Local LLM Model Comparison Tool: Compare 2025's Best Open Source Models

Interactive Model Comparison Tool

Select up to 4 models from our curated list of the latest and most popular open source LLMs. Compare their specifications, benchmark performance, hardware requirements, and licensing terms in an easy-to-read format.

How to Use This Comparison Tool

Our model comparison tool is designed to help you quickly identify the best local LLM for your specific needs. Here's how to get the most out of it:

Step 1: Consider Your Hardware Constraints

Before selecting models, assess your available hardware. The VRAM requirements listed are for FP16 precision - you can reduce these by 50-75% using 4-bit quantization. If you have limited VRAM, focus on smaller models like Yi-Coder 9B or Phi-4 14B.

Step 2: Identify Your Primary Use Case

Different models excel at different tasks. Use these guidelines to narrow your selection:

• Coding & Development: Qwen3-Coder, Codestral 25.08, Qwen 2.5 Coder
• Mathematical Reasoning: Qwen3-Thinking, DeepSeek R1, Phi-4
• General Chat & Assistance: Llama 3.3, Mistral Small, Gemma 3
• Research & Analysis: Command R+, Llama 4 Scout (long context)
• Lightweight/Edge Deployment: Yi-Coder 9B, Phi-4 14B

Step 3: Compare Key Specifications

Pay attention to these critical factors when comparing models:

• Context Window: Larger context windows allow processing longer documents
• License: Ensure the license meets your commercial/research needs
• Release Date: Newer models often incorporate latest techniques and training data
• Benchmark Scores: Look for performance in areas relevant to your use case

Understanding Model Categories

Flagship Models (70B+ Parameters)

These models offer the highest quality but require significant hardware resources. Examples include Llama 3.3 70B and Command R+ 104B. Best for applications where quality is paramount and hardware isn't a constraint.

Efficient Large Models (20-50B Parameters)

Models like Qwen3-Thinking 235B (22B active) and Codestral 25.08 provide excellent performance with more reasonable hardware requirements. These represent the sweet spot for many production applications.

Compact Models (7-15B Parameters)

Models such as Yi-Coder 9B and Phi-4 14B are designed for resource-constrained environments while maintaining good performance. Ideal for edge deployment, personal use, or when hardware is limited.

Mixture of Experts (MoE) Models

MoE models like Qwen3-Coder (480B total, 35B active) offer the performance of large models with the efficiency of smaller ones. They activate only a subset of parameters for each inference, reducing computational requirements.

Benchmark Interpretation Guide

Understanding benchmark scores helps you evaluate model performance objectively:

Coding Benchmarks

• HumanEval: Measures ability to generate correct Python functions from docstrings
• MBPP: Tests programming problem-solving with more complex, multi-step challenges
• LiveCodeBench: Evaluates performance on recent, real-world coding problems
• SWE-Bench: Assesses ability to resolve GitHub issues in real repositories

Reasoning Benchmarks

• MMLU: Tests general knowledge across 57 academic subjects
• AIME: Evaluates mathematical reasoning using competition-level problems
• GPQA: Measures graduate-level scientific reasoning ability
• Arena-Hard: Compares models on challenging, open-ended questions

Hardware Requirements Planning

Proper hardware planning is crucial for successful local LLM deployment. Here's what you need to know:

VRAM Considerations

The VRAM requirements in our comparison assume FP16 precision. You can significantly reduce these requirements using quantization:

• 4-bit quantization: Reduces VRAM by ~75% with minimal quality loss
• 8-bit quantization: Reduces VRAM by ~50% with negligible quality impact
• Context length impact: Longer contexts require additional VRAM for attention cache

CPU and RAM Requirements

While GPU acceleration is preferred, CPU inference is possible for smaller models. Ensure you have sufficient system RAM (typically 2x the model size) for CPU inference or as overflow when VRAM is insufficient.

Licensing Considerations

Model licenses significantly impact how you can use and deploy these models:

Model Selection Best Practices

Follow these best practices when selecting a model for your project:

Start with Your Constraints

Begin by identifying your hard constraints: available hardware, licensing requirements, and performance needs. This will help you narrow down the field quickly.

Test Multiple Candidates

Benchmarks provide guidance, but real-world performance on your specific tasks is what matters. Download and test 2-3 candidate models with your actual use cases.

Consider Future Scaling

If you plan to scale your application, consider models with commercial-friendly licenses and reasonable hardware requirements that won't become prohibitive at scale.

Stay Updated

The local LLM landscape evolves rapidly. New models are released monthly, often with significant improvements. Regularly revisit your model choice as new options become available.

Frequently Asked Questions

Which model should I choose for my first local LLM project?

For beginners, we recommend starting with Phi-4 14B or Yi-Coder 9B. These models offer good performance with modest hardware requirements and permissive licenses, making them ideal for learning and experimentation.

Can I run these models on Apple Silicon Macs?

Yes! Many of these models run well on Apple Silicon using tools like Ollama or LM Studio. Models up to 30B parameters typically run smoothly on Macs with 32GB+ unified memory. The unified memory architecture allows using system RAM as VRAM.

How do I know if a model will fit in my VRAM?

Use this rough calculation: Model parameters × precision (2 bytes for FP16, 0.5 bytes for 4-bit) + context cache (varies by length) + overhead (~20%). Our comparison tool shows FP16 requirements - divide by 4 for 4-bit quantized estimates.

What's the difference between instruct and base models?

Instruct models are fine-tuned to follow instructions and engage in conversations, making them ready for chat applications. Base models are trained only on text prediction and typically require additional fine-tuning for specific tasks.

This comparison tool is updated regularly with the latest model releases and benchmark results. Data sources include official model releases, Hugging Face, research papers, and community benchmarks. Last updated: January 2025.