What Is the Best AI Model for Test Automation?

Liviu Lupei

Founder & Solutions Architect, Endtest · May 20, 2026

Choosing the best AI model for test automation is not a matter of taste.

It should not be based on which chatbot gave the most impressive demo, which model is currently trending on social media, or a few prompts that worked well in a proof of concept.

The best AI model for test automation has to be measured.

And it has to be measured against the realities of software testing:

• Can it create useful automated tests quickly?
• Can it identify the right web elements?
• Can it understand element states?
• Can it choose reliable selectors?
• Can it avoid hallucinating actions, locators, assertions, or expected results?
• Can it maintain tests when the application changes?
• Can it do that at a cost that makes sense at scale?
• Can it produce output that humans can inspect, edit, and trust?

That last part matters.

A model can look impressive in a chatbot window and still be a poor fit for production-grade test automation.

The best model depends on the task

There is no universal "best AI model" for every test automation task.

A model that is strong at writing long explanations may not be the best model for identifying the correct button in a complex DOM. A model that is good at generating Playwright code may not be the best model for self-healing a broken locator. A model that produces beautiful reasoning may be too slow or too expensive for repeated testing workflows.

Test automation breaks down into many smaller tasks:

A general-purpose foundation model may perform well in some of these areas and poorly in others.

That is why "Which model is smartest?" is the wrong question. The useful question is "Which model performs best on the exact test automation task we need to solve?"

How to measure the best AI model for test automation

A useful benchmark should evaluate several dimensions at the same time.

If you only measure accuracy, you may choose a model that is too slow or too expensive. If you only measure cost, you may choose a model that creates unreliable tests. If you only measure speed, you may choose a model that produces hallucinations.

A serious benchmark should include at least four dimensions.

1. Speed

Speed matters because test automation is an interactive workflow.

When a user asks AI to create a test, update a step, identify an element, or repair a locator, the answer should arrive quickly.

Slow AI changes behavior.

If the AI takes too long, users stop relying on it. They go back to manual work. Or worse, they accept a lower-quality result because waiting for another attempt is painful.

Speed also matters during maintenance. If a locator breaks and AI assistance takes too long, the test suite becomes slower to debug and harder to trust.

General foundation models can be powerful, but they are often built to support a very wide range of tasks: writing, coding, reasoning, summarization, translation, image understanding, planning, customer support, and more.

That breadth comes with overhead.

A specialized model that only needs to solve test automation problems can often be much faster because it is optimized for a narrower domain.

2. Reliability

Reliability is more important than raw intelligence.

In test automation, the AI does not need to write a poem, debate philosophy, or generate a complete business strategy. It needs to do specific things correctly and repeatedly.

For example:

• Click the correct "Sign in" button, not another button with similar text.
• Verify that checkout succeeded, not merely that the page changed.
• Extract the actual SMS code, not a random number nearby.
• Repair a locator without changing the business intent of the test.
• Avoid selecting elements that are hidden, disabled, covered, or irrelevant.

A reliable model should behave consistently across many similar tasks.

That is different from a model that occasionally gives an impressive answer.

For testing, consistency wins.

3. Hallucination rate

Hallucinations are especially dangerous in test automation.

In a normal chatbot conversation, a hallucination might produce a wrong explanation or an imaginary fact. That is bad, but the user can sometimes notice it.

In test automation, hallucinations can create false confidence.

The AI might:

• Invent a selector that does not exist.
• Click an element that looks plausible but is not the right one.
• Add an assertion that checks the wrong outcome.
• Claim that a test passed for the wrong reason.
• "Fix" a broken step by changing the test intent.
• Treat a visual issue as acceptable when it is actually a defect.
• Produce a false positive and allow a real bug to reach production.

That last risk matters.

If AI is directly involved in deciding whether a test passed during execution, and the AI hallucinates, the result becomes harder to trust.

A safer architecture is to use AI during creation, maintenance, assertions, visual analysis, and failure investigation, while keeping the actual execution deterministic whenever possible.

When a test runs, the system should follow clear steps and expected outcomes. AI should assist when something needs interpretation or repair, not casually reinterpret the test on every run.

4. Cost

Cost is not a secondary detail.

Most AI providers price API usage based on tokens, including input tokens and output tokens. OpenAI, Anthropic, Google, and DeepSeek all publish token-based API pricing for their models.

That matters because test automation can become token-heavy very quickly.

If you ask a general model to modify a Playwright test, it may need to process:

• The test file
• Page object files
• Shared fixtures
• Helper methods
• Configuration files
• Previous failure logs
• Screenshots or traces
• Related selectors
• Package files
• CI logs

Even a small change can require a large amount of context.

That is expensive.

And as test suites grow, the cost grows with them. More files, more prompts, more retries, more debugging sessions, more AI calls.

This is one of the reasons why AI-generated test code can become surprisingly expensive to maintain. The first few tests may be cheap. The full workflow around them may not be.

A specialized AI test automation system can be more efficient because it does not need to process an entire general-purpose codebase for every small testing task.

It can operate on structured test data, page information, element metadata, execution history, screenshots, and test steps in a more focused way.

That is a major difference.

General foundation models are broad by design

Models like GPT, Claude, Gemini, DeepSeek, and other foundation models are broad AI systems.

They are designed to help with many categories of work:

• Writing
• Coding
• Summarization
• Translation
• Reasoning
• Customer support
• Data extraction
• Brainstorming
• Image understanding
• Research assistance
• Agentic workflows

That breadth is useful.

It is also the reason they are not always the best fit for test automation.

Test automation has its own domain-specific requirements. The model needs to understand web pages, DOM structures, element states, selectors, waits, assertions, browser differences, test data, execution context, and maintenance patterns.

A general model may know about these topics. But knowing about them is not the same as being optimized for them.

This is why a general model can sound confident while still making a poor testing decision.

It may choose a selector that is technically valid but unstable. It may prefer visible text when a more reliable attribute is available. It may click the wrong element because two buttons look similar. It may generate code that works for the demo but becomes difficult to maintain after a few months.

These models are not bad. They are not specialized enough for every testing task.

Why general models often hallucinate in testing workflows

General foundation models are trained to predict useful responses across many contexts.

That makes them flexible, but it can also make them too willing to fill gaps.

In test automation, filling gaps is dangerous.

If a model does not have enough information, it should not invent a selector. If the page source does not contain an element, it should not pretend it exists. If a screenshot is ambiguous, it should not guess with high confidence. If the test intent is unclear, it should not silently change the scenario.

But general models are often optimized to be helpful and conversational. That can create problems.

You have probably seen this behavior already outside of testing. A generated article may look polished but feel generic. A generated image may look impressive at first glance but have obvious flaws. A generated email may sound fluent but not quite human. A generated answer may begin with "You're absolutely right" even when the situation is more nuanced.

That is a meme, but it is also a symptom of a model trying to be broadly helpful.

In test automation, broadly helpful is not enough.

A testing model needs to be precise, skeptical, and constrained.

It should prefer saying "I cannot determine the correct element from this information" over confidently choosing the wrong one.

Would you fly in a plane piloted by a general chatbot?

This is a useful way to think about specialization.

A general foundation model may be impressive. It may write code, summarize documents, explain architecture, and generate test ideas.

But would you want a plane piloted by a general chatbot?

Probably not, and not because the chatbot is useless or general AI is unimpressive. Aviation requires systems designed, tested, constrained, certified, and optimized for aviation.

The same principle applies at a smaller scale in test automation.

You do not need the broadest possible model. You need the model and system that are best suited for the job.

In testing, the job is more specific than "generate a plausible answer":

• Understand the page.
• Understand the user intent.
• Select the right element.
• Create a maintainable test.
• Avoid false positives.
• Avoid false negatives.
• Keep execution reliable.
• Help humans trust the result.

That requires specialization.

Why specialized AI is faster and more reliable

A specialized AI model is not necessarily "smarter" in every possible way.

It may not be better at writing poetry. It may not be better at summarizing legal documents. It may not be better at discussing history, finance, or philosophy.

But it can be better at test automation because it is optimized for test automation.

That is the important distinction.

Specialization improves the system in several ways.

It reduces irrelevant reasoning

A general model has to consider many possible interpretations of a prompt. A specialized testing model can operate inside a narrower context.

If the task is to identify a button, generate a locator, or repair a test step, the model does not need to reason about every possible domain. It needs to reason about web automation.

That narrower scope can make the result faster and more consistent.

It improves selector decisions

Reliable selectors are one of the most important details in test automation.

A bad selector may work today and fail tomorrow. A good selector can survive layout changes, text changes, and component updates.

A specialized testing model can be tuned to prefer selector strategies that work better in real automation scenarios.

For example, it can learn to avoid fragile paths, ignore irrelevant generated classes when better attributes exist, and consider element visibility, state, role, text, surrounding context, and prior execution history.

It reduces hallucinations

A specialized model can be trained and constrained to behave differently from a general chatbot.

It can be designed to avoid inventing elements, selectors, or assertions. It can be taught to return uncertainty when the page context is insufficient. It can use structured inputs and outputs instead of free-form guesses.

That matters because test automation does not reward creativity in the same way as content generation.

It rewards correctness.

It improves cost efficiency

A specialized model can often solve the task with less context and fewer tokens.

Instead of feeding a general model an entire repository, a specialized testing system can provide the exact information needed for the task:

• Relevant page structure
• Element candidates
• Screenshot context
• Test step history
• Previous successful locators
• Execution logs
• Known backup locators
• Assertion targets

That makes the AI workflow more efficient.

Which model is used matters, and so does how the system around the model is designed.

The architecture matters as much as the model

Choosing the right AI model is only part of the equation.

The engineering around the model can give it superpowers, or it can make it slower, more expensive, and less reliable.

A weak architecture asks a general model to do everything.

A strong architecture gives the model the right context, the right constraints, the right tools, and the right output format.

For test automation, this includes:

• How the page source is processed
• How screenshots are interpreted
• How candidate elements are ranked
• How selectors are generated
• How element visibility and state are evaluated
• How prior executions are used
• How backup locators are stored
• How assertions are represented
• How AI output is validated
• How humans review and edit the result
• How deterministic execution is preserved

This is where many AI test automation experiments fail. They focus only on the model, when the full system is the product.

The problem with using Claude to generate Playwright code

A common workflow today looks like this:

1. Ask Claude, GPT, or another model to generate a Playwright framework.
2. Store the generated code in GitHub.
3. Ask the model to add more tests.
4. Ask the model to fix failing tests.
5. Ask the model to update selectors.
6. Ask the model to refactor the framework.
7. Repeat.

This can work in the beginning.

The first demo may look excellent.

But over time, several issues appear.

The model needs more and more context

As the framework grows, the AI needs to understand more files before making safe changes.

A small request like "fix this checkout test" may require the model to inspect the test file, helper functions, page objects, fixtures, configuration, CI logs, and previous changes.

That increases token consumption.

And because most AI providers bill based on token usage, larger context means higher cost.

The tests become harder for humans to read

AI-generated Playwright or Selenium code may be valid, but that does not mean the whole team can understand it.

A product manager cannot easily review the business intent of a generated test by reading code. A manual tester may not feel comfortable editing selectors, fixtures, or helper functions. A support engineer cannot quickly confirm whether the test covers the customer issue they reported.

The result is low adoption.

And low adoption is one of the classic reasons internal Selenium and Playwright frameworks struggle inside companies.

Maintenance depends on AI again

If the team does not fully understand the generated code, they become dependent on AI to maintain it.

That creates a strange loop:

• AI creates the framework.
• The team does not fully understand the framework.
• The framework breaks.
• The team asks AI to fix it.
• The framework becomes more complex.
• The team understands it even less.

That is not a healthy long-term strategy.

AI should reduce maintenance burden, not create a system that requires constant AI intervention to remain usable.

Code is not always the best output for test automation

Code is powerful.

But for many end-to-end testing workflows, code is not the most accessible output.

If the goal is to democratize test automation, the output should be understandable by more than just developers.

That is why generating tests as human-readable steps is often better than generating raw test code.

A human-readable test can be reviewed by QA, developers, product managers, support teams, and business stakeholders.

It can show the intent clearly:

That is easier to understand than a generated code file with imports, fixtures, helper functions, selectors, waits, and assertions.

This is not an argument against code.

Developers should absolutely write unit tests, integration tests, API tests, and lower-level checks in code.

But for business-critical end-to-end workflows, human-readable automation often leads to better team adoption.

And adoption is what makes test automation successful long term.

Why Selenium and Playwright frameworks often struggle in companies

Selenium and Playwright are useful technologies.

Selenium has been widely used for browser automation for many years. Playwright is a strong modern framework with good developer experience, auto-waiting, browser automation APIs, and useful debugging tools.

Selenium and Playwright are not the problem. Internal code-first frameworks are difficult to scale across a whole team.

Many companies follow the same pattern:

1. A proof of concept looks great.
2. A few tests are created quickly.
3. The team is excited.
4. The suite grows.
5. The application changes.
6. Locators start breaking.
7. Test data becomes messy.
8. CI failures become noisy.
9. Only one or two people understand the framework.
10. Other team members avoid touching the tests.
11. Maintenance becomes slower and more expensive.
12. The automation project loses trust.

This pattern has been discussed across industry research and quality engineering reports for years. The DORA research on test automation frames automated testing as an engineering capability connected to delivery performance rather than a tooling choice. The World Quality Report continues to show that quality engineering, automation, AI, and human-in-the-loop systems are deeply connected. The State of Testing report also tracks the growing role of AI and automation in testing, and the human side of the profession.

Test files existing does not make test automation successful. It succeeds when the team uses the tests, trusts the tests, maintains the tests, and keeps them in sync with development.

That requires adoption.

And adoption is much easier when tests are readable, editable, and accessible to the whole team.

AI should not be involved in every execution decision

One of the most important architectural choices in AI test automation is deciding when AI should be used.

AI is useful for:

• Creating tests
• Suggesting steps
• Generating assertions
• Extracting variables
• Repairing broken locators
• Explaining failures
• Comparing visual differences
• Importing tests from other formats

But when a test is executing, AI should not casually reinterpret every step.

The test should run deterministically.

If the test says "Click the Submit button," the system should click the Submit button that was defined by the test logic. It should not ask an AI model to decide from scratch on every run, unless there is a failure or ambiguity that needs assistance.

That restraint matters because AI can hallucinate.

If AI is directly involved in deciding whether a test passed or failed on every execution, the team may not fully trust the result.

A false failure wastes time.

A false pass is worse.

A false pass can send a real defect to production.

That is why the safest approach is to combine AI assistance with deterministic execution.

AI helps create and maintain the test. The test itself remains stable, inspectable, and repeatable.

The best AI model is not always the largest model

There is a common assumption that the largest or most expensive model must be the best model.

That is not always true.

For test automation, a smaller specialized model can outperform a larger general model on the specific tasks that matter.

This is normal in software engineering.

A specialized database can outperform a general storage system for a certain query pattern. A specialized image model can outperform a general model for a specific visual inspection task. A specialized static analyzer can outperform a general coding assistant for a narrow class of code quality checks.

The same principle applies to AI test automation.

The best model is the one that produces the best measured outcome for the task, not the one with the most impressive general benchmark.

For test automation, the outcome that matters is a reliable test, not a nice paragraph.

How Endtest evaluates AI for test automation

Endtest has been focused on test automation since 2016.

We introduced AI-powered self-healing tests in 2017, long before the current wave of generative AI tools became mainstream. Since then, our approach has always been practical: AI should make test automation more reliable, not more mysterious.

Over the last two years, we have studied and benchmarked many AI models and approaches.

That included general foundation models, newer coding models, open models, commercial APIs, and specialized approaches. We have also continued to benchmark new foundation models as they appear, because the options change quickly.

Our conclusion has been consistent:

General-purpose models are impressive, but they are usually too imprecise, too slow, or too expensive for many production test automation workflows.

They are not bad models. The task is specialized.

Automated testing requires precision around web elements, selectors, browser behavior, page state, assertions, execution history, and maintenance. A model that is excellent for general reasoning may still be inefficient for those narrow, repeated tasks.

That is why Endtest uses AI models specialized in test automation.

We are not using the typical general models that people usually hear about in chatbot discussions. Endtest does not rely on OpenAI, Anthropic, or a public GitHub model as the core engine for its AI test automation capabilities.

We are not claiming that our AI is "smarter" in every possible area. The narrower, more useful claim is that Endtest AI is designed to be faster, more reliable, and more cost-efficient for creating and maintaining automated tests.

That specialization is what allows us to offer Unlimited AI, so users do not have to think about tokens, credits, or surprise AI invoices every time they create, maintain, or improve tests.

Why Unlimited AI matters

Unlimited AI is a pricing feature, but it also changes how users behave.

When AI usage is metered by tokens or credits, users naturally become cautious.

They may think twice before generating another test. They may avoid asking AI to improve an assertion. They may hesitate to run another maintenance attempt. They may worry that normal testing work will create an unexpected bill.

That is not ideal.

AI should make test automation easier, not introduce a new cost anxiety.

Endtest is built so users can rely on AI throughout the testing lifecycle:

• Creating tests
• Maintaining tests
• Generating assertions
• Working with variables
• Improving stability
• Importing existing tests
• Understanding failures
• Reducing repetitive work

Because our AI is specialized and the architecture is optimized around test automation, we can make that usage predictable.

That matters for teams that want to scale.

How Endtest uses AI across the testing lifecycle

The best AI model for test automation is not useful by itself. It needs to be part of a complete testing platform.

Endtest uses AI across multiple areas of the product.

AI Test Creation Agent

The Endtest AI Test Creation Agent can create automated tests from plain-English instructions.

The output is a normal Endtest test made of clear, editable steps, not an opaque code file.

That matters because the team can inspect the result, improve it, and understand what is being tested.

AI Assertions

AI Assertions help users validate complex page conditions using natural language.

Instead of manually building every low-level check, users can describe what should be true.

This is useful for pages where the expected outcome is easier to express in business language than in selectors.

AI Variables

AI Variables help extract dynamic values from pages, emails, SMS messages, documents, and other test artifacts.

This is useful for flows involving confirmation codes, order IDs, invoice numbers, temporary passwords, magic links, and other dynamic data.

Visual AI

Visual AI helps detect meaningful visual differences.

Traditional assertions can confirm that text exists, but they may miss layout problems, broken components, overlapping elements, or visual regressions.

Visual AI helps cover that gap.

Self-healing tests

Endtest includes self-healing capabilities powered by AI.

When an element changes, Endtest can use stored backup locators and AI-assisted repair to help keep tests stable.

This reduces the maintenance burden that often affects Selenium and Playwright suites.

Email and SMS testing

Modern end-to-end testing often needs to go beyond the browser.

Endtest supports email testing and SMS testing, allowing teams to validate flows such as:

• Account confirmation
• Password reset
• Magic links
• User invitations
• Two-factor authentication
• SMS verification
• Notification workflows

This is important because real user journeys often involve more than clicking through a website.

Cross-browser cloud execution

Endtest runs tests in the cloud on real browsers on Windows and macOS machines.

That includes real Safari on macOS machines.

This matters because browser behavior can differ in production. The official Playwright documentation says Playwright runs Chromium, Firefox, and WebKit. WebKit support is useful, but it is not the same as testing on the branded Safari browser on real macOS machines.

If your customers use Safari, real Safari testing matters.

The best AI testing system combines specialization with readability

The best AI model is only one ingredient.

A reliable AI test automation system also needs:

• Structured test steps
• Human-readable output
• Deterministic execution
• Reliable cloud infrastructure
• Strong debugging artifacts
• Cross-browser support
• Maintenance assistance
• Email and SMS testing
• Visual validation
• Team collaboration
• Predictable pricing
• Good customer support

This is why asking "Which model is best?" on its own can be misleading. The question to ask is "Which AI testing system produces the most reliable results for our team?"

A general model might generate a decent test once.

A specialized test automation platform should help your team create, run, maintain, debug, and improve tests every week.

That is the difference.

How to choose the best AI model for your team

If you are evaluating AI models or AI testing tools, use a practical benchmark.

Do not only test with one happy-path example.

Use real workflows from your product.

Test creation benchmark

Ask the AI to create tests for several real user journeys:

• Signup
• Login
• Password reset
• Checkout
• Subscription upgrade
• Account settings
• Email confirmation
• SMS verification
• File upload
• Report generation

Measure:

• How long it takes
• How accurate the generated test is
• How much editing is needed
• Whether the assertions are meaningful
• Whether non-technical team members can understand the output

Locator benchmark

Give the AI real page sources and screenshots.

Ask it to identify target elements such as:

• Buttons
• Inputs
• Dropdowns
• Modals
• Hidden elements
• Disabled elements
• Similar-looking elements
• Elements inside dynamic components

Measure:

• Correct element selection
• Selector stability
• False positives
• Ability to say "not enough information"
• Performance on changed UI

Maintenance benchmark

Break existing tests intentionally.

Change labels, move elements, update CSS classes, modify layout, or add new wrappers.

Then measure:

• Whether the AI repairs the test correctly
• Whether it preserves the original intent
• Whether it introduces new fragility
• How many attempts are needed
• How much human review is required

Cost benchmark

Measure cost at the workflow level rather than the model call level.

Include:

• Test creation
• Revisions
• Debugging
• Maintenance
• Failure analysis
• Context loading
• Retries
• Prompt overhead
• Long-term usage

This gives a more realistic picture than comparing one prompt against another.

Adoption benchmark

Ask people outside the automation team to review the generated tests.

Can QA understand them? Can product managers validate the business intent? Can developers debug failures? Can support teams confirm that customer issues are covered?

If the answer is no, the tool may not scale across the organization.

The answer: the best AI model is specialized, measured, and productized

So, what is the best AI model for test automation?

The best model is not necessarily the largest, the most famous, or the one that performs best in a generic coding benchmark.

The best AI model for test automation is the one that performs best on the actual testing tasks your team needs:

• Fast test creation
• Accurate element detection
• Reliable selector generation
• Low hallucination rate
• Useful assertions
• Efficient maintenance
• Predictable cost
• Human-readable output
• Stable execution

In many cases, that means specialized AI models and specialized test automation systems will outperform general-purpose foundation models, not because they are more impressive in every domain but because they are built for this domain.

That is the same reason you use a database for structured queries, a browser engine for rendering pages, and a CI system for delivery pipelines.

Final thoughts

General foundation models are remarkable.

They can write code, explain concepts, summarize documents, generate test ideas, and help developers move faster. For skilled programmers, AI-assisted coding can be useful.

But test automation has different requirements.

It needs speed, reliability, low hallucination rates, stable selectors, meaningful assertions, deterministic execution, readable output, maintainability, and predictable cost.

A generic model can help with parts of that workflow.

A specialized AI test automation system can be designed around the entire workflow.

That is the direction Endtest has taken.

We use AI models specialized in test automation, combined with a platform that turns AI output into clear, editable, deterministic tests that run on real browsers in the cloud.

What makes AI useful in practice is specialized AI, measured against real testing problems and built into a system that helps teams create, run, and maintain reliable automated tests.

If you want to see how that works in practice, explore the Endtest AI Test Creation Agent or compare plans on the pricing page.