OpenClaw does not use one fixed AI model by default. OpenClaw works as an AI agent gateway: you connect it to the model provider you want, such as OpenAI ChatGPT, Anthropic Claude, Google Gemini, OpenRouter, DeepSeek, Kimi, MiniMax, Groq, or local models. In practice, the best OpenClaw setup is usually not “one AI for everything,” but a routed system where cheaper models handle routine tasks and stronger models like Claude Sonnet or GPT-4-class models handle complex reasoning, writing, planning, and tool use.

That is the most important answer if you are asking, “Which AI does OpenClaw use?” OpenClaw is not the AI model itself. It is the layer that lets an AI model use tools, connect to apps, and act across channels like Telegram, WhatsApp, Discord, Slack, Gmail, browsers, calendars, and local files.

In my user research, the strongest pattern was clear: people do not choose OpenClaw because they want another chatbot. They choose it because they want an AI that can actually do things.

They choose OpenClaw because they want an AI that can actually do things. If you want that same “AI that actually does things” experience without managing the installation process, model routing, and scattered tools yourself, Buda is worth looking at: it is a cloud-native multi-agent workspace that gives AI agents persistent Drive, Browser, Terminal, Git, Automations, and channel deployment in one place.

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Which AI does OpenClaw use by default?

OpenClaw is best understood as an AI assistant gateway or agent runner, not as a single-model AI product. That means the AI behind OpenClaw depends on the provider you configure.

The most commonly discussed OpenClaw model options include:

AI provider or model family	How it is typically used with OpenClaw
OpenAI / ChatGPT / GPT models	General assistant tasks, writing, reasoning, coding, workflows
Anthropic Claude / Claude Sonnet / Claude Opus	Complex agent work, decision-making, email drafting, client-facing workflows
Google Gemini / Gemini Flash	Lower-cost routine tasks, quick checks, lightweight automation
OpenRouter	Multi-model routing, fallback models, cost control
DeepSeek	Budget-sensitive reasoning or coding tasks
Kimi	Low-cost or coding-oriented workflows
MiniMax	Budget workflows and experimentation
Groq / Llama	Fast local or hosted inference experiments
Local models / Qwen / Ollama-style setups	Privacy-sensitive tasks, local execution, cost control

The practical answer is this:

OpenClaw can use ChatGPT, Claude, Gemini, local models, and other LLMs. The model is configurable. The best model depends on the task, cost tolerance, privacy needs, and how much reliability you need from the agent—similar to evaluating what is the best machine to run OpenClaw.

In my research, the best-performing OpenClaw setups were usually hybrid. A single expensive model for every task often became too costly. A single cheap model for every task often became unreliable. The winning pattern was to route tasks by difficulty.

For example, one researched setup used a local or low-cost model for routine email and calendar checks, then escalated more complex reasoning to Claude Sonnet. That setup reported about a 92% reduction in monthly token spend and an estimated hybrid cost around $2 per million tokens, heavily optimizing the overall OpenClaw cost. The important lesson is not that one router or one provider is always best. The lesson is that model routing matters more than model loyalty.

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How OpenClaw works with AI models and agents

To understand which AI OpenClaw uses, you need to separate three layers:

1. The model: ChatGPT, Claude, Gemini, DeepSeek, Kimi, a local model, or another LLM.
2. The agent layer: OpenClaw, which gives the model instructions, memory, tools, and execution context.
3. The connected apps and tools: Telegram, WhatsApp, Gmail, Discord, Slack, browser, files, shell, calendars, or APIs.

A normal chatbot answers a prompt. OpenClaw is designed for agent workflows (especially if you know how to run OpenClaw effectively) where one prompt may trigger several steps:

• Planning the task
• Choosing tools
• Reading or writing files
• Checking email or calendar data
• Opening a browser
• Summarizing information
• Calling another model
• Asking for confirmation before an external action

This is why the question “Which AI does OpenClaw use?” is less important than “Which AI should OpenClaw use for this specific job?”

A cheap model might be fine for checking whether new emails arrived. It may not be fine for drafting a sensitive business reply, making a multi-step plan, or deciding what to do with private documents, bringing up valid concerns about whether it is safe to install OpenClaw.

The biggest cost problem I found in user research was not the price of one message. It was the hidden cost of agent loops. An OpenClaw task can create multiple model calls behind the scenes. A single user request can turn into planning calls, tool calls, summarization calls, memory calls, and retry calls.

That is why the best OpenClaw users evaluate models by cost per completed task, not just cost per token.

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Best AI model for OpenClaw: Claude, ChatGPT, Gemini, OpenRouter, or local models?

There is no universal best AI for OpenClaw. The best choice depends on what you are asking the agent to do.

Claude Sonnet for complex OpenClaw agent workflows

In my research, Claude Sonnet was frequently the preferred model for serious agent work (often prompting developers to compare OpenClaw vs Claude Code). The strongest use cases included:

• Customer support responses
• Email drafting
• Business decision support
• Complex reasoning
• Multi-step planning
• Client-facing agent deployments

One deployment-focused case from my research used Claude Sonnet for about 90% of business use cases. The reason was not that Claude was the cheapest model per token. The reason was that it often required fewer retries for complex work.

That matters because agent workflows can become expensive when the model makes mistakes. A cheaper model that needs three retries, extra correction, or human cleanup may cost more in practice than a stronger model that completes the task correctly the first time.

Practical takeaway: Claude Sonnet is often a strong choice for OpenClaw when the workflow requires judgment, nuance, reliability, and fewer failed tool loops.

ChatGPT and OpenAI models for flexible OpenClaw workflows

OpenAI models are a natural fit for OpenClaw because many builders already understand ChatGPT-style workflows. ChatGPT can be used for:

• General assistant tasks
• Writing and rewriting
• Research summaries
• Coding help
• Workflow planning
• Customer-facing drafts
• Personal productivity

For many users, ChatGPT is the easiest starting point because it is familiar. If you already have OpenAI API access, OpenClaw can become the execution layer that turns ChatGPT-style intelligence into a tool-using assistant.

However, there is an important distinction:

Using ChatGPT in the ChatGPT app is not the same as connecting OpenClaw to OpenAI through an API or supported provider method.

For OpenClaw, the key question is usually whether you are connecting through an API key, a provider integration, or a routed model service.

Gemini Flash for low-cost routine OpenClaw tasks

Gemini Flash and similar lightweight models appear most useful for simple, high-volume work. Examples include:

• Basic classification
• Routine notifications
• Simple email checks
• Short summaries
• Low-risk automation
• Repetitive background tasks

In one researched routing setup (sometimes seen in older discussions of OpenClaw vs Clawdbot), a lightweight model handled the majority of routine load while stronger models handled harder work. One case described a setup where Flash-style routing handled about 85% of the workload, with more advanced models used only when needed.

That pattern is important. Gemini Flash may not be the best model for every OpenClaw task, but it can be valuable when the task is simple and repeated many times.

Practical takeaway: Use cheaper models for predictable, low-risk, repeatable tasks. Do not waste premium models on “is there a new email?” checks.

OpenRouter for multi-model OpenClaw routing

OpenRouter is useful when you want access to multiple models through one layer. In OpenClaw workflows, it is often considered for:

• Testing different models
• Cost control
• Fallback routing
• Avoiding dependence on one provider
• Matching model strength to task complexity

The limitation is that automatic model selection is not always reliable. In my research, “auto” routing was not always trusted for complex learning, planning, or agentic tasks. The better approach was manual or rules-based routing: assign simple work to cheaper models and reserve stronger models for high-value tasks.

Practical takeaway: OpenRouter can help OpenClaw use many models, but you should still define routing rules instead of assuming auto-selection will always choose correctly.

Local models for private OpenClaw workflows

Local models are attractive when privacy and cost control matter. They are useful for:

• Local file processing
• Private notes
• Offline workflows
• Sensitive documents
• Reducing API costs
• Experimenting without per-token billing

The tradeoff is hardware and setup complexity. A strong local model may require a capable GPU, memory, and more engineering effort. In my research, people were especially interested in whether a local model could actually use OpenClaw tools, write files, call functions, and behave like an agent rather than just produce text.

Practical takeaway: Local models are best for privacy-sensitive and cost-sensitive workflows, but they require more setup and may not match premium cloud models for complex agent reliability.

How to use OpenClaw with ChatGPT

To use OpenClaw with ChatGPT, you generally connect OpenClaw to an OpenAI-compatible model provider, configure your API credentials or provider access, choose the model you want, and then give OpenClaw access to the tools or channels it should use.

A practical ChatGPT plus OpenClaw setup usually looks like this:

1. Choose your OpenAI model
   Select the model based on your task. Use a stronger GPT model for complex planning, writing, coding, or tool-heavy workflows. Use a cheaper model for lightweight, repetitive tasks.
2. Connect OpenClaw to OpenAI
   Configure the OpenAI provider inside OpenClaw using the supported provider setup method. In many setups, this means adding an API key or provider credentials.
3. Define the OpenClaw agent role
   Tell the agent what it is responsible for. For example: personal assistant, email assistant, content operations assistant, research assistant, support assistant, or coding helper.
4. Connect tools carefully
   Add only the tools the agent actually needs. Common tools include Gmail, calendar, browser, Telegram, Discord, Slack, local files, or APIs.
5. Set permission boundaries
   Do not give full access to everything on day one. Start with read-only or low-risk tasks. Require approval for sending emails, deleting files, purchasing items, publishing content, or making external changes.
6. Test with real workflows
   Do not only test “hello world” prompts. Test the workflow you actually care about: drafting an email, summarizing a thread, checking a calendar, researching a topic, or creating a task list.
7. Track cost per completed task
   ChatGPT model cost should be judged by the total workflow cost, not only the first prompt. If the agent needs many retries, tool loops, or corrections, the workflow may be more expensive than expected.

A simple example:

Before OpenClaw with ChatGPT:
You ask ChatGPT to draft an email, copy the output, paste it into Gmail, check your calendar manually, and decide what to send.

After OpenClaw with ChatGPT:
OpenClaw can connect the model to your email and calendar context, draft a reply, check scheduling constraints, and prepare the response for approval.

The best starting workflow is not a fully autonomous agent. The best starting workflow is a human-approved assistant: let OpenClaw prepare actions, then require your confirmation before anything leaves your system.

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Do you need Claude Pro for OpenClaw?

No, you do not necessarily need Claude Pro for OpenClaw. OpenClaw can work with different AI providers depending on how you configure it. Claude Pro may be useful if your setup supports using your Claude subscription in the workflow, but many OpenClaw users instead connect through APIs, OpenAI-compatible providers, OpenRouter, local models, or other model services.

The deeper question is not whether you need Claude Pro. The better question is:

Do you need Claude-level reasoning for the workflows you want OpenClaw to run?

For simple automation, Claude Pro may be unnecessary. For complex agent behavior, Claude Sonnet or Claude Opus-level reasoning may be worth it.

When Claude Pro or Claude models make sense for OpenClaw

Claude is a strong fit when your OpenClaw workflow involves:

• Complex multi-step reasoning
• Long instructions
• Sensitive email drafting
• Customer communication
• Business decisions
• Summarizing long context
• Reducing retries
• Higher-quality writing

In one researched personal case, a user connected OpenClaw into a local assistant setup and used Claude as part of a broader workflow involving Telegram, WhatsApp, Gmail, calendar, browser access, and investment report summaries. The setup took about 20 minutes for OpenClaw installation after the machine was ready, while preparing the old Mac Mini and installing Ubuntu took around 2 hours.

That case showed why Claude-style models are attractive: the user was not just chatting. The agent was expected to read messages, manage context, summarize reports, and support real personal decisions.

When you do not need Claude Pro for OpenClaw

You probably do not need Claude Pro if your OpenClaw use case is mostly:

• Basic reminders
• Simple classification
• Routine email checks
• Low-stakes summaries
• Notifications
• Basic content formatting
• Local file organization
• Experimental workflows

For these tasks, a cheaper model through OpenAI, Gemini, OpenRouter, DeepSeek, Kimi, MiniMax, or a local model may be enough.

Claude Pro vs API for OpenClaw

A common mistake is assuming a chatbot subscription and an API-based agent workflow are the same thing. They are not always interchangeable.

A subscription such as Claude Pro or ChatGPT Plus may give you access to the consumer chat app. OpenClaw setups often require provider configuration, API access, OAuth-style integration, or another connection method depending on the implementation.

For production-style OpenClaw workflows, API access is often easier to measure, route, and control. For personal experimentation, a subscription-based setup may feel more convenient if supported by your OpenClaw installation path.

Practical answer: You do not need Claude Pro to use OpenClaw, but Claude models can be very effective for OpenClaw when the task requires reliable reasoning and fewer retries.

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OpenClaw case study: personal AI assistant with Telegram, WhatsApp, Gmail, calendar, and browser

One of the clearest OpenClaw use cases from my research was a personal assistant running on an old machine.

Use case description

The setup used OpenClaw on a repurposed 2011 Mac Mini. The goal was to create a personal AI assistant that could work across messaging, email, calendar, and browser-based tools.

User profile

The user was technically comfortable enough to repair an older computer, install Ubuntu, and configure an agent stack.

Goal

The goal was to turn unused hardware and existing AI access into a practical local assistant. The assistant was expected to:

• Communicate through Telegram
• Monitor or participate in WhatsApp-style group context
• Work with Gmail
• Use shared calendar information
• Access browser-based investment reports
• Summarize investment recommendations
• Help with time-sensitive personal tasks, such as registering a child for swimming lessons

Tools mentioned

The workflow included:

• OpenClaw
• Claude
• Telegram
• WhatsApp
• Gmail
• Browser access
• Calendar tools
• Ubuntu on an old Mac Mini

Quantifiable data

Two concrete numbers stood out:

• Preparing the old Mac Mini and installing Ubuntu took about 2 hours
• Installing OpenClaw took about 20 minutes

Before OpenClaw

Before the setup, the old Mac Mini was unused, AI access was underutilized, and personal workflows were scattered across messaging apps, email, calendar, and browser tools.

After OpenClaw

After the setup, OpenClaw became the agent layer connecting AI to real tools. Instead of asking a chatbot for generic advice, the user could work toward an assistant that had access to actual personal context and channels.

Insight

This case shows the real reason people ask, “Which AI does OpenClaw use?” They are usually not trying to compare model names in isolation. They are trying to build a practical assistant that can operate across the tools they already use.

The model matters, but the workflow matters more.

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OpenClaw case study: reducing token cost by routing simple tasks away from Claude

The most important cost lesson from my research is that OpenClaw can become expensive if every task goes to a premium model.

Use case description

A hybrid routing setup used cheaper or local models for routine tasks and escalated complex work to Claude.

User profile

The user was building OpenClaw workflows with enough volume that token cost mattered. This was not a one-off chatbot experiment. It was a system designed to run repeatedly.

Goal

The goal was to stop using Claude for low-value background tasks such as routine email or calendar checks, while still using a stronger model when the work required deeper reasoning.

Tools mentioned

The setup referenced:

• OpenClaw
• Claude 3.5 Sonnet
• Local models
• GPT-style models
• A routing layer such as ClawRouter

Quantifiable data

The reported result was significant:

• About 92% reduction in monthly token spend
• Hybrid cost around $2 per million tokens

Before OpenClaw routing

The workflow used premium models for routine tasks. That meant even simple checks could consume expensive tokens throughout the day.

Example problem: using Claude to repeatedly check whether an inbox has new messages is usually wasteful.

After OpenClaw routing

The workflow routed simple tasks to cheaper or local models. Claude was reserved for complex reasoning, higher-risk outputs, or tasks that needed better judgment.

Insight

This case is one of the strongest arguments against asking only, “Which AI does OpenClaw use?”

The better question is:

Which AI should OpenClaw use for each category of task?

For serious OpenClaw usage, the answer is usually model routing.

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OpenClaw case study: Claude Sonnet for business workflows and client deployments

Business workflows are where model reliability becomes more important than raw token price.

Use case description

In business-oriented OpenClaw deployments, Claude Sonnet was used for customer interaction, email drafting, and decision-support workflows.

User profile

The setup came from a builder or service provider working on client-style agent deployments.

Goal

The goal was to produce reliable, high-quality outputs with fewer retries and less cleanup.

Tools mentioned

The main model mentioned was:

• Claude Sonnet

Quantifiable data

The clearest number from the research was:

• Claude Sonnet was recommended for about 90% of business use cases in that deployment context

No specific ROI, revenue lift, or conversion-rate improvement was shared.

Before Claude-centered routing

A cheaper model-first approach could reduce apparent token cost but increase rework, retries, and human review.

After using Claude Sonnet for core business work

Claude Sonnet handled the higher-value tasks where quality mattered. Cheaper models could still handle background work, but Claude was used where failure would cost time, trust, or money.

Insight

For business workflows, the cheapest model is not always the cheapest system. A model that produces better first-pass results can reduce total task cost.

This is especially true for OpenClaw because agent mistakes may trigger tool loops, extra prompts, more summarization, and manual correction.

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OpenClaw case study: low-cost models such as Gemini, DeepSeek, Kimi, MiniMax, and OpenRouter

Budget-sensitive OpenClaw users often experiment with cheaper model providers. This is reasonable, but the results depend heavily on the task.

Use case description

Low-cost providers were tested for daily agent tasks, coding workflows, fallback models, and high-volume background activity.

User profile

The users in this category were typically solo builders, technical hobbyists, or budget-conscious operators.

Goal

The goal was to reduce the cost of running OpenClaw without losing too much quality.

Tools mentioned

The main tools and models included:

• OpenRouter
• Kimi
• Gemini Flash or Gemini Flash Lite
• DeepSeek API
• MiniMax
• Groq
• Llama-style models

Quantifiable data

Several concrete numbers appeared in the research:

• Kimi heavy usage reportedly exhausted a weekly quota in about 36 hours
• DeepSeek API pricing was discussed around $0.28 per million input tokens and $0.42 per million output tokens
• MiniMax was discussed as having a $10/month style plan
• One routing example claimed Flash handled about 85% of the workload

These numbers should be treated as case-specific, not universal pricing guarantees. Model pricing and limits change often.

Before low-cost routing

A single premium model handled too many tasks, creating cost pressure.

After low-cost routing

Cheap models handled simple tasks, and stronger models were used only when needed.

Insight

Low-cost models are valuable in OpenClaw, but only when paired with clear task boundaries. They are best for repetitive, low-risk, easy-to-check work.

For high-risk, ambiguous, or client-facing work, cheaper models may create hidden costs through retries and correction.

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OpenClaw content operations case study: where AI agents help and where they still need review

OpenClaw can be useful for content operations, but it should not be treated as a fully autonomous brand strategist.

Use case description

In content operations research, OpenClaw was most useful for high-volume, repetitive tasks that did not require deep judgment.

Examples include:

• Reformatting content
• Drafting variants
• Summarizing source material
• Organizing content ideas
• Preparing rough outlines
• Turning notes into structured drafts

User profile

The use case fits content operators, no-code builders, solo marketers, and teams trying to reduce repetitive manual work.

Goal

The goal was to increase content production speed and reduce repetitive manual effort.

Tools mentioned

The primary tool was OpenClaw, with the model depending on the user’s setup.

Quantifiable data

No measurable data shared.

Before OpenClaw

Content tasks were handled manually: copying notes, rewriting drafts, summarizing threads, creating outlines, and formatting outputs.

After OpenClaw

OpenClaw could handle some repetitive content workflows, but brand tone, final judgment, and sensitive claims still required human review.

Insight

OpenClaw is useful when the task has clear inputs, clear outputs, and easy review. It is risky when the task requires brand judgment, legal sensitivity, factual precision, or final publishing authority.

The best content workflow is not “let OpenClaw publish automatically.” It is “let OpenClaw prepare, structure, and draft, then let a human approve.”

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OpenClaw AI model selection framework: how to choose the right AI

The best way to choose an AI for OpenClaw is to group tasks by risk and complexity.

Use cheap models for low-risk routine tasks

Use Gemini Flash, DeepSeek, Kimi, MiniMax, OpenRouter-routed models, or local models for:

• Inbox checks
• Calendar checks
• Simple tagging
• Short summaries
• Notifications
• Basic classification
• Repetitive formatting
• Low-risk internal tasks

These tasks are good candidates for lower-cost models because mistakes are easier to detect and fix.

Use stronger models for reasoning and business-critical tasks

Use Claude Sonnet, Claude Opus, GPT-4-class models, or other strong reasoning models for:

• Customer communication
• Sales replies
• Complex email drafting
• Business decisions
• Multi-step planning
• Research synthesis
• Legal-adjacent or policy-sensitive summaries
• Sensitive personal or financial context
• Tool-heavy workflows where errors can cascade

These tasks need better judgment and fewer retries.

Use local models for privacy-sensitive work

Use local models when the priority is:

• Privacy
• Offline access
• Local files
• Lower API exposure
• Cost control after hardware investment

But be realistic. Local models may need strong hardware and careful setup. They may also need additional configuration to use OpenClaw tools effectively.

Use routing when OpenClaw becomes a daily system

Once OpenClaw is running every day, a single-model setup often becomes inefficient.

A practical routing structure could look like this:

Task type	Recommended OpenClaw model strategy
Routine checks	Cheap model or local model
Summaries	Cheap or mid-tier model
Personal assistant tasks	Mid-tier model with human approval
Customer-facing writing	Claude Sonnet or GPT-4-class model
Complex planning	Strong reasoning model
Sensitive files	Local model or privacy-focused setup
High-volume workflows	Router plus fallback rules

The key is to make model choice part of the workflow design.

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OpenClaw cost: why cost per completed task matters more than token price

A major mistake with OpenClaw is choosing a model only by token price.

OpenClaw tasks can be more expensive than normal chatbot prompts because an agent may make multiple calls. One request may involve:

• Planning
• Tool selection
• Web browsing
• Reading files
• Summarization
• Memory update
• Error correction
• Retrying failed steps
• Drafting final output

This creates a simple but important formula:

True OpenClaw cost = model price × number of calls × retries × review burden

A cheap model can be expensive if it fails often. A premium model can be economical if it completes the task correctly with fewer loops.

The best cost metric is:

Cost per completed task

Not:

Cost per input token

In the strongest cost case from my research, routing simple work away from Claude reduced monthly token spend by about 92%. That happened because the workflow stopped treating every task as equally difficult.

The practical lesson is simple:

• Do not use Claude or GPT-4-class models for every background check.
• Do not use cheap models for sensitive, complex, or high-risk tasks.
• Route tasks based on difficulty.
• Measure total workflow cost, not just model sticker price.

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OpenClaw security: what to consider before giving an AI access to tools

The more useful OpenClaw becomes, the more careful you need to be.

OpenClaw can connect models to tools like email, browser, files, shell, calendars, and messaging platforms. That creates real power, but also real risk.

The main risks include:

• Prompt injection
• Accidental email sending
• Wrong file edits
• Over-permissioned agents
• Browser-based manipulation
• Private data exposure
• Tool loops that perform unintended actions

The safest OpenClaw setup starts small.

Practical safety rules for OpenClaw

Use these rules before giving OpenClaw access to important systems:

1. Start with read-only access where possible
   Let the agent summarize and prepare before it can send, delete, or modify.
2. Require approval for external actions
   Sending emails, posting messages, submitting forms, purchasing, deleting, and publishing should require human confirmation.
3. Use a separate system user
   If OpenClaw runs locally, consider a dedicated OS user with limited permissions.
4. Restrict file access
   Give OpenClaw access only to specific folders, not your entire machine.
5. Disable shell access unless truly needed
   Shell access is powerful and risky. Avoid enabling it for casual workflows.
6. Log agent actions
   You need to know what the agent did, which tools it used, and where failures happened.
7. Test with low-risk workflows first
   Start with summaries, drafts, and reminders before giving OpenClaw permission to act.

The safest version of OpenClaw is not the most autonomous version. It is the version where the AI can prepare useful work while you stay in control of irreversible actions.

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OpenClaw setup strategy: the best practical configuration for most users

For most people, the best OpenClaw configuration is a three-tier model system.

Tier 1: Cheap or local model for background tasks

Use this tier for:

• Checking inbox state
• Short summaries
• Tagging
• Notifications
• Simple routing
• Basic classification

Recommended model types:

• Gemini Flash-style models
• DeepSeek
• Kimi
• MiniMax
• Local models
• OpenRouter low-cost options

Tier 2: Mid-tier model for everyday assistant work

Use this tier for:

• Personal assistant tasks
• Drafting non-sensitive replies
• Calendar reasoning
• Content outlines
• Research summaries
• Note organization

Recommended model types:

• Stronger OpenAI models
• Claude Sonnet if cost allows
• Reliable OpenRouter selections
• Better local models if available

Tier 3: Premium reasoning model for high-value tasks

Use this tier for:

• Client communication
• Important emails
• Complex decision-making
• High-stakes summaries
• Multi-step planning
• Sensitive business workflows

Recommended model types:

• Claude Sonnet
• Claude Opus
• GPT-4-class models
• Other top-tier reasoning models

This approach gives OpenClaw the best balance of cost, speed, and quality.

The highest-performing setups in my research were not built around one model. They were built around model discipline: cheap models for cheap tasks, strong models for strong tasks, and human approval for anything risky.

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FAQ: Which AI does OpenClaw use?

Which AI does OpenClaw use?

OpenClaw does not use one fixed AI. It can work with different model providers such as ChatGPT, Claude, Gemini, OpenRouter, DeepSeek, Kimi, MiniMax, Groq, and local models. The AI depends on your configuration.

Is OpenClaw an AI model?

No. OpenClaw is not itself the AI model. It is an agent gateway or execution layer that connects AI models to tools, apps, files, browsers, messaging platforms, and workflows.

Can OpenClaw use ChatGPT?

Yes. OpenClaw can be configured to use OpenAI or ChatGPT-style models if your setup supports the provider connection. ChatGPT is useful for general assistant tasks, writing, coding, summaries, and workflow planning.

How do I use OpenClaw with ChatGPT?

Connect OpenClaw to an OpenAI-compatible provider, configure the required credentials, select the model, define your agent role, connect tools carefully, and test with low-risk workflows first. Use human approval before allowing the agent to send emails, publish content, delete files, or make external changes.

Do you need Claude Pro for OpenClaw?

No. You do not necessarily need Claude Pro for OpenClaw. OpenClaw can use different providers and models. Claude Pro or Claude models can be useful for complex reasoning and high-quality agent workflows, but they are not mandatory for every setup.

Is Claude better than ChatGPT for OpenClaw?

Claude Sonnet often performs well in complex OpenClaw workflows that require judgment, long-context reasoning, and fewer retries. ChatGPT is also strong for general-purpose tasks, coding, writing, and assistant workflows. The better choice depends on the task.

What is the cheapest AI model for OpenClaw?

Cheaper options often include Gemini Flash-style models, DeepSeek, Kimi, MiniMax, OpenRouter-routed models, and local models. However, the cheapest token price is not always the cheapest workflow. Measure cost per completed task.

Can OpenClaw use local AI models?

Yes, OpenClaw can be used with local models in some setups. Local models are useful for privacy and cost control, but they may require stronger hardware and more technical configuration.

Does OpenClaw work with OpenRouter?

Yes, OpenRouter is commonly considered for OpenClaw because it gives access to multiple models and can help with routing or fallback strategies. However, automatic model selection may still need manual rules for serious workflows.

Which AI should I choose for studying or school tasks in OpenClaw?

For learning, explanations, and study planning, use a model that is strong at reasoning and clear explanations. Claude Sonnet or a strong GPT model is usually better than a very cheap model for tutoring-style tasks.

Should I use one model or multiple models with OpenClaw?

For serious use, multiple models are usually better. Use cheaper models for routine tasks and stronger models for complex, sensitive, or business-critical work.

Why does OpenClaw become expensive?

OpenClaw can become expensive because agent workflows may trigger multiple model calls: planning, tool use, summarization, memory updates, retries, and final responses. This is why cost per completed task is more important than token price.

Is OpenClaw safe to connect to Gmail, files, or a browser?

It can be useful, but you should limit permissions. Start with read-only access, restrict folders, require approval for external actions, and avoid giving shell or full-system access unless necessary.

Can OpenClaw replace automation tools?

OpenClaw can help with flexible, language-based workflows, but it does not always replace dedicated automation tools. It works best when tasks involve reasoning, summarization, drafting, or tool coordination. For predictable workflows, traditional automation may still be more reliable.

What is the best OpenClaw setup for most users?

The best setup is usually hybrid: a cheap or local model for routine tasks, a mid-tier model for everyday assistant work, and a premium model such as Claude Sonnet or a GPT-4-class model for complex reasoning and high-value tasks.

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Final answer: Which AI does OpenClaw use?

OpenClaw can use many AI models. It is not limited to one default AI. You can connect OpenClaw to ChatGPT, Claude, Gemini, OpenRouter, DeepSeek, Kimi, MiniMax, Groq, or local models depending on your setup.

For most real workflows, the best OpenClaw configuration is not a single model. It is a routed model system:

• Use cheap or local models for simple routine tasks.
• Use ChatGPT or mid-tier models for general assistant work.
• Use Claude Sonnet or another premium reasoning model for complex, sensitive, or business-critical workflows.
• Require human approval before external actions.
• Measure success by cost per completed task, not cost per token.

That is the practical answer behind the keyword “Which AI does OpenClaw use?” OpenClaw uses the AI you connect to it, but the best results come from choosing the right AI for each task.

Which AI does OpenClaw use?

OpenClaw does not use one fixed AI model by default. OpenClaw is an AI agent gateway: it connects to the model provider you choose, such as OpenAI ChatGPT, Anthropic Claude, Google Gemini, OpenRouter, DeepSeek, Kimi, MiniMax, Groq, or local models. In real workflows, the best OpenClaw setup is usually hybrid: cheaper models handle routine tasks, while stronger models like Claude Sonnet or GPT-4-class models handle complex reasoning, writing, and tool-heavy work.

That is the clearest answer to “Which AI does OpenClaw use?” OpenClaw is not the model itself. It is the layer that lets an AI model use tools, connect to apps, and operate across channels such as Telegram, WhatsApp, Gmail, Discord, Slack, browsers, calendars, files, and APIs.

From my user research, the strongest pattern was simple: people do not use OpenClaw because they want another chatbot. They use it because they want an AI that can actually do things.

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Which AI does OpenClaw use by default?

OpenClaw is best understood as an AI agent runner, not a single-model AI product. The AI behind OpenClaw depends on the provider you configure.

Common OpenClaw model options include:

AI provider or model	Typical OpenClaw use
OpenAI / ChatGPT / GPT models	General assistant work, writing, coding, planning
Claude Sonnet / Claude Opus	Complex reasoning, email drafting, business workflows
Gemini / Gemini Flash	Lower-cost routine checks and lightweight automation
OpenRouter	Multi-model access, fallback routing, cost control
DeepSeek / Kimi / MiniMax	Budget-sensitive workflows
Groq / Llama-style models	Fast inference experiments
Local models / Qwen / Ollama-style setups	Privacy-sensitive local work

The practical answer is: OpenClaw uses whichever AI model you connect to it. The better question is which model should handle each task.

A single premium model for every task often becomes expensive. A single cheap model for every task often becomes unreliable. The strongest setups I analyzed used task-based routing: routine tasks went to low-cost models, while complex tasks escalated to Claude Sonnet or GPT-class models.

One routing case reported a 92% reduction in monthly token spend by moving routine email and calendar checks away from Claude and only using a stronger model for harder reasoning. The same setup estimated hybrid cost at around $2 per million tokens.

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How OpenClaw works with AI models

OpenClaw has three practical layers:

1. The model: ChatGPT, Claude, Gemini, DeepSeek, Kimi, local models, or another LLM.
2. The agent layer: OpenClaw, which gives the model tools, instructions, memory, and execution context.
3. The connected tools: Gmail, calendar, browser, Telegram, WhatsApp, files, APIs, Slack, Discord, or shell access.

This is why OpenClaw cost and quality can vary so much. A normal chatbot gives one answer. An OpenClaw agent may plan, call tools, read files, summarize results, update memory, retry after errors, and then produce a final answer.

That means the real cost is not just the model’s token price. It is the cost per completed task.

A cheap model can become expensive if it fails repeatedly. A premium model can be cheaper overall if it completes the task correctly with fewer retries.

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Best AI model for OpenClaw: Claude, ChatGPT, Gemini, OpenRouter, or local models?

There is no universal best AI for OpenClaw. The right model depends on task complexity, privacy, budget, and how much failure matters.

Claude Sonnet for complex OpenClaw workflows

Claude Sonnet was the most consistently preferred option in complex agent workflows from my research. It appeared strongest for:

• Customer communication
• Email drafting
• Business decision support
• Multi-step planning
• Long-context reasoning
• Tool-heavy workflows

In one business deployment case, Claude Sonnet was recommended for about 90% of business use cases. The reasoning was practical: although Claude may cost more per token, it often needs fewer retries for complex work.

That matters in OpenClaw because model mistakes can create tool loops, extra calls, manual cleanup, and higher total cost.

ChatGPT and OpenAI models for flexible OpenClaw workflows

ChatGPT and OpenAI models are a strong starting point because they are familiar and flexible. They fit well for writing, coding, general assistance, summaries, and planning.

A practical OpenClaw plus ChatGPT workflow looks like this:

Before: ask ChatGPT to draft an email, copy it into Gmail, check your calendar manually, then decide what to send.

After: OpenClaw connects the model to email and calendar context, drafts the reply, checks scheduling constraints, and prepares the response for human approval.

The key distinction: using ChatGPT in the ChatGPT app is not the same as connecting OpenClaw to an OpenAI-compatible provider. OpenClaw usually needs provider configuration, credentials, or API access depending on your setup.

Gemini, DeepSeek, Kimi, MiniMax, and OpenRouter for lower-cost routing

Lower-cost models are useful when the task is simple, repetitive, and easy to verify. Examples include:

• Inbox checks
• Calendar checks
• Short summaries
• Basic classification
• Notifications
• Formatting
• Low-risk background tasks

One routing case described a Flash-style model handling about 85% of routine workload, while stronger models were saved for harder tasks. Other cost-focused research found examples such as Kimi heavy usage exhausting a weekly quota in about 36 hours, DeepSeek API pricing discussed around $0.28 per million input tokens and $0.42 per million output tokens, and MiniMax-style plans around $10/month.

These figures are case-specific and pricing can change, but the insight is stable: low-cost models are valuable when paired with clear routing rules.

Local models for privacy-sensitive OpenClaw workflows

Local models are attractive when privacy and API cost control matter. They are useful for private files, internal notes, offline workflows, and sensitive documents.

The tradeoff is setup complexity. A strong local model may require capable hardware, memory, and more technical configuration. The practical challenge is not only whether the model can chat, but whether it can reliably use OpenClaw tools, write files, call functions, and follow constraints.

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How to use OpenClaw with ChatGPT

To use OpenClaw with ChatGPT, connect OpenClaw to an OpenAI-compatible model provider, configure your credentials, choose the model, and define what tools the agent can access.

A practical setup process:

1. Choose the model
   Use a stronger GPT model for complex writing, planning, coding, and tool-heavy tasks. Use cheaper models for lightweight work.
2. Connect the provider
   Add your OpenAI-compatible provider credentials or API key through the OpenClaw setup path.
3. Define the agent role
   Decide whether it is a personal assistant, email assistant, content assistant, research assistant, or support assistant.
4. Limit tools at first
   Start with Gmail, calendar, browser, Telegram, Slack, files, or APIs only when needed.
5. Require approval
   Do not let the agent send emails, delete files, publish content, or submit forms without confirmation.
6. Measure the real cost
   Track total calls, retries, and successful completed tasks, not just token price.

The best starting point is not a fully autonomous agent. It is a human-approved assistant that prepares useful work and asks before taking external action.

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Do you need Claude Pro for OpenClaw?

No, you do not necessarily need Claude Pro for OpenClaw. OpenClaw can work with different model providers depending on how you configure it. Claude Pro or Claude models can be useful, but they are not mandatory.

The better question is: Do you need Claude-level reasoning for your OpenClaw workflow?

Claude makes sense when OpenClaw is handling:

• Complex planning
• Long-context summaries
• Sensitive email drafting
• Customer communication
• Business workflows
• Fewer-retry agent execution

You may not need Claude Pro if your workflows are mostly:

• Basic reminders
• Simple classification
• Notifications
• Routine email checks
• Low-stakes summaries
• Formatting tasks

A common mistake is assuming a chatbot subscription and an API-based OpenClaw workflow are the same. They are not always interchangeable. A subscription like Claude Pro may give access to the consumer chat app, while OpenClaw may require API access, provider configuration, OAuth-style setup, or another connection method depending on the installation.

For production-style workflows, API-based access is usually easier to measure, route, and control. For personal experimentation, subscription-based access may be convenient when supported.

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OpenClaw case study: personal AI assistant on an old Mac Mini

One of the clearest OpenClaw use cases I studied was a personal assistant built on a repurposed 2011 Mac Mini.

Setup

The machine was repaired, Ubuntu was installed, and OpenClaw was configured as a local assistant connected to everyday tools.

Tools used

The workflow included:

• OpenClaw
• Claude
• Telegram
• WhatsApp
• Gmail
• Calendar
• Browser access
• Ubuntu on an old Mac Mini

Goal

The goal was to turn unused hardware and underused AI access into a practical assistant that could work across personal messaging, email, calendar, and browser-based tools.

The assistant was intended to help with tasks such as reading family messages, handling Gmail and calendar context, logging into browser-based investment reports, summarizing investment recommendations, and supporting time-sensitive personal tasks like registering a child for swimming lessons.

Data

Two concrete numbers stood out:

• Preparing the Mac Mini and installing Ubuntu took about 2 hours
• Installing OpenClaw took about 20 minutes

Before and after

Before OpenClaw: the old Mac Mini was unused, AI access was underutilized, and personal workflows were scattered across messaging apps, email, calendar, and browser tools.

After OpenClaw: the AI became part of a tool-using local assistant. Instead of only chatting, the model could work through OpenClaw across real personal systems.

Insight

This case shows why the question “Which AI does OpenClaw use?” is incomplete. The model matters, but the real value comes from connecting that model to the user’s actual tools and context.

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OpenClaw case study: reducing token cost by 92% with model routing

The strongest cost case from my research involved routing simple tasks away from premium models.

Setup

The workflow used cheaper or local models for routine tasks and escalated complex reasoning to Claude Sonnet.

Goal

The goal was to avoid using Claude for low-value background tasks such as routine email or calendar checks while still using a strong model when judgment mattered.

Tools used

The setup referenced:

• OpenClaw
• Claude 3.5 Sonnet
• Local models
• GPT-style models
• A routing layer such as ClawRouter

Data

The reported result:

• About 92% reduction in monthly token spend
• Hybrid cost around $2 per million tokens

Before and after

Before routing: premium models handled too many simple tasks, creating unnecessary token burn.

After routing: routine checks went to cheaper or local models, while Claude handled complex reasoning and higher-risk outputs.

Insight

This is the clearest argument for hybrid OpenClaw setups. The best OpenClaw configuration is not “one best AI.” It is a model-routing system that matches task difficulty to model capability.

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OpenClaw case study: Claude Sonnet for business workflows

Business workflows are where reliability matters more than raw token price.

Setup

Claude Sonnet was used for client-style workflows involving customer interaction, email drafting, and business decision support.

Goal

The goal was to reduce retries, improve output quality, and make agent behavior more reliable in high-value tasks.

Data

The clearest quantitative finding was that Claude Sonnet was recommended for around 90% of business use cases in this deployment context.

No specific ROI or revenue lift was shared.

Before and after

Before: cheaper models looked attractive on token price but could create more rework, failed tool calls, and human cleanup.

After: Claude Sonnet handled the higher-value work where quality, judgment, and fewer retries mattered.

Insight

For business OpenClaw workflows, the cheapest model is not always the cheapest system. A stronger model can reduce total cost if it completes the task with fewer failures.

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OpenClaw safety and permissions

OpenClaw becomes more powerful when it can access tools. That also makes it riskier.

The main risks are:

• Prompt injection
• Accidental email sending
• Wrong file edits
• Over-permissioned agents
• Browser-based manipulation
• Private data exposure
• Tool loops that perform unintended actions

My practical safety rule is simple: start read-only, then add permissions slowly.

For most OpenClaw setups:

• Give the agent access only to tools it actually needs
• Restrict file access to specific folders
• Use a separate system user where possible
• Avoid shell access unless necessary
• Require human approval for sending, deleting, publishing, purchasing, or submitting forms
• Log actions so you can audit what happened

The safest OpenClaw setup is not the most autonomous one. It is the one where the AI prepares useful work while the human controls irreversible actions.

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FAQ: Which AI does OpenClaw use?

Which AI does OpenClaw use?

OpenClaw can use many AI models. It may connect to ChatGPT, Claude, Gemini, OpenRouter, DeepSeek, Kimi, MiniMax, Groq, or local models depending on your setup.

Is OpenClaw an AI model?

No. OpenClaw is an agent gateway or execution layer. It connects AI models to tools, apps, files, browsers, and workflows.

Can OpenClaw use ChatGPT?

Yes. OpenClaw can use ChatGPT or OpenAI-compatible models when configured with the right provider credentials or API access.

How do I use OpenClaw with ChatGPT?

Connect OpenClaw to an OpenAI-compatible provider, choose the model, define the agent role, connect only the tools it needs, and require approval for external actions.

Do you need Claude Pro for OpenClaw?

No. Claude Pro is not required for every OpenClaw setup. Claude models are useful for complex reasoning and business workflows, but OpenClaw can also use ChatGPT, Gemini, OpenRouter, local models, and other providers.

Is Claude better than ChatGPT for OpenClaw?

Claude Sonnet often performs well for complex reasoning, long-context work, and business workflows. ChatGPT is strong for general assistant work, writing, coding, and flexible productivity tasks. The better choice depends on the workflow.

What is the cheapest AI model for OpenClaw?

Lower-cost options often include Gemini Flash, DeepSeek, Kimi, MiniMax, OpenRouter-routed models, and local models. However, the cheapest token price is not always the cheapest completed task.

Can OpenClaw use local AI models?

Yes, in supported setups. Local models are useful for privacy and cost control, but they may require stronger hardware and more configuration.

Should I use one model or multiple models with OpenClaw?

For serious use, multiple models are usually better. Use cheaper models for routine tasks and stronger models for complex, sensitive, or business-critical work.

Why can OpenClaw become expensive?

OpenClaw workflows can trigger multiple calls for planning, tool use, summarization, memory, retries, and final output. That is why cost per completed task matters more than token price.

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Final answer

OpenClaw uses the AI model you connect to it. It can work with ChatGPT, Claude, Gemini, OpenRouter, DeepSeek, Kimi, MiniMax, Groq, and local models.

For most real use cases, the best setup is hybrid:

• Use cheap or local models for routine tasks
• Use ChatGPT or mid-tier models for general assistant work
• Use Claude Sonnet or GPT-4-class models for complex reasoning and business workflows
• Require human approval before external actions
• Measure success by cost per completed task, not cost per token

That is the practical answer behind “Which AI does OpenClaw use?” OpenClaw is model-flexible, but the best results come from choosing the right AI for each task.