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Best AI Models 2025: Complete Local AI Model Comparison Guide |
Compare the best AI models for local installation including Llama 3.2, Qwen 2.5, Phi 3.5, and DeepSeek R1. Find the perfect AI model for coding, writing, and general use. |
best AI models 2025, Llama 3.2, Qwen 2.5, Phi 3.5, local AI models, AI model comparison, coding AI models |
TL;DR: Just download llama3.2:3b and start there. It's like the iPhone of AI models - works great for most people.
Complete guide to choosing the best AI models for local installation. Compare Llama 3.2, Qwen 2.5, Phi 3.5, and other top AI models for coding, writing, and general use.
llama3.2:3b - This is what I recommend to everyone
- Handles conversations, writing, and basic coding pretty well
- Small enough to run on most computers
- Fast enough that you won't get impatient
phi3.5:3.8b - Microsoft's entry
- Really efficient for what it does
- Good for learning without burning through your RAM
qwen2.5:7b - The multilingual one
- Great if you need languages other than English
- Solid reasoning abilities
gemma2:9b - Google's offering
- Reliable and well-tested
- Good all-around performance
tinyllama:1.1b - Ultra-lightweight option
- Trained on 3 trillion tokens despite tiny size
- Perfect for very old hardware or embedded devices
Perfect for older computers or when you need speed over everything:
- smollm2:135m - The absolute smallest model that's still useful
- smollm2:360m - The smallest capable model you can run
- qwen3:0.6b - Alibaba's tiny but surprisingly smart model
- tinyllama:1.1b - Compact 1.1B model trained on 3 trillion tokens
- smollm2:1.7b - Microsoft's lightweight champion
- qwen3:1.7b - Step up with better reasoning
- llama3.2:1b - Meta's compact dialogue model
- sailor2:1b - Multilingual model for South-East Asian languages
Real talk: These aren't going to blow your mind, but they're genuinely useful for basic tasks and run on anything.
Most people should start here:
- llama3.2:3b - My go-to recommendation for beginners
- stable-code:3b - Excellent coding model that punches above its weight
- phi3.5:3.8b - Microsoft's reliable workhorse
- qwen3:4b - Great balance of capability and speed
- command-r7b - Cohere's model with advanced Arabic language capabilities
- qwen2.5:7b - Excellent multilingual support
- starcoder2:7b - Transparently trained open code model
- codestral:7b - Mistral's first dedicated code generation model
- qwen3:8b - Latest generation, very capable
- sailor2:8b - Specialized for South-East Asian languages
- opencoder:8b - Bilingual (English/Chinese) coding model
- glm4:9b - Supports 26 languages, strong at math
- gemma3n:9b - Google's efficient model for everyday devices
For when you need serious capability:
- llama3.2-vision:11b - Text + image understanding
- mistral-nemo:12b - Great efficiency for its size
- olmo2:13b - Competitive with much larger models
- llava:13b - Excellent vision + language model
- phi-4:14b - Microsoft's new reasoning powerhouse
- qwen2.5:14b - Solid general-purpose model
- qwen3:14b - Updated version with better reasoning
- deepseek-coder-v2:16b - Top-tier coding capabilities
- deepcoder:14b - New coding specialist at o3-mini level
- sailor2:20b - Specialized for South-East Asian languages
- gemma3n:27b - Google's larger efficient model
- qwq:32b - Reasoning specialist for complex problems
Only if you've got serious hardware:
- athene-v2:72b - Excellent for mathematics and technical tasks
- llama3.1:70b - Still one of the best general models
- llama3.3:70b - Meta's latest large model
- llama3.2-vision:90b - Most capable vision model
- qwen3:32b - Excellent performance across tasks
- deepseek-v3 - Cutting-edge 671B MoE model (37B active)
- qwen3:235b - Flagship model from Alibaba (MoE, 22B active)
- gpt-oss - OpenAI's open-weight models with reasoning, function calling, and configurable thinking effort (84.1K pulls in 5 hours!)
- phi-4:14b - Microsoft's new reasoning model rivaling much larger models in complex tasks
- qwen3 series - Alibaba's massive update with models from 0.6B to 235B, includes "thinking" mode
- deepcoder:14b - Fully open-source coding model performing at o3-mini level
- mistral-small-3.1 - Mistral's latest with vision understanding and 128K context
- olmo2:7b and olmo2:13b - AI2's open models competitive with Llama 3.1
- tulu3 - Allen Institute's leading instruction-following model
- granite3.2 - IBM's updated models with 128K context and better reasoning
- deepcoder:14b - New coding specialist performing at the level of o3-mini
- opencoder:8b - Bilingual (English/Chinese) coding model
- starcoder2:15b - Larger transparently trained code model
- codestral - Mistral's first dedicated code generation model
- codegemma - Google's lightweight coding specialist
- dolphin - Uncensored instruct-tuned models based on Llama and Mistral
- command-r - Large language model optimized for conversational interaction
- athene-v2:72b - Excellent for mathematics and technical tasks (if you have the hardware)
- sailor2:8b - Specialized for South-East Asian languages
- gemma3n:9b - Google's efficient model for everyday devices
- stable-code:3b - Efficient coding specialist with impressive performance
- llava:13b - Vision + language model for image understanding
- bespoke-minicheck - Factuality checking model to detect hallucinations
Fair warning: The 30B+ models need serious hardware. Most people are better off with 3B-14B models that actually run well on their machines.
If you're building search or retrieval applications, these models generate vector embeddings:
- snowflake-arctic-embed - High-performance text embedding suite with multilingual support
- bge-m3 - Multi-functionality, multilinguality, and multi-granularity embedding model
- all-minilm - Lightweight sentence embedding models (22M and 33M parameters)
Most people don't need these unless you're building specific search or RAG applications.
Think of model size like engine displacement in cars - bigger usually means more powerful, but also uses more resources:
- 3B models - Like a fuel-efficient compact car. Quick responses, handles basic tasks
- 7B models - Like a mid-size sedan. Good balance of power and efficiency
- 13B models - Like a performance car. More capable but needs premium fuel (RAM)
- 30B+ models - Like a supercar. Incredibly capable but most people can't afford to run them
- 🎯 Just start with llama3.2:3b - seriously, stop overthinking it
- 🌍 Need other languages? Try qwen3:4b or glm4:9b (supports 26 languages)
- 💻 Want coding help? Try stable-code:3b or qwen3-coder:7b
- �️ Want to work with images? Grab llama3.2-vision:11b
- 🧠 Need complex reasoning? Try qwq:32b if you have the hardware
- �🚀 Got a powerful machine and want quality? Go for qwen3:14b or olmo2:13b
These names look like gibberish but there's a pattern:
- llama3.2 = Model family and version
- 8b = Size (8 billion parameters - bigger number = smarter but slower)
- q4_k_m = How much it's compressed (smaller file, tiny bit less quality)
- Most people are perfectly happy with llama3.2:3b
- You can download different models anytime - it's not a marriage
- Switching between models in LM Studio or Ollama takes like 30 seconds
Start simple, see what you actually need, then upgrade if you want to.