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Model Recommendations

Best Ollama Local Models for OpenClaw in 2026 (For Tool Calling / Agent Tasks)

2026-02-03 OpenClaw Community

OpenClaw, as an Agent framework, has very high requirements for function/tool calling stability, long context handling, and avoiding loops or hallucinations. Small models (<14B) are prone to issues. The community consensus is to start with at least 14B–32B, with 32B+ being more reliable.

Top Recommended Ollama Models Ranking (2026 Community Consensus)

  1. Qwen3 Series / Qwen3-Coder (Top Pick)

    • qwen3-coder:32b or qwen3:32b-instruct
    • Why is it the best? Extremely stable tool calling (rarely hallucinates calls or forgets parameters), top-tier performance, outstanding Agent tasks, highest price-performance ratio.
    • Hardware Requirements: 24–32GB VRAM (using q4/q5 quantization)
    • Pull Command: 
      ollama pull qwen3-coder:32b
# Or larger version: ollama pull qwen3:72b-instruct-q4_K_M (Requires 48GB+ VRAM)

  2. GLM-4.7-Flash / GLM-4.7 Series

    • One of the strongest in the 30B class, very precise tool calling (many find it more obedient than Qwen of the same class).
    • Especially suitable for coding + system operation tasks.
    • Downside: Occasionally gets slightly lost in ultra-long conversations (varies by user).
    • Pull: ollama pull glm-4.7-flash

  3. GPT-OSS Series

    • gpt-oss:20b / gpt-oss:120b (Use larger version if hardware permits)
    • Designed specifically for Agent tasks, clean tool calling, strong reasoning capabilities.
    • Tested 20B version is already very stable, 120B is top-tier but resource-intensive.
    • Pull: ollama pull gpt-oss:20b (or check for latest tag)

  4. DeepSeek-R1 / DeepSeek-Coder-V2

    • Extremely strong reasoning and coding, excellent tool usage.
    • Suitable for tasks requiring significant logical judgment.
    • Pull: ollama pull deepseek-r1:32b or relevant deepseek-coder variants

  5. Llama 3.3:70b (or Llama 3.2/3.1 Tool-Enhanced Versions)

    • High versatility, Meta's latest SOTA level, good tool support.
    • A safe choice if you have strong hardware (48GB+ VRAM).
    • Pull: ollama pull llama3.3:70b

Quick Selection Table (Based on Your Hardware)

Your VRAMRecommended Entry ModelExpected PerformanceNotes
8–16GBqwen3-coder:14b or glm-4.7-flashBarely Usable ~ DecentSmall models loop easily, need patient prompt tuning
24–32GBqwen3-coder:32b / glm-4.7Highly RecommendedSweet spot for most people
40GB+qwen3:72b / gpt-oss:120b / llama3.3:70bTop TierClose to cloud-based strong models
Mac Studio / M1 Max+Qwen Series or GLM (Apple Silicon Optimized)ExcellentAvoid overly large models

Practical Tips (For More Stable Local Models)

  • Temperature: Set to 0 or 0.1–0.2 to avoid hallucinations.
  • Context Length: OpenClaw often uses ultra-long prompts, prefer models with 32k+ context support (Qwen3, GLM-4.7 support is excellent).
  • Tool Parameter Issues: Check ~/.openclaw/workspace/TOOLS.md, some models require manual keyword changes like "cmd" β†’ "command" (common bug).
  • Slow Speed β†’ Use q4_K_M / q5_K_M quantized versions, small precision loss but much faster.
  • Most Stable Combo: Main model qwen3-coder:32b, backup glm-4.7-flash. This dual-model switching covers almost all scenarios.

Currently, the most common "Local God Team" in the community is qwen3-coder + glm-4.7-flash, which has almost no blind spots.