If you've been running OpenClaw on Claude Opus 4.6 or Sonnet, you may have recently hit a wall. Anthropic has restricted OpenClaw access for a subset of users — and OpenAI's business plan quotas have become impractical for high-volume agentic use. The community on PricePerToken and the OpenClaw Discord has been scrambling to find replacements.
This isn't just "which model is better for chat." Agentic workloads are different — tool calling, multi-turn context management, cron-triggered autonomous tasks, and consistent instruction-following matter far more than benchmark scores. A model that aces MMLU can still be completely useless for running a heartbeat agent.
Here's the honest breakdown based on real community testing, not benchmarks.
Context: This post covers the model switching situation as of late April 2026. Anthropic's restriction appears to be usage-pattern-based, not account-level. If your Claude access is still working, keep using it — it remains the best option for complex reasoning. This guide is for when you need a fallback.
What Makes a Model Good for OpenClaw (Not Just Chat)
Before the model reviews, the criteria that actually matter for agentic workloads:
- Tool-calling reliability — Does it consistently call tools with correct arguments? Does it know when NOT to call a tool?
- Instruction-following — Does it respect SOUL.md and memory file constraints? Does it deviate from defined behavior?
- Multi-turn coherence — Does context degrade over a long conversation? Does it lose track of prior tool results?
- Cron/heartbeat tasks — Can it handle autonomous background tasks without going off-script?
- Cost efficiency — API pricing matters when you're running dozens of turns per hour autonomously
The Alternatives: Community Verdicts
The community consensus replacement for Claude in agentic workflows. PricePerToken rankings as of Apr 23 show Kimi K2.5 in the top spot by vote, but in practice, Minimax M2.7 is the most commonly reported successful Claude replacement for actual OpenClaw power users. Multiple users report it handling nit cron tasks that MiMo and GPT failed on. Tool calling is reliable. Instruction following is solid.
Community-ranked #1 on PricePerToken's OpenClaw leaderboard as of Apr 23. Strong agentic performance, good context handling. Available via Moonshot API and OpenRouter. One caveat: the community initially had negative reviews of earlier Kimi versions — K2.5 is substantially better than K2 or K1. If you tried Kimi before and gave up, worth re-testing on K2.5 specifically.
Several users report Opus/GPT-level quality in many tasks, and it's been popular since launch. The problem is the credit/billing system: Token Plan credits count against everything — session history, SOUL.md content, tool outputs, cache. Heavy OpenClaw users report burning through a month's allocation in a single day after loading two session contexts. The model performance is good; the economics for agentic use are broken unless you're using it for lightweight tasks.
Community verdict is consistent: GLM 5.x is bad for agentic tasks and automation. Reports of flooding Telegram with code dumps instead of replies, erratic tool calling, and poor instruction-following on structured prompts. May be fine for simple Q&A; for anything that requires reliable autonomous behavior, skip it.
OpenClaw v2026.4.22 added native xAI support (grok-imagine image gen, 6 TTS voices, grok-stt). The community feedback on Grok for agentic tasks is thin — most reports are mixed or negative, and many users skipped it. May be worth testing for specific use cases (especially image generation, where grok-imagine is now the default option). For core reasoning and tool use, Minimax or Kimi are safer bets.
The Claude restriction situation is exactly why routing routine tasks to local models matters. Qwen 3.5 9B via Ollama handles summarization, drafting, basic Q&A, and heartbeat log entries well — at $0/request. Reserve your API quota for complex reasoning and multi-step tool chains. If you're not running local models yet, this is the most resilient long-term strategy regardless of which cloud provider you're using.
Switching Models in OpenClaw
Model switching is non-destructive — you can change the default without touching your SOUL.md, memory files, or skill stack.
Option 1: Change default model in openclaw.json
"model": "minimax/minimax-m2.7"
Option 2: Switch live from chat (v2026.4.22+)
/models add moonshot/kimi-k2.5
Option 3: Route by task type (recommended)
"model": "minimax/minimax-m2.7",
"heartbeat": { "model": "ollama/qwen3.5:9b" }
The Resilient Strategy: Don't Rely on One Provider
The Claude restriction situation illustrates a core risk: if your OpenClaw deployment is 100% dependent on one model provider, any access change — restrictions, price hikes, quota cuts, outages — breaks your agent.
The most resilient setup:
- Primary model: Claude Sonnet or Minimax M2.7 for complex reasoning
- Fallback model: Kimi K2.5 via OpenRouter (available even when direct API access fails)
- Local model: Qwen 3.5 9B via Ollama for all routine tasks (heartbeat, drafting, summarization)
This setup costs roughly the same as running Claude-only — because 60–70% of your traffic moves to the free local tier — while making you immune to any single provider's access decisions.
OpenRouter as a hedge: openrouter/moonshot/kimi-k2-5 routes through OpenRouter's infrastructure. Even if direct Moonshot API access degrades, OpenRouter typically maintains routing continuity. Worth having as a configured fallback even if you don't use it as primary.
Bottom Line
If your Claude access was restricted: start with Minimax M2.7. It's the community's most battle-tested replacement for agentic workloads. Kimi K2.5 is the #1 ranked alternative by community vote and worth testing in parallel. Avoid GLM 5.x for anything autonomous.
If your Claude access is still working: set up Ollama + local models now, before you need them. The time to build the fallback is when you don't need it, not when you do.
Want Your Model Routing Set Up Properly?
ClawReady configures multi-provider model routing as part of every setup — primary cloud model, fallback, and local model for routine tasks. You get resilience built in from day one, not after a provider change breaks your agent.
See Setup Packages →