Best Model to Run OpenClaw: A Complete Guide for 2026

February 27, 2026

Choosing the right LLM for your OpenClaw agent isn't just a preference — it directly determines how reliably your agent calls tools, how well it reasons through multi-step tasks, and how fast it burns through your budget. We ran every major model through real OpenClaw workloads and benchmarked what actually matters: tool calling reliability, sustained agentic performance, and cost per task.

Here's what we found.

What Makes a Model Good for OpenClaw?

OpenClaw agents aren't chatbots. They browse the web, run shell commands, manage files, spawn sub-agents, and chain together dozens of tool calls in a single session. That puts very specific demands on the underlying LLM:

1. Reliable Tool Calling — The model must consistently generate correct function call schemas and handle multiple tool calls per turn. This is the single most important capability. Models are evaluated on benchmarks like BFCL (Berkeley Function Calling Leaderboard) and IFBench.

2. Strong Reasoning — Agents plan multi-step workflows, decompose complex tasks, and recover when steps fail. Chain-of-thought and extended thinking modes dramatically improve agentic performance.

3. Long Context Window — Agents accumulate tool results, conversation history, and environment state over long sessions. A large context window (200K+ tokens) prevents context loss during extended operations.

4. Instruction Following — Your agent's personality is defined in its SOUL.md file. The model must follow those system instructions precisely without drifting over hundreds of tool calls.

5. Speed — For agents on Telegram, Discord, or Slack, response latency directly impacts user experience.

The Best: Claude Opus 4.6

Claude Opus 4.6 is the most capable model you can run with OpenClaw today. No caveats, no asterisks.

Why It Wins

• Terminal-Bench 2.0: Highest score among all frontier models on real-world agentic coding tasks
• Humanity's Last Exam: Leads all frontier models on complex multidisciplinary reasoning
• SWE-Bench Verified: 80%+ on real-world software engineering — the first model family to cross that threshold
• 76% on MRCR v2 (needle-in-haystack retrieval) vs. 18.5% for Sonnet 4.5 — a completely different capability class for long-context work
• 200K standard context window, extendable to 1M tokens — your agent can maintain coherent state across extremely long sessions
• 128K output token support — no truncation on complex, multi-part responses
• Adaptive thinking — the model dynamically decides when and how deeply to reason, optimizing cost on simpler sub-tasks

Pricing

What It Costs in Practice

A typical OpenClaw session — browsing the web, executing a few commands, summarizing results — runs about 5K-15K input tokens and 2K-5K output tokens per turn. At standard pricing, that's roughly $0.08-$0.15 per interaction. With prompt caching enabled (which OpenClaw leverages for system prompts and SOUL.md), input costs drop by up to 90%, bringing per-interaction cost closer to $0.05-$0.10.

For a moderately active agent handling 50-100 interactions per day, expect roughly $3-$10/day depending on task complexity.

The Verdict

If you need an agent that reliably handles complex, multi-step workflows — researching topics, writing and debugging code, managing files, coordinating sub-agents — Opus 4.6 is the model to choose. It makes fewer mistakes, recovers better from errors, and maintains coherent behavior across longer sessions than anything else available.

Best Economical Alternative: Kimi K2.5

Kimi K2.5 by Moonshot AI is our default model on OpenClaw for good reason. It delivers frontier-class agentic performance at a fraction of the cost.

Why It's the Smart Budget Pick

• Mixture-of-Experts architecture: 1 trillion total parameters, but only 32 billion active per inference — this is how they keep costs so low while maintaining quality
• Agent Swarm technology: Can spawn up to 100 sub-agents executing parallel workflows across up to 1,500 tool calls — purpose-built for frameworks like OpenClaw
• Stable over 200-300 sequential tool calls without instruction drift — critical for long-running agent sessions
• 76.8% on SWE-Bench Verified — within striking distance of Opus
• 96.1% on AIME 2025 — outperforming nearly all proprietary models on mathematical reasoning
• 256K token context window — more than enough for most agent workflows
• Open-source weights available on Hugging Face — you can self-host if needed

Pricing

The Math That Matters

At OpenRouter pricing ($0.45/$2.25 per million tokens), Kimi K2.5 is roughly 11x cheaper on input and 11x cheaper on output than Opus 4.6.

That same moderately active agent running 50-100 interactions per day costs approximately $0.30-$1.00/day with Kimi K2.5 — comfortably within OpenClaw's default $10/month budget.

For users just getting started or running agents that handle routine tasks (scheduling, notifications, simple web lookups), Kimi K2.5 delivers 80-90% of the capability at roughly 10% of the cost.

Full Model Comparison

Here's how every model available on OpenClaw stacks up:

How to Choose

Choose Opus 4.6 if:

• Your agent handles complex, multi-step workflows
• Reliability matters more than cost (business-critical tasks)
• You need deep reasoning, code generation, or research capabilities
• Your agent manages sub-agents and needs strong coordination

Choose Kimi K2.5 if:

• You want the best bang for your buck
• Your agent runs routine to moderately complex tasks
• You're on the default $10/month budget
• You need stable long-running sessions with many tool calls

Choose Gemini 2.5 Flash or GPT-4o Mini if:

• You need the absolute cheapest option
• Your agent handles simple, repetitive tasks
• Speed matters more than depth of reasoning

Choose Claude Sonnet 4.5 if:

• You want a middle ground between Opus 4.6 and Kimi K2.5
• You need strong coding capabilities with moderate cost
• Your agent runs sustained operations (it's proven reliable over 30+ hour sessions)

Monthly Cost Estimates

For an agent handling ~75 interactions per day (a moderately active agent):

Our Recommendation

Start with Kimi K2.5. It's our default for a reason — it handles the vast majority of agent tasks well and keeps costs predictable. When you hit tasks that need deeper reasoning, more reliable tool chaining, or higher-stakes output, switch to Claude Opus 4.6.

The best setup is using both: Kimi K2.5 as your daily driver, with Opus 4.6 reserved for when you need an agent that simply doesn't make mistakes.

You can switch models at any time from your OpenClaw dashboard.