| title | Best AI Models 2026: Complete Local AI Model Comparison Guide |
|---|---|
| description | Compare the best AI models for local installation including Qwen 3.5, Gemma 4, gpt-oss, and Granite 4.1. Find the perfect AI model for coding, writing, and general use. |
| keywords | best AI models 2026, Qwen 3.5, Gemma 4, gpt-oss, Granite 4.1, local AI models, AI model comparison, coding AI models |
TL;DR: Just download qwen3.5:4b 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 Qwen 3.5, Gemma 4, gpt-oss, and other top AI models for coding, writing, and general use.
qwen3.5:4b (~3.4GB) - This is what I recommend to everyone
- Handles conversations, writing, images, and basic coding pretty well
- Small enough to run on most computers
- Fast enough that you won't get impatient
gemma3:4b (~3.3GB) - Google's small model
- Really efficient for what it does
- Good for learning without burning through your RAM
granite4.1:3b - IBM's compact model
- Apache 2.0 licensed, good at tool calling and structured output
- Nice when you want predictable, boring, correct answers
gemma4:12b (~7.6GB) - Google's mid-size option
- Reliable and well-tested
- Vision, audio, and tool calling built in
- qwen3.5:4b - My go-to for daily use
- qwen3.5:9b - The step up once you've got 16GB of RAM
- gemma4:12b - When you want higher quality (needs more power)
- granite4.1:8b - Reliable alternative, permissively licensed
- qwen3-coder:30b - Currently the best local coding model I've used
- devstral-small-2:24b - Built for agentic workflows across many files
- qwen3.5:9b - Good general coding without a dedicated coding model
- qwen2.5-coder:7b - Older but still very usable on modest hardware
- granite4.1:8b - Solid for technical tasks and structured output
- qwen3.5:9b - Good storyteller and fast
- gemma4:12b - Really excels at creative stuff
- mistral-medium-3.5 - Strong creative writing, but it's a 128B model
- gemma4:31b - Top tier but needs a beast of a machine
- gpt-oss:20b - OpenAI's open-weight reasoning model, the best value here
- qwen3.5 with thinking enabled - Reasoning without a separate download
- olmo-3.1:32b - Fully open training pipeline if you care about that
- gemma4 (e2b/e4b/12b/26b/31b) - Google's current family. Vision, audio, tool calling, thinking
- qwen3.5 (0.8b through 122b) - Multimodal with a 256K context window across the whole range
- qwen3.6 (27b/35b) - Newest Qwen, tuned for agentic coding
- gpt-oss (20b/120b) - OpenAI's open-weight models
- granite4.1 (3b/8b/30b) - IBM, Apache 2.0, enterprise-oriented
- ministral-3 (3b/8b/14b) - Mistral's edge-focused family
- lfm2.5:8b - Fast, reliable tool calling on consumer hardware
- qwen3-coder / qwen3-coder-next - Best-in-class local coding assistants
- qwen3-vl and minicpm-v4.5 - Image and video understanding
- glm-ocr and deepseek-ocr - Document OCR and extraction
Fair warning: The 30B+ models need serious hardware. Most people are better off with 4B-14B models that actually run well on their machines.
These don't chat. They turn text into vectors so you can search it - the engine behind "chat with your documents":
- embeddinggemma:300m - Google's compact embedding model, a good default
- nomic-embed-text - Large context window, very widely used
- qwen3-embedding (0.6b/4b/8b) - Range of sizes if you need more accuracy
- bge-m3 - Multilingual and multi-granularity
- all-minilm (22m/33m) - Tiny and fast when quality matters less than speed
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:
- 1B-4B models - Like a fuel-efficient compact car. Quick responses, handles basic tasks
- 8B-14B models - Like a mid-size sedan. Good balance of power and efficiency
- 24B-32B models - Like a performance car. More capable but needs premium fuel (RAM)
- 70B+ models - Like a supercar. Incredibly capable but most people can't afford to run them
You'll see tags like qwen3.5:35b-a3b or gemma4:26b-a4b. That means 35B total parameters with only ~3B active for any given token. Practical translation: it needs memory like a big model but runs at the speed of a small one. Great deal if you have the RAM.
Parameter count is a rough proxy; what decides whether a model runs well is whether its download fits in your GPU's VRAM. The hardware section in the README has a per-GPU sizing table, an explanation of what happens when a model only partly fits, and which GPUs are supported.
- Just start with qwen3.5:4b - seriously, stop overthinking it
- Need other languages? Any Qwen 3.5 size handles them well
- Want coding help? Try qwen3-coder:30b, or qwen2.5-coder:7b if that's too big
- Got a powerful machine and want quality? Go for gpt-oss:20b or gemma4:31b
Warning: Don't download a 70B model if you only have 8GB RAM - it won't work!
These names look like gibberish but there's a pattern:
- qwen3.5 = Model family and version
- 9b = Size (9 billion parameters - bigger number = smarter but slower)
- a3b = Active parameters, if it's a mixture-of-experts model
- q4_K_M = How much it's compressed (smaller file, tiny bit less quality)
- qat = Quantization-aware trained, so it holds quality better when compressed
- mlx = Built for Apple Silicon
- cloud = Runs on someone else's server, not yours
- Most people are perfectly happy with qwen3.5:4b
- 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.