What is the Ollama Model Library?
Ollama hosts hundreds of open-source AI models ready for one-command local deployment. This guide covers the best models for different tasks — coding, chat, reasoning, and specialized use cases. All models run locally on your hardware with full privacy.
Answer-Ready: Ollama Model Library provides 500+ open-source AI models for local use. One-command download and run. Best coding models: Qwen2.5-Coder, DeepSeek-Coder. Best chat: Llama 3.1, Mistral. Best reasoning: Phi-3, Gemma 2. All run locally with full privacy.
Best for: Developers choosing the right local model for their use case. Works with: Ollama, Jan, Open WebUI, Claude Code (as backend). Setup time: Under 2 minutes per model.
Best Models by Task
Coding Models
| Model | Size | Strength | Command |
|---|---|---|---|
| Qwen2.5-Coder | 7B/32B | Best overall coding | ollama run qwen2.5-coder:7b |
| DeepSeek-Coder V2 | 16B | Complex reasoning | ollama run deepseek-coder-v2:16b |
| CodeLlama | 7B/34B | Code completion | ollama run codellama:7b |
| Starcoder2 | 3B/7B/15B | Multi-language | ollama run starcoder2:7b |
Chat Models
| Model | Size | Strength | Command |
|---|---|---|---|
| Llama 3.1 | 8B/70B | Best general chat | ollama run llama3.1:8b |
| Mistral | 7B | Fast, efficient | ollama run mistral |
| Gemma 2 | 9B/27B | Google quality | ollama run gemma2:9b |
| Phi-3 | 3.8B/14B | Small but capable | ollama run phi3:14b |
Reasoning Models
| Model | Size | Strength | Command |
|---|---|---|---|
| Qwen2.5 | 7B/72B | Math & logic | ollama run qwen2.5:7b |
| Phi-3 Medium | 14B | Analytical tasks | ollama run phi3:14b |
| Mixtral | 8x7B | Expert mixture | ollama run mixtral:8x7b |
Specialized Models
| Model | Use Case | Command |
|---|---|---|
| LLaVA | Vision + text | ollama run llava |
| Nomic-Embed | Embeddings | ollama run nomic-embed-text |
| Whisper | Speech-to-text | Via whisper.cpp |
Hardware Requirements
| Model Size | RAM Needed | GPU VRAM | Best For |
|---|---|---|---|
| 3B | 4GB | 4GB | Laptops |
| 7B | 8GB | 8GB | Desktop |
| 13B | 16GB | 16GB | Workstation |
| 34B | 32GB | 24GB | Pro GPU |
| 70B | 64GB | 48GB | Server |
Model Selection Guide
Need coding help?
→ Small project: qwen2.5-coder:7b
→ Complex code: deepseek-coder-v2:16b
Need general chat?
→ Best quality: llama3.1:8b (or 70b if you have the hardware)
→ Fastest: mistral:7b
Need reasoning?
→ Math/logic: qwen2.5:7b
→ Analysis: phi3:14b
Limited hardware?
→ phi3:3.8b or gemma2:2bFAQ
Q: Which model is closest to GPT-4? A: Llama 3.1 70B or Qwen2.5 72B are the closest in general capability. For coding, Qwen2.5-Coder 32B rivals GPT-4 on benchmarks.
Q: Can I use these with Claude Code?
A: Yes, run Ollama as a local server and point Claude Code to http://localhost:11434 as a custom endpoint.
Q: How much disk space do models need? A: Roughly 1GB per billion parameters in Q4 quantization. A 7B model is ~4GB, 70B is ~40GB.