OpenClaw + Qwen 3.5: Best Local Model for AI Agents

Why Qwen 3.5 Stands Out for AI Agents

Most local models are trained for general chat. Qwen 3.5 was trained with a heavy emphasis on instruction following, structured output, and tool use. These are exactly the capabilities that matter for OpenClaw agents. When your agent reads a SOUL.md file with 15 rules and needs to follow every single one, Qwen 3.5 handles it more reliably than alternatives at the same parameter count.

Alibaba released Qwen 3.5 in early 2026 under the Apache 2.0 license, making it free for commercial use. The model family includes 7B, 14B, 32B, and 72B variants, all available through Ollama. The 7B variant is the sweet spot for most agent workloads: fast enough for real-time responses, capable enough to follow complex SOUL.md configurations, and small enough to run on a mid-range GPU or Apple Silicon Mac.

Benchmark Comparison: Qwen vs Llama vs Mistral vs DeepSeek

We tested Qwen 3.5, Llama 3.1, Mistral, and DeepSeek V3 across five agent-specific benchmarks using OpenClaw agents with identical SOUL.md configurations. All models were run through Ollama at their default quantization. Here are the results for the 7B-8B parameter class.

Larger Models: 14B and Above

At the 14B parameter level, Qwen 3.5 extends its lead further. The 14B variant reaches near-cloud-model quality for most agent tasks.

Notice that Qwen 3.5 32B matches Llama 3.1 70B on agent benchmarks while requiring half the VRAM and running 50% faster. This makes the 32B variant an excellent choice if you have a high-end GPU but want to avoid the resource requirements of a 70B model.

Install Qwen 3.5 via Ollama

Getting Qwen 3.5 running takes two commands. Ollama handles the model download, quantization, and serving automatically.

# Install Ollama (if not already installed)
curl -fsSL https://ollama.com/install.sh | sh

# Pull Qwen 3.5 (default: 7B variant, ~4.5 GB download)
ollama pull qwen3.5

# Pull larger variants if your hardware supports it
ollama pull qwen3.5:14b    # 14B variant (~9 GB)
ollama pull qwen3.5:32b    # 32B variant (~19 GB)

# Verify the model is available
ollama list

# Quick test to confirm it works
ollama run qwen3.5 "List 3 benefits of AI agents"

# Check the API endpoint is running
curl http://localhost:11434/api/tags

Connect OpenClaw to Qwen 3.5

Once Ollama is serving Qwen 3.5, point OpenClaw at the local endpoint. No API keys are needed.

# Add Qwen 3.5 as a model provider
openclaw models add qwen-local \
  --provider ollama \
  --endpoint http://localhost:11434 \
  --model qwen3.5

# Set it as the default model
openclaw models set-default qwen-local

# Test the connection
openclaw models test qwen-local
# Expected: "Model qwen-local is responding correctly"

You can also configure it directly in the config file for more granular control.

{
  "models": {
    "qwen-local": {
      "provider": "ollama",
      "endpoint": "http://localhost:11434",
      "model": "qwen3.5",
      "temperature": 0.7,
      "context_length": 32768,
      "timeout": 120
    },
    "qwen-14b": {
      "provider": "ollama",
      "endpoint": "http://localhost:11434",
      "model": "qwen3.5:14b",
      "temperature": 0.7,
      "context_length": 32768,
      "timeout": 180
    }
  },
  "default_model": "qwen-local"
}

SOUL.md Configuration Optimized for Qwen

Qwen 3.5 follows SOUL.md instructions exceptionally well, but there are specific patterns that maximize its performance. The key is clear structure, explicit formatting rules, and concise instructions. Qwen responds better to direct commands than vague guidelines.

# Research Analyst

## Identity
- Name: Researcher
- Role: Technical Research Analyst
- Model: qwen3.5 (via Ollama)

## Personality
- Data-driven and precise
- Presents findings in structured formats
- Cites sources and provides confidence levels

## Rules
- Always respond in JSON when asked for structured data
- Keep responses under 200 words for simple questions
- Use markdown tables for comparisons
- Never fabricate statistics or sources
- If unsure, state your confidence level explicitly
- Follow the output format specified in each request

## Output Format
- Use headers for sections
- Use bullet points for lists
- Use code blocks for technical content
- End every analysis with "Next Steps" section

## Skills
- browser: Research topics on the web
- file: Read and write analysis reports

Multi-Agent Team with Qwen

You can run an entire multi-agent team on Qwen 3.5. Here is a practical example with different Qwen variants assigned to different roles based on their computational needs.

# Content Team (All Local, All Qwen)

## Agents
- @researcher: Technical research and data gathering (qwen3.5:14b)
- @writer: Content drafting from research notes (qwen3.5:7b)
- @editor: Review, fact-check, and polish (qwen3.5:7b)
- @analyst: Data analysis and reporting (qwen3.5:14b)

## Workflow
1. @researcher gathers data on the assigned topic
2. @researcher passes findings to @writer
3. @writer drafts the article using research notes
4. @editor reviews the draft for quality and accuracy
5. @analyst generates performance projections

## Model Assignment
- Research and analysis tasks: qwen3.5:14b (needs reasoning depth)
- Writing and editing tasks: qwen3.5:7b (speed matters more)

Best Use Cases for Qwen 3.5 Agents

Qwen 3.5 excels at specific agent tasks. Here is where it performs best and where you might want to consider alternatives.

Memory and Context Window Considerations

Qwen 3.5 supports a 32K token context window, which is significantly larger than most local models. This matters for agents that need to maintain conversation history, process documents, or work with large SOUL.md configurations.

The 32K context window gives Qwen 3.5 a practical advantage for agent use cases. A typical SOUL.md file uses 500-1500 tokens. A single agent response uses 200-800 tokens. With 32K tokens available, your agent can maintain 15-20 conversation turns before context starts getting truncated. This is enough for most interactive agent workflows.

{
  "models": {
    "qwen-local": {
      "provider": "ollama",
      "endpoint": "http://localhost:11434",
      "model": "qwen3.5",
      "context_length": 32768,
      "max_tokens": 2048,
      "temperature": 0.7
    }
  },
  "session": {
    "max_history": 20,
    "summarize_after": 15,
    "clear_on_idle": 300
  }
}

Qwen Next 80: What to Expect

Alibaba has announced Qwen Next 80, an 80B parameter model expected in mid-2026. Based on early technical previews and leaked benchmarks, here is what it means for OpenClaw agent users.

The good news: when Qwen Next 80 drops, your OpenClaw configuration stays the same. Pull the new model through Ollama, update the model name in your config, and your agents automatically use the upgraded model. No SOUL.md changes, no agent reconfiguration.

Performance Optimization Tips

Getting the most out of Qwen 3.5 with OpenClaw requires tuning both the Ollama runtime and your agent configuration. Here are practical optimizations that make a measurable difference.

# Keep model loaded in memory (prevents cold start delay)
# Set OLLAMA_KEEP_ALIVE to -1 for always-on
export OLLAMA_KEEP_ALIVE=-1

# Increase GPU layers for faster inference (NVIDIA)
export OLLAMA_NUM_GPU=99

# Set number of parallel requests (if running multiple agents)
export OLLAMA_NUM_PARALLEL=4

# Increase context size at the Ollama level
export OLLAMA_CONTEXT_LENGTH=32768

# On macOS, add these to your shell profile (~/.zshrc)
echo 'export OLLAMA_KEEP_ALIVE=-1' >> ~/.zshrc
echo 'export OLLAMA_NUM_PARALLEL=4' >> ~/.zshrc

# Restart Ollama to apply
brew services restart ollama  # macOS
sudo systemctl restart ollama  # Linux

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Frequently Asked Questions