '>= 90%'

},

'glm-4.6': {

'procedural_accuracy': '>= 95%',

'structured_execution_compliance': '>= 98%',

'explicit_instruction_success': '>= 92%'

}

};

```

Performance Benchmarking

Comparative Analysis:

```javascript

function benchmarkPerformance(model, testSuite) {

const results = runPerformanceTests(model, testSuite);

const baseline = getIndustryBaseline(model);

return {

relative_performance: results.score / baseline.score,

improvement_opportunities: identifyImprovements(results, baseline),

model_strengths: analyzeModelStrengths(results),

optimization_recommendations: generateRecommendations(results)

};

}

```

Model-Specific Optimizations

Claude Sonnet Optimizations:

1. Context Merging: Combine related contexts to reduce switching overhead
2. Weight-Based Decision Making: Use historical success patterns for decisions
3. Progressive Loading: Load skills progressively based on immediate needs
4. Adaptive Quality Thresholds: Adjust quality targets based on task complexity

Claude 4.5 Optimizations:

1. Anticipatory Execution: Start likely tasks before explicit request
2. Enhanced Parallelization: Maximize concurrent task execution
3. Predictive Caching: Cache likely-needed resources proactively
4. Advanced Pattern Matching: Use complex pattern recognition for optimization

GLM-4.6 Optimizations:

1. Structured Sequencing: Optimize task order for efficiency
2. Explicit Instruction Optimization: Minimize ambiguity in instructions
3. Step-by-Step Validation: Validate each step before proceeding
4. Clear Handoff Protocols: Ensure clean transitions between tasks

Universal Optimizations

Cross-Model Techniques:

1. Resource Pooling: Share resources across compatible tasks
2. Intelligent Caching: Cache results based on usage patterns
3. Batch Processing: Group similar operations for efficiency
4. Lazy Loading: Load resources only when needed

Performance Configuration Loading

```javascript

function loadPerformanceConfiguration(model) {

const baseConfig = getBasePerformanceProfile(model);

const historicalData = getHistoricalPerformanceData(model);

const currentContext = assessCurrentContext();

return mergeAndOptimizeConfiguration(baseConfig, historicalData, currentContext);

}

```

Runtime Performance Adjustment

```javascript

function adjustRuntimePerformance(currentMetrics, targetProfile) {

const adjustments = calculateNeededAdjustments(currentMetrics, targetProfile);

return {

timeout_adjustments: adjustments.timeouts,

quality_modifications: adjustments.quality,

resource_reallocation: adjustments.resources,

strategy_changes: adjustments.strategy

};

}

```

Performance Monitoring and Alerting

```javascript

function monitorPerformanceHealth(model, metrics) {

const healthScore = calculatePerformanceHealth(model, metrics);

if (healthScore < 0.8) {

return {

status: 'degraded',

recommendations: generateImprovementActions(model, metrics),

automatic_adjustments: applyAutomaticOptimizations(model, metrics)

};

}

return { status: 'healthy', score: healthScore };

}

```

When to Apply Performance Scaling

1. Task Initialization: Set performance targets based on model and task type
2. Mid-Execution Adjustment: Adapt parameters based on current performance
3. Resource Optimization: Scale resource allocation based on availability
4. Quality-Performance Tradeoffs: Balance speed vs accuracy based on requirements

Integration Points

• Orchestrator Agent: Use for task planning and resource allocation
• All Specialized Agents: Use for model-specific execution optimization
• Quality Controller: Use for adaptive quality target setting
• Background Task Manager: Use for concurrent task optimization

This skill ensures optimal performance across all supported models while maintaining high quality standards and adapting to varying task requirements.