Model Guide

This reference covers common Ollama models and selection guidance.

Popular Models

Chat/General Models

Model	Params	Best For	Notes
`qwen3:4b`	4B	Fast tasks, quick answers	Thinking-enabled, very fast
`llama3.1:8b`	8B	General chat, reasoning	Good all-rounder
`gemma3:12b`	12.2B	Creative, design tasks	Google model, good quality
`phi4-reasoning:latest`	14.7B	Complex reasoning	Thinking-enabled
`mistral-small3.1:latest`	24B	Technical tasks	May need CPU offload
`deepseek-r1:8b`	8.2B	Deep reasoning	Thinking-enabled, chain-of-thought

Coding Models

Model	Params	Best For	Notes
`qwen2.5-coder:7b`	7.6B	Code generation, review	Best local coding model
`codellama:7b`	7B	Code completion	Meta's code model
`deepseek-coder:6.7b`	6.7B	Code tasks	Good alternative

Embedding Models

Model	Params	Dimensions	Notes
`bge-m3:latest`	567M	1024	Multilingual, good quality
`nomic-embed-text`	137M	768	Fast, English-focused
`mxbai-embed-large`	335M	1024	High quality embeddings

Model Selection Guide

By Task Type

Quick questions: qwen3:4b (fastest)
General chat: llama3.1:8b
Coding: qwen2.5-coder:7b
Complex reasoning: phi4-reasoning or deepseek-r1:8b
Creative/design: gemma3:12b
Embeddings: bge-m3:latest

By Speed vs Quality

Fastest ←──────────────────────────────→ Best Quality
qwen3:4b → llama3.1:8b → gemma3:12b → mistral-small3.1

Tool Use Support

Models with good tool/function calling support:

✅ qwen2.5-coder:7b - Excellent
✅ qwen3:4b - Good
✅ llama3.1:8b - Basic
✅ mistral models - Good
⚠️ Others - May not support tools natively

OpenClaw Integration

To use Ollama models in OpenClaw sub-agents, use these model paths:

ollama/qwen3:4b
ollama/llama3.1:8b
ollama/qwen2.5-coder:7b
ollama/gemma3:12b
ollama/mistral-small3.1:latest
ollama/phi4-reasoning:latest
ollama/deepseek-r1:8b

Auth Profile Required

OpenClaw requires an auth profile even for Ollama (no actual auth needed). Add to auth-profiles.json:

"ollama:default": {
  "type": "api_key",
  "provider": "ollama",
  "key": "ollama"
}

Hardware Considerations

8GB VRAM: Can run models up to ~13B comfortably
16GB VRAM: Can run most models including 24B+
CPU offload: Ollama automatically offloads to CPU/RAM for larger models
Larger models may be slower due to partial CPU inference

Installing Models

# Pull a model
ollama pull llama3.1:8b

# Or via the skill script
python3 scripts/ollama.py pull llama3.1:8b

# List installed models
python3 scripts/ollama.py list

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