Best Models to Run OpenClaw Locally for a Law Office
Law offices have two competing priorities when running OpenClaw: accuracy on nuanced legal tasks and strict data confidentiality. The right model depends on which constraint is harder. This guide covers the practical options, sourced from a r/openclaw thread with 147 upvotes.
The short answer
If client data can go through an external API: Claude Sonnet 5 or Opus 5.5 (Anthropic API) — best accuracy, easiest setup.
If data must never leave your network: Llama 3 70B or Llama 3.3 70B via Ollama — fully air-gapped, competitive quality.
Option 1 — Claude Sonnet via the API (recommended for most firms)
Claude Sonnet (currently Sonnet 5) is the sweet spot for legal document work: it handles long context windows well, follows complex instructions reliably, and costs $2/$10 per million input/output tokens for Sonnet 5. For typical OpenClaw tasks — summarising deposition transcripts, drafting correspondence, reviewing contract clauses — Sonnet's accuracy is hard to beat at this price point.
Setup is a single environment variable:
ANTHROPIC_API_KEY=sk-ant-...
Then set agents.defaults.model.primary to "anthropic/claude-sonnet-5" (or run openclaw configure). Your data transits Anthropic's API but is not used for training under the standard API terms.
Option 2 — Claude Opus for high-stakes work
If the firm needs the strongest possible reasoning — complex multi-party contracts, privilege review, nuanced legal research — Claude Opus 5.5 is the step up. At $4/$20 per million tokens it costs about twice as much as Sonnet 5, so reserve it for tasks where errors have real consequences. Most firms use Sonnet for routine tasks and Opus only for final review passes.
Option 3 — Llama 3 70B locally via Ollama (air-gapped)
For firms with strict data-sovereignty requirements — where client data must never transit a third-party server — running a local model is the only compliant option. Llama 3 70B (Meta) and Llama 3.3 70B are both strong performers on legal reasoning benchmarks and run on a single high-end workstation or small server with 48 GB+ VRAM.
Install Ollama, pull the model, and point OpenClaw at the local endpoint:
ollama pull llama3.3:70b
// ~/.openclaw/openclaw.json — OpenClaw talks to Ollama's native API (default http://127.0.0.1:11434).
// Don't point it at the /v1 OpenAI-compatible URL; that breaks tool calling.
{
agents: { defaults: { model: { primary: "ollama/llama3.3:70b" } } }
}
Or run openclaw onboard and choose Ollama; it discovers local models for you. See OpenClaw's Ollama provider docs.
Throughput is slower than API calls, but for a small law office processing documents in batch, this is usually acceptable. Quality is noticeably below Claude Opus on very complex reasoning but solid for document drafting, summarisation, and extraction tasks.
What the r/openclaw community recommends
The consensus in the thread: start with Claude Sonnet via API to validate your OpenClaw workflows, then evaluate whether a local model is needed. Many firms discover that Anthropic's data handling terms are acceptable after review, and the API option is far simpler to maintain long-term.
One highly-upvoted comment noted that OpenClaw's real value in a law office is cross-app automation — not just model quality. Tasks like "email this deposition PDF to the calendar bot and schedule prep time" or "monitor this Google Sheet for new client intake forms and draft welcome emails" are where OpenClaw outperforms a simple chat interface regardless of which model is underneath.
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Official sources
Checked against the OpenClaw docs, release 2026.9.6. If anything here disagrees with these pages, trust the vendor docs.