The best model for OpenClaw: what we run, and the default trap

Agent bills are input bills

The economics of running an agent are not the economics of chatting with one. OpenClaw’s own docs describe how context works: the system prompt is rebuilt and sent every run — rules, tool schemas, skills, workspace files — plus the conversation so far, plus every tool result. Output is a few sentences; input is everything. We measured this on our own pods: the spend curve tracks input and cached input almost entirely, so the number that decides your bill is the input price per million tokens, and the cached-input price right behind it.

The default trap (we paid for this lesson)

Every setup path OpenClaw offers defaults to flagship-class models. A ChatGPT/Codex sign-in selects openai/gpt-5.6-sol. A plain OpenAI API key sets openai/gpt-5.6 — which resolves to Sol. And OpenRouter onboarding defaults to openrouter/auto, a meta-router that picks from dozens of models and bills you at whatever it picked. In July we watched one of our own pods burn through $10 of credit in about seven days of light use — auto-routing had settled on Sol, at $5 per million input tokens, for messages that a model a fiftieth of the price could have handled. If you pay per token, the first configuration change worth making is a concrete model choice:

openclaw models set openai/gpt-5.6-luna   # or your pick
# or in ~/.openclaw/openclaw.json:
# { agents: { defaults: { model: { primary: "openai/gpt-5.6-luna" } } } }

What the tiers cost right now

OpenRouter’s listed prices as of early August 2026, per million tokens (input / output / cached input):

• GPT-5.6 Sol (flagship): $5 / $30 / $0.50
• GPT-5.6 Luna (efficient tier): $0.10 / $0.60 / $0.01 — the listing currently carries a 50%-off flag, so treat the exact figure as promotional
• GPT-5-mini (last generation): $0.25 / $2 / $0.025
• Claude Sonnet 5 (Anthropic mid-tier): $2 / $10 / $0.20

Notice the odd one out: the current-generation efficient model is cheaper than the previous generation’s small model, and roughly fifty times cheaper than the flagship on the input side — the side agents actually consume.

Why the cheapest model is the wrong goal

There is a floor, and OpenClaw’s security docs are the ones who set it: smaller and cheaper models are more susceptible to tool misuse and instruction hijacking under adversarial prompts, and the official recommendation is to use a latest-generation, best-tier model for any bot that can run tools or touch files. Our own operational version of the same lesson: last-generation small models fumble multi-step setup work that current-generation efficient models handle fine. That’s why the practical sweet spot for a per-token agent is the newest efficient tier — not the oldest cheap one — and why we default our own managed OpenClaw pods to exactly that, with the flagship a config line away when a task deserves it. OpenClaw also supports a fallbacks list per agent, so you can pin the efficient model as primary and let the harness fail over rather than silently upgrade.

The subscription exception

All of this math inverts if your ChatGPT subscription powers the agent: flat-rate plans absorb the flagship’s token prices, which is exactly why subscription sign-in defaults to Sol and should stay there. The per-token discipline above is for API-key and OpenRouter setups — the ones where every million tokens has a price tag.