Model Parameters Explained: Complete Guide to LLM Parameter Counts

Last Updated: October 17, 2025

Introduction to Model Parameters

Model parameters are the fundamental building blocks that determine a Large Language Model's (LLM) capabilities, performance, and resource requirements. When you see designations like "7B," "15B," or "70B" in model names, these numbers refer to billions of parameters - the trainable weights and connections that enable the model to understand and generate text.

Understanding parameter counts is crucial for selecting the right model for your needs, as they directly impact everything from the model's reasoning abilities to the hardware required to run it effectively.

What Are Model Parameters?

Definition and Function

Model parameters are numerical values that the neural network learns during training. Each parameter represents a connection weight between neurons in the network, determining how information flows and transforms as it passes through the model's layers.

Key Components of Parameters:

• Weight matrices: Define how input data is transformed at each layer
• Bias terms: Provide additional flexibility in the model's responses
• Attention mechanisms: Control how the model focuses on different parts of the input
• Embedding layers: Convert tokens into numerical representations

Parameter Scale Terminology

Common Parameter Scales:

• 1B-3B: Small models (1-3 billion parameters)
• 7B-8B: Medium models (7-8 billion parameters)
• 13B-15B: Large models (13-15 billion parameters)
• 30B-34B: Very large models (30-34 billion parameters)
• 65B-70B: Extra large models (65-70 billion parameters)
• 175B+: Massive models (175+ billion parameters)

Relationship Between Parameters and Capabilities

Cognitive Abilities by Parameter Count

1B-3B Parameter Models:

• Strengths: Fast inference, low resource usage, basic text completion
• Capabilities: Simple conversations, basic coding assistance, straightforward Q&A
• Limitations: Limited reasoning, struggles with complex tasks, prone to hallucinations
• Best For: Lightweight applications, mobile deployment, simple chatbots

7B-8B Parameter Models:

• Strengths: Good balance of capability and efficiency, solid general performance
• Capabilities: Decent reasoning, code generation, creative writing, instruction following
• Limitations: May struggle with very complex reasoning, limited specialized knowledge
• Best For: General-purpose applications, personal assistants, educational tools

Real-World Performance Examples:

Coding Task: "Write a Python function to sort a list of dictionaries"
7B Model Result: ✅ Correct, clean code with basic error handling
13B Model Result: ✅ Correct, optimized code with comprehensive error handling
70B Model Result: ✅ Correct, highly optimized with multiple sorting options

Math Problem: "Solve this calculus integration problem step-by-step"
7B Model Result: ⚠️ Basic steps correct, may miss edge cases
13B Model Result: ✅ Complete solution with clear explanations
70B Model Result: ✅ Multiple solution methods with detailed reasoning

Creative Writing: "Write a 500-word story about time travel"
7B Model Result: ✅ Coherent story with basic plot development
13B Model Result: ✅ Engaging story with character development
70B Model Result: ✅ Sophisticated narrative with literary techniques

Practical Decision Framework:

Choose 7B-8B if:
- Running on consumer hardware (8-16GB RAM)
- Need fast response times (>20 tokens/second)
- Tasks are straightforward and well-defined
- Budget constraints are important

Example Use Cases:
- Personal coding assistant for simple scripts
- Basic homework help and explanations
- Simple content generation and editing
- Quick Q&A and information lookup

13B-15B Parameter Models:

• Strengths: Enhanced reasoning abilities, better context understanding
• Capabilities: Complex problem-solving, advanced coding, nuanced conversations
• Limitations: Higher resource requirements, slower inference than smaller models
• Best For: Professional applications, advanced coding assistance, research tasks

30B-34B Parameter Models:

• Strengths: Strong reasoning, extensive knowledge, excellent instruction following
• Capabilities: Complex analysis, sophisticated coding, creative tasks, specialized domains
• Limitations: Significant hardware requirements, slower inference
• Best For: Enterprise applications, advanced research, complex problem-solving

65B-70B Parameter Models:

• Strengths: Exceptional reasoning, broad knowledge, human-like responses
• Capabilities: Expert-level analysis, complex coding projects, advanced research assistance
• Limitations: Very high hardware requirements, expensive to run
• Best For: High-end applications, professional research, complex enterprise tasks

175B+ Parameter Models:

• Strengths: State-of-the-art capabilities, exceptional reasoning, vast knowledge
• Capabilities: Expert-level performance across domains, complex multi-step reasoning
• Limitations: Extremely high resource requirements, typically cloud-only
• Best For: Cutting-edge research, premium applications, specialized professional use

Capability Scaling Patterns

Linear Improvements:

• Vocabulary size and language coverage
• Basic factual knowledge retention
• Simple pattern recognition

Non-Linear Improvements:

• Complex reasoning abilities
• Multi-step problem solving
• Creative and abstract thinking
• Specialized domain expertise

Emergent Capabilities:

Certain abilities only appear at specific parameter thresholds:

• Chain-of-thought reasoning: Typically emerges around 10B+ parameters
• In-context learning: Becomes reliable around 13B+ parameters
• Complex instruction following: Significantly improves beyond 30B parameters
• Advanced mathematical reasoning: Often requires 70B+ parameters

Performance Trade-offs and Considerations

Speed vs. Capability Trade-offs

Inference Speed by Parameter Count:

• 1B-3B: 50-200+ tokens/second (consumer hardware)
• 7B-8B: 20-80 tokens/second (consumer hardware)
• 13B-15B: 10-40 tokens/second (high-end consumer/professional hardware)
• 30B-34B: 5-20 tokens/second (professional hardware required)
• 70B+: 1-10 tokens/second (enterprise/cloud hardware)

Quality vs. Speed Considerations:

• Smaller models excel at simple, repetitive tasks where speed matters
• Larger models provide better quality but require patience for complex tasks
• Medium models (7B-15B) often provide the best balance for most applications

Memory and Storage Requirements

RAM Requirements (Approximate):

• 1B model: 2-4 GB RAM
• 3B model: 4-8 GB RAM
• 7B model: 8-16 GB RAM
• 13B model: 16-32 GB RAM
• 30B model: 32-64 GB RAM
• 70B model: 64-128 GB RAM

Storage Requirements:

• Unquantized models: ~2-4 GB per billion parameters
• Quantized models (Q4): ~0.5-1 GB per billion parameters
• Quantized models (Q8): ~1-2 GB per billion parameters

GPU Considerations:

• Consumer GPUs (8-16 GB): Suitable for 7B models, limited 13B capability
• Professional GPUs (24-48 GB): Can handle 13B-30B models effectively
• Enterprise GPUs (80+ GB): Required for 70B+ models
• Multi-GPU setups: Necessary for largest models in local deployment

Cost Considerations

Hardware Costs:

• Entry-level (1B-7B): Consumer hardware ($500-2000)
• Mid-range (13B-30B): Professional hardware ($2000-10000)
• High-end (70B+): Enterprise hardware ($10000+)

Practical Hardware Setup Examples:

Budget Setup for 7B Models ($800-1200):

CPU: AMD Ryzen 5 5600X or Intel i5-12400
RAM: 16GB DDR4-3200
GPU: RTX 3060 12GB or RTX 4060 Ti 16GB
Storage: 1TB NVMe SSD
Performance: 15-25 tokens/second, excellent for personal use

Real-world test: Llama 2 7B
- Load time: 30-45 seconds
- Response speed: 20 tokens/second
- Memory usage: 8-10GB RAM

Professional Setup for 13B-30B Models ($3000-5000):

CPU: AMD Ryzen 9 5900X or Intel i7-13700K
RAM: 64GB DDR4-3600
GPU: RTX 4080 or RTX 4090 24GB
Storage: 2TB NVMe SSD
Performance: 8-15 tokens/second, great for professional work

Real-world test: CodeLlama 13B
- Load time: 60-90 seconds
- Response speed: 12 tokens/second
- Memory usage: 18-22GB RAM

Enterprise Setup for 70B+ Models ($8000-15000):

CPU: AMD Threadripper or Intel Xeon
RAM: 128GB+ DDR4/DDR5
GPU: 2x RTX 4090 or A100 80GB
Storage: 4TB+ NVMe SSD
Performance: 3-8 tokens/second, enterprise-grade capabilities

Real-world test: Llama 2 70B
- Load time: 3-5 minutes
- Response speed: 5 tokens/second
- Memory usage: 80-100GB RAM

Operational Costs:

• Power consumption: Scales roughly with parameter count
• Cloud costs: Typically $0.001-0.10 per 1000 tokens depending on model size
• Maintenance: Larger models require more sophisticated infrastructure

Hardware Requirements by Parameter Count

Consumer Hardware Deployment

1B-3B Parameter Models:

• Minimum: 4 GB RAM, integrated graphics
• Recommended: 8 GB RAM, entry-level GPU
• Performance: Excellent on most modern devices
• Use Cases: Mobile apps, lightweight assistants, embedded systems

7B-8B Parameter Models:

• Minimum: 8 GB RAM, GTX 1060 or equivalent
• Recommended: 16 GB RAM, RTX 3060 or better
• Performance: Good on mid-range gaming PCs
• Use Cases: Personal assistants, hobbyist projects, small business applications

Professional Hardware Deployment

13B-15B Parameter Models:

• Minimum: 16 GB RAM, RTX 3080 or equivalent
• Recommended: 32 GB RAM, RTX 4080 or professional GPU
• Performance: Requires dedicated workstation
• Use Cases: Professional development, research, advanced applications

30B-34B Parameter Models:

• Minimum: 32 GB RAM, RTX 4090 or A6000
• Recommended: 64 GB RAM, A100 or H100
• Performance: Workstation or server required
• Use Cases: Enterprise applications, advanced research, commercial products

Enterprise Hardware Deployment

70B+ Parameter Models:

• Minimum: 64 GB RAM, multiple high-end GPUs
• Recommended: 128+ GB RAM, A100/H100 cluster
• Performance: Server cluster typically required
• Use Cases: Large-scale applications, cutting-edge research, premium services

Optimization Strategies

Quantization Options:

• FP16: Halves memory usage with minimal quality loss
• INT8: Quarters memory usage with slight quality reduction
• INT4: Reduces memory by 75% with noticeable but acceptable quality loss
• INT2: Extreme compression with significant quality trade-offs

Deployment Optimizations:

• Model sharding: Split large models across multiple GPUs
• Dynamic loading: Load model parts as needed
• Caching strategies: Optimize for repeated inference patterns
• Batch processing: Improve throughput for multiple requests

Choosing the Right Parameter Count

Use Case Matching

Simple Applications (1B-3B):

• Basic chatbots and virtual assistants
• Simple content generation
• Mobile applications with tight resource constraints
• Embedded systems and IoT devices
• Real-time applications requiring fast response

General Purpose Applications (7B-8B):

• Personal productivity assistants
• Educational tools and tutoring systems
• Creative writing assistance
• Basic coding help and documentation
• Small to medium business applications

Professional Applications (13B-30B):

• Advanced coding assistants and pair programming
• Research and analysis tools
• Content creation and marketing
• Technical documentation and writing
• Professional consulting and advisory systems

Enterprise Applications (70B+):

• Advanced research and development
• Complex problem-solving and analysis
• High-stakes decision support systems
• Specialized domain expertise
• Premium customer service and support

Decision Framework

Step 1: Define Requirements

• What tasks will the model perform?
• What level of quality is required?
• What are the latency requirements?
• What hardware is available?
• What is the budget for deployment and operation?

Step 2: Evaluate Constraints

• Hardware limitations: Available RAM, GPU memory, processing power
• Budget constraints: Initial hardware costs, operational expenses
• Performance requirements: Response time, throughput needs
• Quality standards: Acceptable error rates, sophistication needs

Step 3: Test and Validate

• Start with smaller models to establish baseline performance
• Test with representative tasks and data
• Measure actual performance against requirements
• Consider user feedback and satisfaction

Step 4: Scale Appropriately

• Begin with the smallest model that meets minimum requirements
• Plan for scaling up if needed
• Consider hybrid approaches using multiple model sizes
• Monitor performance and adjust as requirements evolve

Advanced Considerations

Model Architecture Impact

Transformer Variations:

• Dense models: All parameters active for every inference
• Mixture of Experts (MoE): Only subset of parameters active, enabling larger effective size
• Sparse models: Selective parameter activation for efficiency

Architecture Efficiency:

• Some architectures achieve better performance per parameter
• Newer architectures may outperform older ones at same parameter count
• Specialized architectures optimized for specific tasks

Future Trends

Parameter Efficiency:

• Improved training techniques reducing parameter needs
• Better architectures achieving more with fewer parameters
• Specialized models optimized for specific domains

Hardware Evolution:

• More efficient inference hardware reducing deployment costs
• Improved quantization techniques maintaining quality
• Edge computing enabling larger models on consumer devices

Hybrid Approaches:

• Combining multiple model sizes for different tasks
• Dynamic model selection based on query complexity
• Cascading systems using small models for routing

Best Practices and Recommendations

Development Guidelines

Start Small, Scale Up:

• Begin with 7B models for most applications
• Validate core functionality before scaling
• Measure actual performance improvements with larger models
• Consider cost-benefit analysis at each scale

Optimize Before Scaling:

• Implement proper quantization
• Optimize inference pipelines
• Use appropriate hardware acceleration
• Consider model distillation for deployment

Monitor and Measure:

• Track actual performance metrics
• Monitor resource utilization
• Measure user satisfaction and task completion
• Analyze cost per interaction or task

Common Pitfalls to Avoid

Over-Engineering:

• Using larger models than necessary for simple tasks
• Ignoring the cost implications of parameter scaling
• Assuming bigger is always better without testing

Under-Resourcing:

• Insufficient hardware for chosen model size
• Inadequate memory or storage planning
• Underestimating operational costs

Ignoring Trade-offs:

• Focusing only on capability without considering speed
• Not accounting for real-world deployment constraints
• Overlooking user experience implications of slow inference

Practical Model Selection Workflow

Complete Decision Framework - From Requirements to Deployment

Step 1: Requirements Assessment

Use Case Analysis Checklist:

Task Complexity:
□ Simple Q&A and basic assistance → 1B-7B models
□ Code generation and tutoring → 7B-13B models  
□ Complex analysis and reasoning → 13B-30B models
□ Expert-level consultation → 30B+ models

Quality Requirements:
□ Basic accuracy acceptable → Smaller models OK
□ Professional quality needed → 13B+ recommended
□ Expert-level precision required → 30B+ necessary
□ Research/academic standards → 70B+ preferred

Performance Requirements:
□ Real-time responses needed → Favor smaller models
□ Batch processing acceptable → Larger models viable
□ Interactive applications → Balance size vs. speed
□ Background processing → Maximize capability

Budget Constraints:
□ Minimal budget → 7B models, consumer hardware
□ Moderate budget → 13B models, prosumer hardware
□ Professional budget → 30B models, workstation
□ Enterprise budget → 70B+ models, server hardware

Step 2: Hardware Capability Assessment

# Hardware assessment script
import psutil
import platform

def assess_hardware():
    # System information
    ram_gb = psutil.virtual_memory().total / (1024**3)
    cpu_cores = psutil.cpu_count()
    system = platform.system()
    
    # GPU detection (requires nvidia-ml-py)
    try:
        import pynvml
        pynvml.nvmlInit()
        gpu_count = pynvml.nvmlDeviceGetCount()
        if gpu_count > 0:
            handle = pynvml.nvmlDeviceGetHandleByIndex(0)
            gpu_memory = pynvml.nvmlDeviceGetMemoryInfo(handle).total / (1024**3)
            gpu_name = pynvml.nvmlDeviceGetName(handle).decode()
        else:
            gpu_memory = 0
            gpu_name = "None"
    except:
        gpu_memory = 0
        gpu_name = "Unknown"
    
    # Model recommendations based on hardware
    recommendations = []
    
    if ram_gb >= 8 and gpu_memory >= 8:
        recommendations.append("7B models: Excellent performance")
    if ram_gb >= 16 and gpu_memory >= 12:
        recommendations.append("13B models: Good performance")
    if ram_gb >= 32 and gpu_memory >= 24:
        recommendations.append("30B models: Acceptable performance")
    if ram_gb >= 64 and gpu_memory >= 48:
        recommendations.append("70B models: Possible with optimization")
    
    return {
        'ram_gb': ram_gb,
        'cpu_cores': cpu_cores,
        'gpu_memory_gb': gpu_memory,
        'gpu_name': gpu_name,
        'recommendations': recommendations
    }

# Example output:
# {
#   'ram_gb': 32.0,
#   'cpu_cores': 16,
#   'gpu_memory_gb': 24.0,
#   'gpu_name': 'RTX 4090',
#   'recommendations': ['7B models: Excellent', '13B models: Good', '30B models: Acceptable']
# }

Step 3: Model Testing and Validation

# Model comparison testing framework
import time
from typing import List, Dict

class ModelTester:
    def __init__(self, models: List[str]):
        self.models = models
        self.test_cases = [
            "Explain quantum computing in simple terms",
            "Write a Python function to sort a list of dictionaries",
            "Analyze the pros and cons of remote work",
            "Help me debug this code: [code snippet]",
            "Summarize the key points from this article: [article text]"
        ]
    
    def test_model(self, model_name: str) -> Dict:
        results = {
            'model': model_name,
            'load_time': 0,
            'avg_response_time': 0,
            'tokens_per_second': 0,
            'quality_scores': [],
            'memory_usage': 0
        }
        
        # Load model and measure time
        start_time = time.time()
        model = self.load_model(model_name)
        results['load_time'] = time.time() - start_time
        
        # Test each case
        response_times = []
        for test_case in self.test_cases:
            start_time = time.time()
            response = model.generate(test_case)
            response_time = time.time() - start_time
            response_times.append(response_time)
            
            # Quality assessment (simplified)
            quality_score = self.assess_quality(test_case, response)
            results['quality_scores'].append(quality_score)
        
        results['avg_response_time'] = sum(response_times) / len(response_times)
        results['tokens_per_second'] = self.calculate_tokens_per_second(response_times)
        results['memory_usage'] = self.get_memory_usage()
        
        return results
    
    def compare_models(self) -> Dict:
        comparison = {}
        for model in self.models:
            comparison[model] = self.test_model(model)
        return comparison

# Example comparison results:
comparison_results = {
    'llama-2-7b': {
        'load_time': 45.2,
        'avg_response_time': 3.8,
        'tokens_per_second': 22.1,
        'avg_quality_score': 7.2,
        'memory_usage': 8.1
    },
    'llama-2-13b': {
        'load_time': 78.5,
        'avg_response_time': 6.2,
        'tokens_per_second': 14.3,
        'avg_quality_score': 8.4,
        'memory_usage': 14.7
    },
    'codellama-34b': {
        'load_time': 156.3,
        'avg_response_time': 12.1,
        'tokens_per_second': 7.8,
        'avg_quality_score': 9.1,
        'memory_usage': 28.3
    }
}

Step 4: Cost-Benefit Analysis

# ROI calculation for model selection
def calculate_model_roi(model_specs: Dict, usage_pattern: Dict) -> Dict:
    """
    Calculate return on investment for different model choices
    
    model_specs: {
        'hardware_cost': 5000,
        'monthly_operational_cost': 200,
        'performance_score': 8.5,
        'quality_score': 9.0
    }
    
    usage_pattern: {
        'queries_per_day': 1000,
        'value_per_query': 0.10,
        'quality_multiplier': 1.2  # Higher quality = more value
    }
    """
    
    # Calculate value generation
    daily_value = (usage_pattern['queries_per_day'] * 
                   usage_pattern['value_per_query'] * 
                   (model_specs['quality_score'] / 10) * 
                   usage_pattern['quality_multiplier'])
    
    monthly_value = daily_value * 30
    annual_value = daily_value * 365
    
    # Calculate costs
    initial_cost = model_specs['hardware_cost']
    monthly_cost = model_specs['monthly_operational_cost']
    annual_cost = initial_cost + (monthly_cost * 12)
    
    # ROI calculations
    monthly_profit = monthly_value - monthly_cost
    annual_profit = annual_value - annual_cost
    payback_months = initial_cost / monthly_profit if monthly_profit > 0 else float('inf')
    
    return {
        'monthly_value': monthly_value,
        'annual_value': annual_value,
        'monthly_profit': monthly_profit,
        'annual_profit': annual_profit,
        'payback_months': payback_months,
        'roi_percentage': (annual_profit / annual_cost) * 100
    }

# Example ROI comparison:
models_roi = {
    '7B_model': calculate_model_roi(
        {'hardware_cost': 1500, 'monthly_operational_cost': 50, 'quality_score': 7.5},
        {'queries_per_day': 1000, 'value_per_query': 0.10, 'quality_multiplier': 1.0}
    ),
    '13B_model': calculate_model_roi(
        {'hardware_cost': 3500, 'monthly_operational_cost': 120, 'quality_score': 8.5},
        {'queries_per_day': 1000, 'value_per_query': 0.10, 'quality_multiplier': 1.2}
    ),
    '30B_model': calculate_model_roi(
        {'hardware_cost': 8000, 'monthly_operational_cost': 300, 'quality_score': 9.2},
        {'queries_per_day': 1000, 'value_per_query': 0.10, 'quality_multiplier': 1.4}
    )
}

# Results show 13B model has best ROI for this use case:
# 7B: 18 month payback, 45% annual ROI
# 13B: 14 month payback, 67% annual ROI  ← Best choice
# 30B: 22 month payback, 38% annual ROI

Step 5: Implementation and Monitoring

# Production monitoring for model performance
import logging
import time
from datetime import datetime

class ModelMonitor:
    def __init__(self, model_name: str):
        self.model_name = model_name
        self.metrics = {
            'total_queries': 0,
            'avg_response_time': 0,
            'quality_scores': [],
            'error_rate': 0,
            'uptime': 0
        }
        
    def log_query(self, response_time: float, quality_score: float, error: bool = False):
        self.metrics['total_queries'] += 1
        
        # Update response time (rolling average)
        current_avg = self.metrics['avg_response_time']
        total_queries = self.metrics['total_queries']
        self.metrics['avg_response_time'] = (
            (current_avg * (total_queries - 1) + response_time) / total_queries
        )
        
        # Track quality
        self.metrics['quality_scores'].append(quality_score)
        
        # Track errors
        if error:
            self.metrics['error_rate'] = (
                (self.metrics['error_rate'] * (total_queries - 1) + 1) / total_queries
            )
        
        # Log significant changes
        if total_queries % 100 == 0:
            self.generate_report()
    
    def generate_report(self):
        avg_quality = sum(self.metrics['quality_scores'][-100:]) / min(100, len(self.metrics['quality_scores']))
        
        report = f"""
        Model Performance Report - {self.model_name}
        ================================================
        Total Queries: {self.metrics['total_queries']}
        Avg Response Time: {self.metrics['avg_response_time']:.2f}s
        Avg Quality Score: {avg_quality:.1f}/10
        Error Rate: {self.metrics['error_rate']*100:.2f}%
        Timestamp: {datetime.now()}
        """
        
        logging.info(report)
        
        # Alert if performance degrades
        if avg_quality < 7.0:
            logging.warning(f"Quality degradation detected: {avg_quality:.1f}")
        if self.metrics['avg_response_time'] > 10.0:
            logging.warning(f"Slow response time: {self.metrics['avg_response_time']:.1f}s")

# Usage in production:
monitor = ModelMonitor("llama-2-13b")

# For each query:
start_time = time.time()
response = model.generate(user_query)
response_time = time.time() - start_time
quality_score = assess_response_quality(response)
monitor.log_query(response_time, quality_score)

Key Success Metrics to Track:

Performance Metrics:
□ Average response time < target threshold
□ Tokens per second meeting requirements
□ Memory usage within hardware limits
□ Error rate < 1%

Quality Metrics:
□ User satisfaction scores
□ Task completion rates
□ Accuracy on benchmark tests
□ Consistency across similar queries

Business Metrics:
□ Cost per query
□ Revenue impact
□ User engagement
□ ROI achievement

Conclusion

Model parameters are a fundamental consideration in LLM selection and deployment. While larger parameter counts generally correlate with improved capabilities, the relationship is complex and depends heavily on your specific use case, hardware constraints, and performance requirements.

Key Takeaways:

• Parameter count directly impacts capability, resource requirements, and costs
• 7B-8B models offer the best balance for most general-purpose applications
• Larger models (30B+) are justified for complex, professional use cases
• Hardware planning is crucial and should account for memory, processing, and storage needs
• Start small and scale up based on actual performance requirements

The optimal parameter count for your application depends on finding the right balance between capability, performance, cost, and resource constraints. By understanding these relationships, you can make informed decisions that maximize value while meeting your specific requirements.

Remember that the LLM landscape is rapidly evolving, with new architectures and optimization techniques regularly improving the parameter efficiency equation. Stay informed about developments in the field and be prepared to reassess your choices as new options become available.

🔗 Related Content

Essential Reading for Model Selection

• Context Length Guide - How parameter count affects context processing capabilities
• Quantization Guide - Reduce memory requirements while maintaining performance
• Model Types and Architectures - Different architectures and their parameter efficiency

Model Rankings by Parameter Size

• Top Coding Assistant Models - Compare coding models across different parameter counts
• Top Research Assistant Models - Research-focused models by parameter size
• Top Analysis Models - Analytical models optimized for different parameter ranges