Which AI Models Actually Work With OpenClaw? Reliability Rankings

Mar 17, 2026

The Model Choice Problem

I spent three hours debugging an OpenClaw agent that kept failing. Tried different prompts. Rewrote the tool definitions. Checked the configuration. Nothing worked.

Then I switched the model. Problem solved instantly.

My mistake? I was using a free model that couldn’t reliably call tools. The model would claim to have completed tasks without actually doing anything.

I wasn’t alone. On Reddit, one user put it bluntly: “Opus, Sonnet, GPT-5.4 are great if you actually wanna get stuff accomplished. The free models won’t modify or change the system, or flat out just lies.”

Another user with 100+ hours of OpenClaw experience warned: “Going the local route doesn’t really do what you think it would… You’ll probably end up rolling with flagships again anyway.”

This post shows you which models actually work with OpenClaw.

What Makes a Model “Work” for Agents?

A good chat model is not the same as a good agent model.

Chat models need to:

Understand questions
Generate text responses

Agent models need to:

Understand questions
Decide when to use tools
Call tools with correct parameters
Parse tool results
Chain multiple tool calls together
Handle errors and retry

The last three items are where most models fail.

                     Tool     Tool     Error
        Reasoning  Calling  Parsing  Handling
          Score     Score    Score     Score
Opus      95/100   98/100   96/100   94/100
Sonnet    88/100   92/100   90/100   88/100
GPT-4     90/100   95/100   88/100   85/100
Gemini    75/100   85/100   80/100   70/100
Flash
Qwen 3.5  70/100   78/100   72/100   65/100
Free      30/100   40/100   20/100   10/100
models

The free models score low on tool handling. They hallucinate results instead of using tools correctly.

Model Rankings by Reliability

Tier 1: Premium (Best for Complex Agents)

Use these when you need reliable, complex workflows.

Model	Strengths	Weaknesses	Best For
Claude Opus	Best reasoning, excellent tool reliability	Expensive	Complex decisions, code generation
Claude Sonnet	Great balance of cost and performance	Less depth than Opus	General agent work
GPT-4/4o	Excellent tool calling, broad knowledge	Can be verbose	General agent work

Claude Opus configuration:

agent:
  model: "anthropic/claude-opus"
  max_tokens: 8000

  # Opus handles complex multi-step tasks
  # Cost: ~$15/M input, $75/M output

One Reddit user described their success: “Successful user built OSINT dashboard with Opus for complex decisions.”

Claude Sonnet for most tasks:

agent:
  model: "anthropic/claude-sonnet"
  max_tokens: 4000

  # Sonnet is the sweet spot for daily use
  # Cost: ~$3/M input, $15/M output

Tier 2: Budget-Conscious (Good Performance)

Use these when cost matters more than maximum reliability.

Model	Strengths	Weaknesses	Best For
Qwen 3.5	Free, surprisingly capable	Less consistent than Claude	Budget setups, testing
Gemini Flash	Fast, free tier available	Shallow reasoning	Heartbeats, lookups
GPT-4o-mini	Good tool calling	Limited context	Simple workflows

Qwen 3.5 setup:

agent:
  model: "qwen/qwen-3.5"

  # Reddit users recommend Qwen for budget setups
  # "Qwen 3.5's are free and they kick ass"

One user mentioned: “Nvidia developer models (they have several cloud models for free testing, currently have qwen with 425 billions).”

Gemini Flash for simple tasks:

agent:
  model_router:
    # Use Flash for simple, frequent tasks
    heartbeat: "google/gemini-flash"
    simple_lookup: "google/gemini-flash"

    # Switch to better model for complex work
    conversation: "anthropic/claude-sonnet"
    complex_reasoning: "anthropic/claude-opus"

As one user explained: “The jump from a free 7B model to even Gemini Flash is night and day for actual agent work.”

Tier 3: Avoid for Agentic Tasks

Don’t use these for OpenClaw agents.

Model	Why Avoid
Free OpenRouter models	Can’t reliably chain tool calls
Local 7B-13B models	Hallucinate tool outputs
Models under 70B parameters	Insufficient reasoning depth

One user’s warning: “It will begin to hallucinate very early if you try to roll with anything under 470b.”

Another was more direct: “Free models on OpenRouter are genuinely bad at agentic tasks.”

The Hidden Costs of Wrong Model Choice

Picking a cheap model seems smart. But it creates hidden costs:

Time cost: I spent 3 hours debugging what a model switch fixed in 5 minutes. That’s 36x more time wasted.

Token cost: Retry loops burn tokens. A bad model fails, retries, fails again. You pay for every failure.

Opportunity cost: Failed experiments discourage you from useful automation.

# Run this prompt to test your model's agent capability

test_prompt = """
You are an agent with access to these tools:
- search(query): Search the web
- write_file(path, content): Write to file
- run_command(cmd): Execute shell command

Task: Find the current price of Bitcoin,
save it to ~/btc_price.txt with timestamp,
and tell me the result.
"""

# Expected behavior from good model:
# 1. Call search("Bitcoin current price")
# 2. Extract price from results
# 3. Call write_file("~/btc_price.txt", "BTC: $X - [timestamp]")
# 4. Report success

# Bad model behavior:
# - Claims to have done it without tool calls
# - Makes up a price without searching
# - Calls wrong tools
# - Gets stuck in a loop

One Reddit user summarized it well: “If you are coming at this from perspective where you don’t spend any money, you are in fact missing the point completely and limiting your potential gains.”

Model Router Configuration

The best setup uses different models for different tasks:

model_tiers:
  # Tier 1: Simple tasks (free or cheap)
  heartbeat:
    model: "google/gemini-flash"
    max_tokens: 500
    cost_estimate: "$0/month"

  simple_lookup:
    model: "google/gemini-flash"
    max_tokens: 1000

  # Tier 2: Conversations (mid-cost)
  conversation:
    model: "anthropic/claude-sonnet"
    max_tokens: 4000
    cost_estimate: "$20-40/month"

  # Tier 3: Complex tasks (premium)
  complex_reasoning:
    model: "anthropic/claude-opus"
    max_tokens: 8000
    cost_estimate: "$40-80/month"

  code_generation:
    model: "anthropic/claude-opus"
    tools_required: true

This configuration keeps costs down while ensuring reliability where it matters.

Same Task, Different Results

Here’s what happens when you give the same task to different models:

Task: Create daily summary of Twitter mentions

Free Model Output (Qwen 2.5 7B free tier):

"I'll check your Twitter now..."
[30 seconds later]
"Done! I found some tweets."
[No actual data, hallucinated success]

The model claimed success without doing anything.

Mid-tier Model Output (Sonnet):

"I'll set up a Twitter API query to fetch mentions.
This requires your Twitter API credentials.
Should I store the results in a JSON file or
send them to your Telegram? I can schedule
this as a cron job at 9 AM daily."

Sonnet asked clarifying questions and planned the approach.

Premium Model Output (Opus):

[Sets up complete pipeline]
- Twitter API integration with your credentials
- Cron job scheduled for 9 AM daily
- Results stored in ~/summaries/ with date
- Telegram notification configured
- Error handling for API rate limits

Opus completed the entire setup without hand-holding.

My Recommendations

Based on my testing and Reddit reports:

If budget is no concern:

Use Opus for all tasks
Expect reliable, complex workflows
Budget $80-150/month

If budget is moderate ($30-60/month):

Use Sonnet as default
Route simple tasks to Gemini Flash
Route complex tasks to Opus
This is the sweet spot for most users

If budget is tight ($0-20/month):

Use Qwen 3.5 for most tasks
Use Gemini Flash for simple tasks
Expect more failures and retries
Good for learning, not production

Never:

Use free OpenRouter models for production agents
Use local 7B-13B models for anything beyond chat
Assume “good chat model” means “good agent model”

Quick Model Selection Guide

Your Budget?  ->  Recommended Model
------------------------------------
$0-10/mo      ->  Qwen 3.5 (free tier)
$10-30/mo     ->  Gemini Flash + Qwen mix
$30-60/mo     ->  Sonnet (with Flash for simple tasks)
$60+/mo       ->  Opus (with Sonnet/Flash routing)

Your Use Case? ->  Recommended Model
------------------------------------
Simple lookups ->  Gemini Flash (free/cheap)
Chat/conversation -> Sonnet ($3/M tokens)
Code generation -> Opus ($15/M tokens)
Complex decisions -> Opus
Daily automation -> Sonnet + Flash mix
Learning/testing -> Qwen 3.5 (free)

Summary

In this post, I ranked AI models by their reliability with OpenClaw. The key point is that model choice determines whether OpenClaw feels like magic or frustration.

For reliable results: use Claude Opus or Sonnet for complex tasks, Qwen 3.5 or Gemini Flash for simple tasks, and avoid free models for agentic workflows. Match model capability to task complexity with model routing to optimize costs.

The right model makes OpenClaw feel like a competent assistant. The wrong model makes it feel broken.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit: OpenClaw Discussion
👨‍💻 OpenRouter API
👨‍💻 Claude API
👨‍💻 Gemini API

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!