Best AI Models for OpenClaw in 2026 — Which LLM to Use
A curated guide to the best AI models for OpenClaw in 2026. Compare Claude, GPT-4, Gemini, DeepSeek, and local models for different tasks and budgets.
· Updated March 2026 · 10 min read The model you run in OpenClaw has a huge impact on quality, cost, and speed. Here's a practical guide to what works best in 2026 — for primary agents and for specialized cron tasks.
Top Models for Primary Agent Use
Claude Opus 4.x (Anthropic)
The most capable model for complex reasoning, nuanced instructions, and multi-step task planning. Best for your main interactive agent where you want the highest quality responses. Most expensive per token.
openclaw config set llm.model claude-opus-4-6Claude Sonnet 4.x (Anthropic)
The best balance of capability and cost. Excellent at following complex SOUL.md instructions, managing sub-agents, and producing high-quality outputs. The recommended default for most operators.
openclaw config set llm.model claude-sonnet-4-6GPT-4o (OpenAI)
Strong across all task types. Particularly good at function calling and structured output. A solid alternative to Claude Sonnet when you want model diversity.
Gemini 2.0 Pro (Google)
Excellent for long-context tasks — handles very large documents and codebases well. Good for research and document analysis. Competitive pricing.
Best Budget Models for Cron Jobs
Claude Haiku 3.5 (Anthropic)
~20x cheaper than Sonnet. Handles simple tasks extremely well: status checks, brief summaries, data lookups, formatting. The go-to for high-frequency crons.
openclaw cron update hex-daily-brief --model claude-haiku-3-5Gemini 2.0 Flash (Google)
Very fast and extremely affordable. Best for content generation at scale, simple classification tasks, and anything where speed and cost matter most.
DeepSeek V3 (via OpenRouter)
Remarkable capability at low cost. Strong coding performance, good reasoning. Open weights mean you can run it locally via Ollama too.
Local Models via Ollama
Llama 3.3 70B
The best local model for general tasks. Requires 48GB+ RAM. Zero per-token cost — ideal for privacy-sensitive workflows or high-volume automations.
Llama 3.2 3B
Fast, lightweight, runs on 8GB RAM. Use for simple extraction, classification, or formatting tasks.
openclaw config set llm.provider ollama
openclaw config set llm.model llama3.2:3b
openclaw config set llm.baseUrl http://localhost:11434Recommended Multi-Model Setup
# Primary interactive agent:
default_model=anthropic/claude-opus-4-6
# Sub-agents (coding, analysis):
model: anthropic/claude-sonnet-4-6
# Simple crons:
model: claude-haiku-3-5
# Privacy-sensitive tasks:
model: ollama/llama3.3:70bThe multi-model approach cuts LLM costs by 60-80% without sacrificing quality where it matters. For the full model selection framework, The OpenClaw Playbook covers it in depth — $9.99.
Frequently Asked Questions
What is the default model in OpenClaw?
OpenClaw doesn't have a hardcoded default — you configure your LLM provider and model in openclaw.json or via the CLI. Most users start with Claude Sonnet or GPT-4o as their primary model.
Can I use different models for different tasks in OpenClaw?
Yes — you can specify a model per cron job, per agent, or per task. This is how you optimize costs: cheap models for simple monitoring tasks, powerful models for complex reasoning.
Is it worth running local models with Ollama in OpenClaw?
For privacy-sensitive tasks or high-frequency low-complexity jobs, yes. Local models cost nothing per request. The tradeoff is quality — for complex reasoning or nuanced content, cloud models still outperform.
Which model works best for coding tasks in OpenClaw?
Claude Sonnet 4.x and GPT-4o are the strongest for code generation and review. DeepSeek Coder V2 is excellent and very cost-effective. For automated code review crons, Claude Haiku handles most routine tasks at a fraction of the cost.
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