ARC-AGI-3 Just Reset the Scoreboard. Here's Which Model to Run in Your OpenClaw Agent Right Now.

ARC-AGI-3: Why the Benchmark Drop Matters to You

ARC-AGI-3 was released in late March 2026, and the AI community's reaction was immediate: the leaderboard that everyone had been tracking just got flipped on its head. ARC-AGI-3 is a reasoning benchmark designed to test fluid intelligence — novel problems that can't be memorized from training data. It's closer to what an AI agent actually does all day when you leave it running.

Most public benchmarks favor recall and pattern-matching. ARC-AGI-3 doesn't. The result? A handful of models that scored impressively on MMLU or HumanEval look significantly weaker when required to reason through genuinely novel situations. And a few models — including some you might not have been using — have jumped.

The difference between 72% and 41% sounds abstract. Run both models as an autonomous agent for a week and it becomes visceral: one finishes tasks, one hallucinates tool calls. For OpenClaw users — who leave agents running cron jobs, sub-agent chains, and overnight research tasks — this matters directly.

How OpenClaw API Providers Actually Work

OpenClaw isn't locked to any single model. It's a runtime — the same agent framework can run against Claude, OpenAI, Gemini, Grok, or a local Ollama instance. The model is configured per-session or set as a default, and you can override it per-task.

This architectural choice is underappreciated. You can run Claude for deep reasoning tasks, Grok for anything requiring real-time data, and a local Llama for bulk processing you don't want billed. The agent logic stays identical — only the inference backend changes.

{
  "model": "anthropic/claude-sonnet-4-6",
  "providers": {
    "anthropic": {
      "apiKey": "sk-ant-..."
    },
    "openai": {
      "apiKey": "sk-..."
    },
    "google": {
      "apiKey": "AIza..."
    },
    "xai": {
      "apiKey": "xai-..."
    }
  }
}

The model field sets your default — what every cron job and chat session uses unless you override it. Overrides can be set per-session via /model or per-job by specifying model in the cron payload.

Model-by-Model Breakdown: What OpenClaw Users Actually Experience

Benchmarks give you a number. This section gives you what happens when you leave a model running autonomously for days.

Real Config Examples: Wiring It All Up

Here's how to configure OpenClaw to use different models for different jobs — the multi-provider setup that maximizes both capability and cost efficiency.

1. Set your default model (Claude for daily use)

{
  "model": "anthropic/claude-sonnet-4-6"
}

2. Per-job model override in cron

{
  "schedule": { "kind": "cron", "expr": "0 7 * * *", "tz": "Europe/Belgrade" },
  "sessionTarget": "isolated",
  "payload": {
    "kind": "agentTurn",
    "model": "xai/grok-3",
    "message": "Check X/Twitter for crypto + AI market movers in the last 24h. Write a 5-bullet brief. Send to Telegram."
  },
  "delivery": { "mode": "announce" }
}

3. Gemini for large document processing

{
  "schedule": { "kind": "cron", "expr": "0 22 * * 0" },
  "sessionTarget": "isolated",
  "payload": {
    "kind": "agentTurn",
    "model": "google/gemini-2.5-pro",
    "message": "Read ~/reports/weekly-data-export.csv (200K rows). Identify the top 10 anomalies. Format as a table and send to Telegram."
  }
}

4. Spawn a sub-agent with a different model

sessions_spawn({
  task: "Classify these 500 emails as spam/ham. Return JSON array.",
  model: "ollama/llama3.3",
  mode: "run"
})

What the Community Is Saying

The Hybrid Setup That Actually Wins

Don't pick one model. That's the wrong frame. The right architecture is a model stack — each layer optimized for its job.

With this stack, you get frontier reasoning where it matters, real-time data when you need it, a cost-effective path for large context, and a private local fallback for sensitive work. Monthly API cost for a heavy-use setup: roughly $15–40. Compare that to $200/month for a single ChatGPT Pro subscription that doesn't let you run cron jobs, customize behavior, or wire in your own tools.

Final Recommendations: By Use Case

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