Because OpenClaw performs complex, multi‑step tasks that require high reasoning and heavy context tracking, choosing the right “brain” (LLM) is the most critical decision for any user. Based on community testing and technical benchmarks, here is the breakdown of the best models for OpenClaw in 2026.
The “Gold Standard”: Cloud‑Based Models
Claude 4.5 Opus (Anthropic)
Widely considered best‑in‑class for OpenClaw. Superior reasoning and tool‑calling reliability—rarely fumbles complex “Skills” like Home Assistant integration or multi‑file code edits. Trade‑off: expensive; heavy users report ~$50 in 48 hours during intensive tasks.
Claude 4 Sonnet
The recommended “Daily Driver.” Delivers ~90% of Opus capability at much lower cost ($3/$15 per million tokens). Fast for real‑time chat, smart enough for most automated workflows.
Gemini 3 Pro
The king of context. With a 1M+ token window, ideal when OpenClaw must analyze entire codebases or long histories without the “forgetfulness” that plagues smaller models.
GPT‑4o / GPT‑5.2
Excellent all‑rounders with high speed and widespread integration. GPT‑4o‑mini is often the best “budget cloud” option for high‑volume, simple tasks.
The Local Heavyweights: Privacy and Sovereignty
For those prioritizing data privacy or avoiding API fees, running locally via Ollama, llama.cpp, or vLLM is preferred. Community consensus suggests that size matters significantly for agentic tasks.
GPT‑OSS 120B
Often ranked “Best in Show” for local execution. Rivals cloud models in reasoning and handles complex schema requirements of OpenClaw’s skills. Requires significant VRAM (e.g., Mac Studio 128GB+ RAM or multi‑GPU setups).
Llama 3.3 70B
Fast and intelligent; follows instructions better than most open‑source alternatives. A sweet spot for users with ~48GB+ VRAM (dual RTX 3090/4090).
Qwen 2.5 72B / Qwen 3 (30B+)
Highly competent at tool‑calling. Smaller Qwen 14B/20B variants may struggle with long‑context “slop,” while 70B+ offers reliable task execution.
The “Budget” and Efficiency Tier
If you run mid‑range hardware (~24GB VRAM), choose carefully.
GLM 4.7 Flash
Polarizing but popular. Some users report amazing performance for its size; others find instruction‑tracking degrades in long sessions.
Nemotron 3 Nano (NVIDIA)
Recommended for massive context support (up to ~1M) and its tendency to degrade more gracefully than other small models when conversations get long.
Ministral 3‑8B / Gemma 3 27B
Solid for specialized, low‑latency tasks but may require highly specific prompting (“banana bribes”) for complex system‑level writes.
Critical Considerations for OpenClaw Users
The “Context Wall”
OpenClaw is context‑hungry. Every time the agent inspects email or files, data feeds back into the prompt. Smaller models (<30B) often suffer context degradation—ignoring instructions or hallucinating after a few turns. If your agent starts looping, switch to a larger model or a provider with ultra‑long context (e.g., Gemini).
Security and “Spicy” Permissions
OpenClaw operates at elevated access levels. Giving shell and file‑system permissions can turn prompt injection into a system compromise.
Recommendation: Always run OpenClaw in an isolated environment (Docker, dedicated VM, or separate VLAN).
Local Advantage: Running locally keeps sensitive data (API keys, emails, files) on your machine.
The Verdict
- Professionals: Use Claude 4 Sonnet as the daily driver; switch to Opus for complex coding sprints.
- Power Users / Tinkerers: Go local with GPT‑OSS 120B or Llama 3.3 70B on high‑end hardware.
- High‑Volume / Simple Tasks: Choose GPT‑4o‑mini or DeepSeek V3 to keep costs near zero.
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