Best AI Models for OpenClaw: Ranked & Compared (2026)

OpenClaw supports 8+ model families -- from Claude and GPT to free local models via Ollama. Here's every option ranked by quality, cost, and speed, with real pricing data for February 2026.

The best AI model for OpenClaw in 2026 is Claude Sonnet 4.5 for quality-focused users ($3/$15 per million tokens) and GPT-4o mini for budget-focused users ($0.15/$0.60 per million tokens). OpenClaw supports Claude (Anthropic), GPT (OpenAI), Gemini (Google), DeepSeek, Grok (xAI), Llama, Mistral, and any local model through Ollama or LM Studio. You can switch between them at any time with a single command.

Below is a complete comparison of every model OpenClaw supports -- including pricing, quality ratings, context window sizes, and real-world performance notes. Plus: how to configure models, set up fallbacks, and pick the right one for your use case.

Which Models Does OpenClaw Support?

As of February 2026, OpenClaw supports every major AI model family through a unified configuration layer. You bring your own API key (or run a local model), and OpenClaw handles the rest -- prompt formatting, token counting, streaming, and error handling.

Here's the full list:

• Claude (Anthropic) -- Opus 4.6, Sonnet 4.5, Haiku 4.5. Best overall quality and reasoning.
• GPT (OpenAI) -- GPT-4o, GPT-4o mini. Strong all-rounders with wide ecosystem support.
• Gemini (Google) -- Gemini 2.0 Flash, Gemini 2.0 Pro. Free tier available, good for high-volume.
• DeepSeek -- DeepSeek V3. Cheapest cloud option at ~$0.27/M input tokens.
• Grok (xAI) -- Grok 2, Grok mini. Competitive pricing, growing ecosystem.
• Llama (Meta) -- Llama 3.1 8B, 70B, 405B. Free open-weight models, run locally or via API.
• Mistral -- Mistral Large, Mistral Small, Mixtral. European alternative with strong multilingual support.
• Local models via Ollama -- Any GGUF model. Free, private, runs entirely on your hardware.
• Local models via LM Studio -- GUI-based alternative to Ollama. Easier setup for beginners.

You configure your model in config.yaml or through the onboarding wizard when you first run OpenClaw. Switching later takes one command.

Full Model Comparison Table

Every model ranked by quality, speed, cost, and context window. Prices are per million tokens as of February 2026 (Source: official provider pricing pages).

Best Model for Quality: Claude Sonnet 4.5

If you want the smartest, most reliable responses from your OpenClaw agent, Claude Sonnet 4.5 is the model to pick. It sits at the sweet spot of Anthropic's lineup -- nearly as capable as the flagship Opus 4.6, but roughly 40% cheaper and noticeably faster.

Why Sonnet wins for quality:

• 200K context window -- the largest of any mainstream model. OpenClaw can hold long conversation histories without truncation.
• Excellent instruction following -- critical for OpenClaw's personality system. Sonnet stays in character, follows SOUL.md rules, and handles complex multi-step instructions reliably.
• Strong reasoning -- handles nuanced customer questions, ambiguous requests, and creative tasks better than any model in its price range.
• Prompt caching support -- Anthropic's caching gives up to 90% off repeated system prompts, which is exactly how OpenClaw works. This brings the effective cost down significantly.

At $3/$15 per million tokens, expect to spend $15-25/month at moderate volume (50-200 messages per day). That's roughly $0.10-0.15 per conversation turn -- less than a text message in many countries.

For users who need the absolute best quality and have the budget, Claude Opus 4.6 at $5/$25 is the top of the line. But the quality difference over Sonnet is marginal for most OpenClaw use cases.

Best Model for Budget: GPT-4o mini

At $0.15 per million input tokens and $0.60 per million output tokens, GPT-4o mini is absurdly cheap. A user processing 100 messages per day would spend roughly $2-4/month. For budget-sensitive OpenClaw deployments, nothing else comes close on price-to-quality ratio.

What GPT-4o mini handles well:

• Simple Q&A -- product inquiries, FAQs, status checks
• Template-based responses -- order confirmations, scheduling, routing
• Basic conversation -- friendly chat, greetings, small talk
• High-volume bots -- when you need thousands of responses per day without breaking the bank

Where it struggles compared to Sonnet or GPT-4o:

• Nuanced instructions -- sometimes ignores subtle system prompt rules
• Complex reasoning -- multi-step logic or ambiguous requests get worse answers
• Creative writing -- noticeably more generic and formulaic
• Staying in character -- drifts from personality more often over long conversations

The community recommendation: start with GPT-4o mini. If quality isn't good enough for your specific use case, upgrade to Sonnet. Many users find mini handles 70-80% of their conversations just fine.

Other strong budget options include DeepSeek V3 ($0.27/$1.10) and Grok mini ($0.20/$0.50). DeepSeek is the cheapest cloud option overall, though response quality varies more than mini -- some conversations are excellent, others miss the mark.

Best Model for Privacy: Local Models via Ollama

If your data must never leave your machine -- for legal, compliance, or personal reasons -- local models are the only option. OpenClaw supports two local model runners: Ollama (CLI-based) and LM Studio (GUI-based).

Ollama

Ollama is the most popular local model runner in the OpenClaw community. It's free, open source, and runs on macOS, Linux, and Windows. You install it, pull a model, and point OpenClaw at it.

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model
ollama pull llama3.1:70b

# Configure OpenClaw to use it
openclaw models set ollama/llama3.1:70b

The most popular local models for OpenClaw:

• Llama 3.1 8B -- Runs on 8GB+ RAM. Fast, decent quality. Good starting point.
• Llama 3.1 70B -- Needs 40GB+ RAM. Significantly better quality. Comparable to GPT-4o mini for many tasks.
• Mistral 7B -- Runs on 8GB+ RAM. Good multilingual support. Popular in Europe.
• Mixtral 8x7B -- Needs 32GB+ RAM. Mixture-of-experts architecture. Strong for its size.

LM Studio

LM Studio is a desktop application that provides a graphical interface for downloading and running local models. It's free, works on macOS and Windows, and is easier to set up than Ollama if you prefer GUIs over terminals.

To use LM Studio with OpenClaw: launch LM Studio, download a model, start the local server, then configure OpenClaw to point at http://localhost:1234 (LM Studio's default port). OpenClaw treats it like any other OpenAI-compatible API.

LM Studio's advantage over Ollama is discoverability -- it has a built-in model browser where you can search, filter, and preview models before downloading. The downside is it uses more system resources for the GUI and doesn't support headless server deployments.

Best Model for Speed: Gemini 2.0 Flash

Google's Gemini 2.0 Flash is the fastest model OpenClaw supports, with time-to-first-token under 200ms in most cases. It also has the largest context window at 1 million tokens -- useful if your OpenClaw agent needs to reference long documents or maintain very long conversation histories.

The free tier is generous enough for testing and light personal use. For production, paid pricing is competitive at roughly $0.10/$0.40 per million tokens (Source: Google AI pricing page, February 2026).

Flash's weakness is reasoning depth. It's optimized for speed over thoughtfulness -- quick answers are usually correct, but complex multi-step questions get shallower treatment than Claude or GPT-4o. For chatbots that need fast, simple responses at high volume, it's excellent. For agents that need to think carefully, look elsewhere.

How to Configure Models in OpenClaw

OpenClaw provides three ways to set your model:

1. Onboarding Wizard

When you first install OpenClaw, the setup wizard asks which model you want to use and walks you through API key configuration. This is the easiest method for first-time users.

2. CLI Command

# Set your primary model
openclaw models set claude-sonnet-4.5

# List all available models
openclaw models list

# Show current model configuration
openclaw models show

3. Edit config.yaml Directly

# ~/.openclaw/config.yaml
model:
  primary: claude-sonnet-4.5
  fallback: claude-haiku-4.5
  maxOutputTokens: 1024
  temperature: 0.7
  caching: true

api_keys:
  anthropic: sk-ant-xxxxx
  openai: sk-xxxxx
  google: AIzaSy-xxxxx

You can switch models at any time without losing conversation history. OpenClaw normalizes the message format across providers, so switching from Claude to GPT (or vice versa) is seamless.

Fallback Model Setup

OpenClaw supports a fallback model that automatically activates when your primary model is unavailable. This is critical for production bots that can't afford downtime.

Common scenarios where fallback kicks in:

• Rate limiting (HTTP 429) -- your primary model's API hits its requests-per-minute limit
• API outage -- the provider is temporarily down
• Timeout -- the primary model takes too long to respond
• Budget cap hit -- you've reached your daily spend limit for the primary model

To configure a fallback, add it to your config.yaml:

model:
  primary: claude-sonnet-4.5
  fallback: claude-haiku-4.5

The most popular fallback combinations in the community:

Context Window: Why It Matters

The context window is how much text the model can "see" at once -- including the system prompt, conversation history, and the current message. It directly affects how well your OpenClaw agent remembers past conversations.

For most OpenClaw users, 128K+ is more than enough. The system prompt files (SOUL.md, IDENTITY.md, etc.) typically use 2,000-5,000 tokens. That leaves 123K+ for conversation history -- hundreds of messages.

Local models are the exception. With 8K-32K context windows, your agent forgets earlier messages after 10-30 exchanges. OpenClaw compresses history automatically, but you'll notice the agent "forgetting" things in long conversations. Set memory.maxHistory in your config to control how aggressively OpenClaw truncates.

Model Recommendations by Use Case

Here's a quick decision guide based on what you're building with OpenClaw:

How to Switch Models

Switching models in OpenClaw takes about 10 seconds:

# Option 1: CLI command
openclaw models set claude-haiku-4.5

# Option 2: Edit config.yaml
# Change the "primary" field under "model"

# Option 3: Environment variable (temporary)
OPENCLAW_MODEL=gpt-4o-mini openclaw start

OpenClaw normalizes message formats across providers. Your conversation history, system prompts, and skills all work the same regardless of which model you use. The only things that change are response quality, speed, and cost.

If you're switching to a model from a different provider (e.g., Claude to GPT), make sure you've added the new provider's API key to your config.yaml or environment variables first.

Frequently Asked Questions

Can I use multiple models simultaneously?

Not in the same conversation, but you can configure different models for different channels. For example, your Telegram bot could use Sonnet while your Discord bot uses GPT-4o mini. OpenClaw's channel-specific config overrides let you set a different model per channel in config.yaml.

Do I need separate API keys for each model?

You need one API key per provider, not per model. One Anthropic key covers Opus, Sonnet, and Haiku. One OpenAI key covers GPT-4o and GPT-4o mini. Local models through Ollama need no API key at all.

Which model does the OpenClaw community recommend most?

Based on GitHub discussions and Discord conversations in the OpenClaw community (originally built by Peter Steinberger, formerly Clawdbot, then Moltbot): Claude Sonnet 4.5 for quality, GPT-4o mini for budget, and Ollama with Llama 3.1 for privacy. About 55% of active users run Anthropic models, 25% run OpenAI, and 20% run local or other providers (Source: OpenClaw community survey, January 2026).

Will my system prompt work with all models?

Yes, but quality of instruction-following varies. Claude models are the best at following detailed SOUL.md personality instructions. GPT-4o is close behind. Smaller or cheaper models (mini, Haiku, local) sometimes ignore subtle instructions or drift from character over long conversations. Test your system prompt with each model before going live.

How often should I re-evaluate my model choice?

Every 2-3 months. Model pricing changes frequently -- GPT-4o's price dropped 50% in late 2025, for example. New models launch every few weeks. The OpenClaw team updates model support within days of major releases. Check openclaw models list periodically to see newly supported options.