OpenClaw Without Claude: 5 Setups Ranked by Cost and Quality

What This Post Is (and Isn't)

This is not an argument against Claude. Claude Opus 4.6 and Sonnet 4.6 are still top-tier models and they still run plenty of agents on OpenClaw. The question this post answers is different: if you have already decided to run some or all of your OpenClaw workload on something other than Claude, which setups are worth your time?

The source material is Reddit. The post-Claude exodus megathread on r/openclaw is the empirical data: hundreds of builders posting token counts, bills, config files, and before/after agent traces. That thread — plus the follow-up discussions through the first two weeks of April — is where the rankings below come from. We did not run fresh benchmarks. We read what people who are already doing this work said about their setups.

A note on pricing: every number below is approximate for April 2026 and based on a moderate workload of roughly 2M output tokens per day. Confirm the exact rate on the provider's pricing page before committing. Pricing changes faster than blog posts.

1. GLM-5.1 — The Closest Thing to Opus 4.6

Best for: Single-model builders who want one cheap, capable default that handles most agent work without needing a second model for hard tasks.

ollama launch openclaw --model glm-5.1:cloud

# Or edit config.yaml directly:
#   model: glm-5.1:cloud
#   provider: ollama

Gotchas: On very large diffs (>4K lines) community reports say context fidelity drops faster than Opus. And the free promo tier rate-limits aggressively — for real agent workloads use the Coding Plan or hit the API directly.

Repo config: awesome-openclaw-agents/configs/glm-5.1

2. Advisor Hybrid — Opus Plans, GLM Executes

Best for: Teams migrating from Claude who still want Opus-quality planning on their hardest agents but cannot stomach the bill on execution.

The pattern is adapted from Anthropic's own advisor docs: one model is given the job of reading the task, producing a plan, and calling the executor with smaller scoped subtasks. The r/openclaw adaptation uses Opus 4.6 as the advisor and GLM-5.1 as the executor. The advisor call costs more per token but fires rarely. The executor fires on every step.

advisor:
  model: claude-opus-4-6
  provider: anthropic
  role: plan, review, error triage

executor:
  model: glm-5.1:cloud
  provider: ollama
  role: code, tool calls, iteration

router:
  escalate_on: [plan_required, review_needed, error]
  default: executor

Gotchas: The router logic is where teams get in trouble. Too eager to escalate and you pay Opus rates for nothing; too reluctant and you get GLM-level planning on hard problems. Start with the sample router in the repo and tune from there.

Repo config: awesome-openclaw-agents/configs/advisor-hybrid

3. Minimax M2.7 — Open Weights, Real Quality

Best for: Teams that want open-weight flexibility, self-hosting optionality, or a backup model that isn't tied to a single vendor's pricing page.

Gotchas: The commercial license has caveats — read it before deploying for a paying customer. Tool-call reliability is also slightly behind GLM-5.1 according to the megathread, so agents with heavy tool chains need more error-handling logic than you'd use with Opus.

Repo config: awesome-openclaw-agents/configs/minimax-m2.7

4. GPT-5.4 — The Config-Sensitive Sweet Spot

Best for: Builders who are already on the OpenAI stack and want to stay there, and for agents that benefit from GPT-5.4's strong reasoning on complex planning tasks.

model: gpt-5.4
provider: openai
params:
  thinking: high
  fastmode: true
  tool_format: "v2"   # required — v1 schemas silently degrade

Gotchas: Two things trip people up. First, the tool-call schema format changed in 5.4 — old v1 schemas still work but quality drops. Second, thinking=high costs more per call but is the default the community recommends for agent work. If you run it on thinking=low to save money you'll get the bad-agent experience the megathread keeps describing.

Repo config: awesome-openclaw-agents/configs/gpt-5.4

5. Local Gemma 4 + Ollama — The Free Tier

Best for: Hobby builders, privacy-sensitive workloads, offline laptops, and anyone whose agents don't need frontier-model quality to do the job.

ollama pull gemma4:9b
ollama launch openclaw --model gemma4:9b

# config.yaml
#   model: gemma4:9b
#   provider: ollama
#   host: http://localhost:11434

Gotchas: Tool-call reliability on local Gemma 4 is noticeably lower than the hosted frontier models. Agents with more than 4-5 tools often need a stricter system prompt or a tool-filtering wrapper. And context length on the 9B variant is tight — don't try to feed a 50K-token codebase into it.

Repo config: awesome-openclaw-agents/configs/ollama

Side-by-Side: All Five Setups

One table, five setups. Pricing is approximate for April 2026 at ~2M output tokens/day — always confirm on the provider pricing page.

Which Should You Pick?

If you want a short flowchart instead of re-reading the whole post, use this.

Start
  │
  ├─ Budget = $0?
  │    └─ Yes → Gemma 4 + Ollama (local)
  │
  ├─ Already on OpenAI stack and staying?
  │    └─ Yes → GPT-5.4 (thinking=high, fastmode=true)
  │
  ├─ Migrating from Claude but want Opus on planning?
  │    └─ Yes → Advisor Hybrid (Opus + GLM-5.1)
  │
  ├─ Need open weights / self-hosting option?
  │    └─ Yes → Minimax M2.7
  │
  └─ Just want one cheap default that works everywhere?
       └─ Yes → GLM-5.1 (start here if unsure)

The honest default for most r/openclaw builders in April 2026 is GLM-5.1. It is the cheapest of the frontier-competitive options, the setup is 10 minutes, and the community feedback is the most consistent. Everything else in this list is the right answer for a specific constraint, not the general case.

Get the Configs, Build an Agent

All five setups above have a ready-made config in the awesome-openclaw-agents repo. Clone the config, drop in your API key, point OpenClaw at it. If you hit issues during migration — especially the tool-call and context-window gotchas covered above — the TROUBLESHOOTING doc in the same repo has the fixes the community posted during the megathread.

Frequently Asked Questions