Best AI Models to Use with OpenClaw (Comparison Guide)

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When setting up OpenClaw, one of the most important decisions is choosing the AI model that will power your agent.

OpenClaw itself is the system that connects everything together: your messaging channels, automation workflows, and AI tools. But the actual responses and reasoning come from the AI model you select during configuration.

If you open the OpenClaw configuration wizard, you’ll notice that several AI providers are supported. Each model behaves slightly differently depending on speed, reasoning ability, and cost.

In this guide, we’ll look at the AI models shown during the OpenClaw setup process, compare them, and help you understand which option might be best for your use case.

Video Walkthrough

If you'd like to see the setup and model selection visually, watch the walkthrough below.

https://www.youtube.com/watch?v=5bBp9cU-ZqQ

The video demonstrates how OpenClaw interacts with AI models during configuration and how the system works once the model is connected.

Where AI Models Appear in OpenClaw Setup

During the configuration process, OpenClaw asks you to choose a model provider.

At this point, the CLI interface shows a list of supported providers.

From the setup interface shown in the screenshots, the available providers include:

OpenAI

Anthropic

MiniMax

Moonshot AI (Kimi)

Google

OpenRouter

Qwen

Copilot

Venice AI

Vercel AI Gateway

Once you select one of these providers, OpenClaw then asks for an API key so the agent can communicate with that model.

AI Models Supported by OpenClaw

Below is a simplified comparison based on the providers visible in the setup interface.

Provider	Example Model	What It’s Good For
OpenAI	GPT models	General AI tasks and automation
Anthropic	Claude models	Reasoning and long responses
Moonshot AI	Kimi models	Long context and coding tasks
Google	Gemini models	Multimodal workflows
MiniMax	MiniMax models	Fast responses
OpenRouter	Multiple aggregated models	Switching between providers
Qwen	Qwen models	Alternative AI models
GLM	GLM models	AI chat and automation
Copilot	GitHub Copilot models	Coding workflows

This flexibility is one of the biggest advantages of OpenClaw. You’re not locked into a single AI ecosystem.

Model-by-Model Guidance for OpenClaw Agents

The table above tells you what each provider is. It does not tell you how each one behaves once it is wired into an agent that runs tools, reads files, and messages you all day. Here is a closer look at the major options, with API pricing taken from each provider’s official pricing page in July 2026. Prices change often, so confirm before you commit.

Anthropic Claude

Claude is the strongest default for agent work that involves multi-step reasoning, coding, or long unattended sessions. Claude Opus 4.8 ($5 per million input tokens, $25 per million output) is the heavyweight: it follows complex instructions closely and recovers well when a tool call fails halfway through a task. Claude Sonnet 5 ($3 input / $15 output, with introductory pricing of $2 / $10 through August 31, 2026) gets close to Opus quality on most agent tasks at a lower price. Claude Haiku 4.5 ($1 / $5) is the fast, cheap tier for simple replies.

If your OpenClaw agent writes code or chains many tool calls, start with Sonnet and move to Opus only when tasks get long and hard. Opus and Sonnet both offer a 1 million token context window, which matters once an agent accumulates weeks of conversation history.

OpenAI (GPT)

GPT models are the safe generalist pick, and the lineup covers every budget. The flagship GPT-5.6 family runs from gpt-5.6-sol ($5 input / $30 output per million tokens) through gpt-5.6-terra ($2.50 / $15) down to gpt-5.6-luna ($1 / $6). Below that, gpt-5.4-mini ($0.75 / $4.50) and gpt-5.4-nano ($0.20 / $1.25) make high-volume automation genuinely cheap, and gpt-5.3-codex ($1.75 / $14) is tuned for coding.

For a general-purpose assistant on Telegram or WhatsApp, a mid-tier GPT model is hard to beat: dependable tool calling, fast responses, and cached input billed at roughly 10% of the standard rate. That discount matters because an agent re-sends its conversation history on every turn.

Google Gemini

Gemini’s edge is multimodal work and price. Gemini 3.5 Flash ($1.50 input / $9 output per million tokens) is the fast flagship, Gemini 3.1 Pro Preview ($2 / $12 on prompts up to 200k tokens) handles harder reasoning, and Gemini 2.5 Flash-Lite drops to $0.10 / $0.40. Several Flash models also have a free tier on the Gemini API, which makes Google the cheapest way to test an OpenClaw setup before spending anything.

Pick Gemini when your agent works with images and screenshots, or when budget is the hard constraint.

Moonshot AI (Kimi)

Kimi K3 is the long-context specialist: a context window of just over 1 million tokens at $3 per million input tokens and $15 per million output, with cached input billed at $0.30. That cache price is the number to notice. Because an OpenClaw agent re-sends its conversation on every turn, most input tokens in a long session hit the cache, and the effective cost lands well below the sticker price. Moonshot also ships Kimi K2.7 Code, a dedicated coding model, if your agent’s main job is software.

Local and open models (OpenRouter or a custom provider)

The setup list also includes OpenRouter, Qwen, GLM, MiniMax, XAI, Venice AI, and Vercel AI Gateway. OpenRouter deserves special mention: one API key gives you access to hundreds of models, including open-weight options, and you can switch between them without touching your other credentials.

OpenClaw can also point at a custom provider that speaks the OpenAI-compatible API. That is how self-hosters connect local runtimes such as Ollama or vLLM: zero cost per token and full privacy, in exchange for weaker output than frontier hosted models and the work of running your own inference. Our guide to configuring an LLM provider in OpenClaw covers the setup steps.

What Models Cost in an Agent (Not a Chatbot)

Per-token prices look tiny until you run an agent. A single chat message costs a fraction of a cent. An OpenClaw agent is different: every turn re-sends the system prompt, the tool definitions, and the conversation so far, and one “check my inbox and summarize” request can trigger several model calls in a row.

Three things follow from that:

Input tokens dominate. Agents read far more than they write, so cheap input and cached-input discounts (Kimi’s $0.30 cache-hit rate, OpenAI’s roughly 90% cached-input discount) matter more than headline prices.

Output is the premium line. Output tokens cost about five times input on most providers, so verbose models cost more than their sticker price suggests.

Capability can be the cheaper option. A strong model that finishes a task in three tool calls often costs less than a budget model that takes ten calls and a retry.

Here is the July 2026 snapshot, per million tokens, from official provider pricing pages:

Provider	Model	Input	Output
Anthropic	Claude Opus 4.8	$5.00	$25.00
Anthropic	Claude Sonnet 5	$3.00 ($2.00 intro until Aug 31, 2026)	$15.00 ($10.00 intro)
Anthropic	Claude Haiku 4.5	$1.00	$5.00
OpenAI	gpt-5.6-sol	$5.00	$30.00
OpenAI	gpt-5.4-mini	$0.75	$4.50
OpenAI	gpt-5.4-nano	$0.20	$1.25
Google	Gemini 3.5 Flash	$1.50	$9.00
Google	Gemini 2.5 Flash-Lite	$0.10	$0.40
Moonshot AI	Kimi K3	$3.00 ($0.30 cache hit)	$15.00

A practical rule: measure before you optimize. Run a week on the model you actually want, check the provider’s usage dashboard, and only then decide whether to trade capability for price.

How Model Selection Works in OpenClaw

When running the configuration command inside the terminal, OpenClaw asks which sections you want to configure.

Once you select Model, the system shows the list of providers mentioned earlier.

After selecting the provider:

OpenClaw requests the API key

The key is validated

The selected model becomes the default AI engine

From that point forward, every request sent by the OpenClaw agent will use that AI model.

Example Workflow

A typical setup might look like this:

Start OpenClaw configuration

Select Model provider

Choose a provider (for example OpenAI or Anthropic)

Enter the API key

Save the configuration

Once complete, the AI model is ready to process requests from messaging channels such as Telegram or WhatsApp.

Choosing the Right Model

Different users choose different models depending on how they plan to use OpenClaw.

For example:

Use Case	Suggested Model
General chatbot	OpenAI
Advanced reasoning	Anthropic
Long conversations	Kimi
Experimenting with multiple models	OpenRouter
Coding assistance	Copilot

Since OpenClaw allows switching models later, you can experiment with different providers without rebuilding your setup.

Which Model Fits Which OpenClaw Setup

Different jobs stress different parts of a model. Match the model to the job, not to the leaderboard:

Personal assistant on Telegram or WhatsApp: Claude Haiku 4.5, gpt-5.4-mini, or Gemini 2.5 Flash. Replies come back fast, tool calling is reliable, and the cost stays negligible.

Coding agent: Claude Sonnet 5 first, Claude Opus 4.8 for long or difficult tasks. gpt-5.3-codex and Kimi K2.7 Code are credible alternatives worth an afternoon of testing.

Research and long documents: you want a large context window: Kimi K3, Claude Sonnet 5 or Opus 4.8, or Gemini’s Pro tier. Million-token contexts let the agent hold entire reports in memory instead of forgetting page one by page forty.

High-volume automation: scheduled jobs, monitoring, classification. gpt-5.4-nano or Gemini Flash-Lite handle rote work for pennies; save the expensive model for steps that need judgment.

Screenshots and images: Gemini or Claude. Both accept image input.

Privacy-first or zero-budget: a local model through a custom provider. Expect a real capability gap against hosted frontier models, and test your actual workflows before relying on it.

A pattern that works well in practice: run a cheap default for everyday messages, and switch up to a stronger model when a heavy task shows up. OpenClaw makes that switch a two-minute job, which brings us to the next section.

How to Switch Models in OpenClaw

Because switching is cheap, the right strategy is to start with an affordable model and upgrade only when the agent visibly struggles. The short version:

Open your instance’s terminal and start the OpenClaw configuration wizard.

Select Model, choose the provider, and paste that provider’s API key.

Pick the default model and save. Send a test message to confirm the change took.

For a click-by-click walkthrough with screenshots, see our guide on how to change the default AI model in OpenClaw. If you are adding a provider for the first time, or wiring up OpenRouter or a custom endpoint, follow how to configure an LLM provider in OpenClaw instead.

One note if you would rather skip server management: Agent37’s hosted OpenClaw instances are bring-your-own-key on every plan, so your API key goes straight from your instance’s container to the model provider and you pay the provider’s rates directly. Hosting starts at $3.99 a month on the Basic plan. The Plus plan at $9.99 a month also bundles free usage of GPT-OSS 20B and 120B and DeepSeek V4 Flash (usage caps reset every 5 hours), which lets you run an agent without holding any API key at all. There is no free tier, but the bundled models on Plus are the cheapest way to experiment without a per-token bill.

Why OpenClaw Supports Multiple Models

The goal of OpenClaw is flexibility.

Instead of forcing users to rely on a single AI provider, the platform allows multiple integrations so developers can:

test different models

compare response quality

control API costs

adapt to new AI models as they appear

This design makes OpenClaw useful for both experimentation and production systems.

Final Thoughts

Choosing the right AI model is a key step when setting up OpenClaw.

From the configuration interface shown in the setup screenshots, OpenClaw supports a wide range of providers including OpenAI, Anthropic, Moonshot AI, Google, and several others.

The best approach is usually to start with a model you are familiar with, test it inside your workflow, and adjust later if needed.

Because OpenClaw allows you to change models easily, you’re free to experiment until you find the setup that works best for your AI agent.