Best AI Models for OpenClaw in 2026

OpenClaw supports a wide range of AI models – from Anthropic’s Claude to Google’s Gemini to local models running on your own hardware. The model you choose affects response quality, speed, cost, and how well OpenClaw handles complex tasks like writing, coding, and autonomous decision-making.

This guide covers the best AI models for OpenClaw in 2026: what each one excels at, what it costs, and how to configure them.

How OpenClaw Uses AI Models

OpenClaw uses your configured AI model for every agent turn – every message you send, every heartbeat, every subagent task. You set a primary model and optional fallbacks. If the primary hits a rate limit or fails, OpenClaw automatically tries the next one.

The model choice matters differently across tasks:

• Conversation and daily use: Fast response, good reasoning, understands context well
• Writing and content: Long-form quality, follows instructions precisely
• Coding and automation: Accurate tool calls, handles complex multi-step tasks
• Heartbeats (background monitoring): Cheap and fast – you don’t need the best model for routine checks

OpenClaw lets you set different models for different purposes – a powerful model for your main session and a cheaper one for heartbeats. That alone can cut your API costs significantly.

The Best AI Models for OpenClaw in 2026

1. Claude Sonnet (Anthropic) – Best Overall

Claude Sonnet is the default model for OpenClaw and the best all-around choice for most users. It handles long conversations without losing context, follows complex instructions reliably, writes well, and executes tool calls (file operations, web searches, browser control) accurately.

The current version – Claude Sonnet 4 – is a significant step up from earlier versions in reasoning quality and instruction-following. For most OpenClaw use cases – daily conversation, writing tasks, business automation – it’s the right model.

Best for: General daily use, writing, conversation, autonomous task management.

Cost: ~$3 per million input tokens, ~$15 per million output tokens (API pricing). For typical OpenClaw use, expect $5-20/month depending on usage.

Config: anthropic/claude-sonnet-4-6

Pros: Best balance of quality and cost, strong tool use, excellent instruction-following, long context window.

Cons: More expensive than Gemini Flash for high-volume tasks, requires Anthropic API key.

Get an Anthropic API key to use Claude with OpenClaw.

2. Claude Opus (Anthropic) – Best for Complex Tasks

Claude Opus is Anthropic’s most capable model – significantly more powerful than Sonnet for complex reasoning, nuanced writing, and multi-step autonomous work. If you’re running OpenClaw as a business automation agent handling critical decisions, Opus is worth the higher cost.

The practical use case: use Sonnet for daily conversation and routine tasks, and configure Opus specifically for heavy subagent tasks (coding projects, deep research, complex document analysis) where quality matters more than cost.

Best for: Complex coding tasks, deep research, high-stakes autonomous work where output quality is critical.

Cost: ~$15 per million input tokens, ~$75 per million output tokens. Use selectively – not for heartbeats.

Config: anthropic/claude-opus-4-6

Pros: Best reasoning quality available, handles the most complex tasks, superior for nuanced writing.

Cons: Expensive – can add up quickly if used for routine tasks. Reserve for tasks that need it.

3. Gemini 3.1 Pro (Google) – Best Alternative Primary Model

Google’s Gemini 3.1 Pro is a strong alternative primary model and OpenClaw’s configured fallback. It’s comparable to Claude Sonnet in quality for most tasks, handles long contexts well, and comes with a generous free tier via Google AI Studio that’s useful for testing and low-volume use.

One practical advantage: if you’re hitting Anthropic rate limits, Gemini Pro as a fallback keeps OpenClaw working without interruption. The model quality is close enough that most users won’t notice the difference in daily conversation.

Best for: Primary model for users who want Google’s ecosystem, or as a fallback to Anthropic.

Cost: Competitive with Sonnet. Free tier available for low-volume use via Google AI Studio.

Config: google/gemini-3.1-pro-preview

Pros: Strong quality, generous free tier, large context window, good fallback option, Google Search grounding available.

Cons: Slightly less reliable on complex tool use than Claude, API key required for production use.

Get a Gemini API key free via Google AI Studio.

4. Gemini Flash (Google) – Best for Heartbeats and Background Tasks

Gemini Flash is Google’s fast, cheap model – and it’s the right choice for OpenClaw heartbeats and background monitoring tasks. It’s 10-20x cheaper than Sonnet or Gemini Pro, responds faster, and is more than capable enough for routine checks like “is there anything in the inbox that needs attention?”

OpenClaw lets you set a separate model for heartbeat runs. Configuring Gemini Flash for heartbeats and Claude Sonnet for your main session is one of the most effective ways to reduce API costs without sacrificing quality where it matters.

Best for: Heartbeat runs, background monitoring, high-volume simple tasks where cost matters.

Cost: ~$0.075 per million input tokens – dramatically cheaper than Sonnet or Opus.

Config: google/gemini-3-flash-preview (set in agents.defaults.heartbeat.model)

Pros: Very cheap, fast, free tier available, perfectly adequate for monitoring and simple tasks.

Cons: Not suitable as a primary model for complex tasks – quality gap is noticeable for anything demanding.

5. GPT-5.4 via ChatGPT (OpenAI Codex) – Best for Coding Tasks

OpenAI’s GPT-5.4 accessed via ChatGPT OAuth (the openai-codex provider in OpenClaw) is a strong option for coding-heavy subagent tasks. OpenAI has historically had an edge in coding benchmarks, and GPT-5.4 continues that tradition. If you’re using OpenClaw to spawn coding agents for software projects, this is worth configuring as a subagent model.

The Codex provider uses your existing ChatGPT subscription via OAuth – no separate API key needed if you’re already a ChatGPT Plus or Team subscriber.

Best for: Coding subagents, technical automation tasks, users who already have a ChatGPT subscription.

Config: openai-codex/gpt-5.4

Pros: Strong coding performance, uses existing ChatGPT subscription, good for technical tasks.

Cons: OAuth-based (subscription required), not ideal for general conversation tasks.

6. Local Models via Ollama – Best for Privacy and Zero API Cost

Running a local model via Ollama gives you complete privacy (no data leaves your machine) and zero API costs. OpenClaw supports Ollama natively – configure it once and your local model becomes available as a provider.

The practical limitations: quality is lower than Claude Sonnet or Gemini Pro for complex tasks, and you need hardware capable of running the model (see our hardware guide). For simple automation tasks, shorter conversations, and privacy-sensitive workloads, a 7B or 13B local model is genuinely useful.

Good starting models for OpenClaw via Ollama: Llama 3.3 (7B) for fast general use, Mistral Nemo for instruction-following, Qwen 2.5 Coder for coding tasks.

Best for: Privacy-sensitive use cases, reducing API costs for background tasks, users with capable local hardware.

Cost: $0 API cost (electricity only). Requires local hardware – see our machine guide.

Config: ollama/llama3.3 (after installing Ollama locally)

Pros: Complete privacy, no API costs, works offline, no rate limits.

Cons: Lower quality than frontier models, requires capable hardware, slower inference without a GPU.

Recommended Configurations

Best for most users (balanced cost and quality)

Primary: anthropic/claude-sonnet-4-6
Fallbacks: google/gemini-3.1-pro-preview, google/gemini-3-flash-preview
Heartbeat model: google/gemini-3-flash-preview

Best for low cost (minimize API spend)

Primary: google/gemini-3.1-pro-preview
Fallbacks: google/gemini-3-flash-preview
Heartbeat model: google/gemini-3-flash-preview

Best for maximum quality (no cost concerns)

Primary: anthropic/claude-opus-4-6
Fallbacks: anthropic/claude-sonnet-4-6
Heartbeat model: google/gemini-3-flash-preview

Best for privacy (local-first)

Primary: ollama/llama3.3
Fallbacks: google/gemini-3.1-pro-preview
Heartbeat model: ollama/llama3.3

The Bottom Line

For most OpenClaw users: Claude Sonnet as primary, Gemini Flash for heartbeats. That combination gives you high-quality responses for important tasks and cheap background monitoring. The cost difference between using Sonnet for everything versus Sonnet for main sessions and Flash for heartbeats can easily be $10-30/month depending on heartbeat frequency.

Start with the defaults, watch your API usage for a few weeks, then optimize. OpenClaw’s model fallback system means you can experiment without breaking anything – if a model call fails, the next one in your fallback chain picks it up automatically.

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