Best AI Coding Tools for IntelliJ and JetBrains IDEs in 2026
GitHub Copilot is the strongest starting point for most IntelliJ and JetBrains users in 2026, with a 9.0/10 overall score in the DIY AI code-generation dataset and the highest Integration Ease score of the JetBrains-compatible tools we track.
That does not make it the right integration for every repository. Amazon Q Developer has a clearer role in AWS-heavy development, Windsurf Plugin gives developers a more agent-led workflow without leaving JetBrains, and JetBrains AI Assistant has the closest relationship with the IDE itself.
The useful question, then, is not which coding model is theoretically strongest. It is which JetBrains integration can understand an existing project, make the requested change, avoid touching unrelated code and leave the repository passing its normal checks. This comparison focuses on that decision. Model selection belongs in a separate Best LLM for Coding comparison.
Quick verdict: which JetBrains AI integration should you choose?
| Tool | DIY AI score | Best fit | Decision-changing limitation |
|---|---|---|---|
| GitHub Copilot | 9.0/10 | Most JetBrains developers and GitHub-centred teams | Its JetBrains experience can differ from Copilot in Microsoft’s own editor surfaces |
| Amazon Q Developer | 8.6/10 | AWS-heavy Java and cloud projects | AWS has announced the IDE plugins will lose support on 30 April 2027 |
| Windsurf Plugin | 8.4/10 | Agent-led multi-file work inside JetBrains | The JetBrains plugin does not have the same control over the editor as Devin Desktop |
| JetBrains AI Assistant | 8.2/10 | Native JetBrains workflows and external agent access | Performance can depend on the selected external agent as well as the JetBrains host |
The scores above come from the DIY AI code-generation tools dataset. They measure code accuracy, language support, debugging assistance, integration ease, learning adaptability, repository context, refactoring strength, test generation and documentation generation.
Those scores help narrow the shortlist. Don’t treat them as proof that a plugin will handle your repository correctly. IDE integration introduces another layer of behaviour that a general coding-tool score cannot fully capture.
Test the integration, not just the model
A fair IntelliJ comparison should give every integration the same existing repository and the same bounded task. Blank-file prompts are weak evidence because they remove most of the work an IDE integration is supposed to do: finding relevant code, understanding dependencies, respecting project structure and validating a change.
A useful test is a small behavioural change that crosses several files. For example, add a validation rule to an existing Java service where the implementation, interface, exception handling and tests already exist. Tell each assistant what behaviour must change, but don’t identify every file.
| Check | What good performance looks like | Failure worth recording |
|---|---|---|
| Project understanding | Finds the relevant service, interface and existing tests without being spoon-fed every path | Edits a plausible but unused class or misses an important caller |
| Correct change | Implements the requested behaviour using the repository’s existing patterns | Solves only the obvious path or changes public behaviour that was meant to stay stable |
| Diff discipline | Changes only files required by the task | Reformats unrelated code, edits configuration or creates unnecessary abstractions |
| Test handling | Runs the correct existing test command and adds a meaningful regression test where needed | Claims success without executing tests or writes assertions that do not test the requested behaviour |
| Failure recovery | Uses compiler or test output to make a focused second attempt | Responds to one failure with a broad rewrite |
Record the accepted diff, not generated line count. A six-line change that passes the repository’s checks is usually better evidence than an impressive 200-line agent session followed by 20 minutes of cleanup.
Version control matters in an IntelliJ AI comparison
Plugin behaviour changes quickly enough that a JetBrains AI comparison should record versions alongside its results. Otherwise, tests become hard to reproduce, and older findings can survive long after the integration changes.
For this September 2026 refresh, IntelliJ IDEA 2026.2.3 is the current IDEA maintenance release we verified. Marketplace records checked during the refresh showed the following JetBrains plugin builds. Marketplace packages can vary by IDE build and update channel, so copy the version installed during an actual repository test directly from Settings > Plugins before the run.
| Integration | Version record checked for this refresh | What to record during testing |
|---|---|---|
| GitHub Copilot | 1.13.1-251 | Exact Copilot plugin build plus IntelliJ build |
| Amazon Q | 4.5.262 | Exact Q plugin build, authentication tier and IntelliJ build |
| Windsurf Plugin | 2.12.26 | Exact plugin build, account tier and IntelliJ build |
| JetBrains AI Assistant | 262.9437.148 | AI Assistant build, selected agent and IntelliJ build |
The selected agent also belongs in the test record. Two runs through JetBrains AI Assistant can behave very differently if one uses a JetBrains-provided agent and another uses an external agent through ACP. Calling both results “JetBrains AI” hides the part of the stack that generated the code.
GitHub Copilot: the safest default for most JetBrains users
GitHub Copilot scores 9.0/10 overall and 9.6/10 for Integration Ease in DIY AI’s dataset. That combination makes it the most defensible first installation if you want AI assistance while keeping IntelliJ, PyCharm, WebStorm, Rider or another established JetBrains workflow.
Its advantage is breadth. Completion, chat and agent-oriented work sit in one familiar product, and teams already using GitHub have less account and workflow overhead to introduce.
The limitation is subtler than a missing feature. Copilot’s presence across editors can make developers assume the JetBrains and VS Code experiences are interchangeable. They are not necessarily the same interface with the same release cadence or editor access. This is one reason JetBrains teams should benchmark Copilot inside JetBrains rather than importing conclusions from a VS Code comparison.
Copilot Free currently includes 2,000 code completions per month plus limited chat and agent usage. That is enough to check basic completion behaviour, but a capped free account is a poor basis for a prolonged repository-agent benchmark because usage limits can interrupt the test itself.
GitHub Copilot scores
| Code Accuracy | 9.1/10 | Repository Context | 8.9/10 |
| Integration Ease | 9.6/10 | Refactoring Strength | 8.8/10 |
| Debugging Assistance | 8.9/10 | Test Generation | 8.8/10 |
| Language Support | 9.2/10 | Documentation Generation | 8.9/10 |
Amazon Q Developer: strong for AWS work, but now with a migration date
Amazon Q Developer scores 8.6/10 overall. Its strongest reason to be on this shortlist isn’t generic Java completion. It combines coding assistance with AWS-heavy development, including cloud configuration, AWS SDK work, and specialised transformation workflows.
Its biggest limitation is no longer its 8.5/10 Repository Context score. AWS has announced that support for Amazon Q Developer IDE plugins will end on 30 April 2027, directing users to Kiro for comparable agentic coding, chat, and MCP capabilities. AWS’s Amazon Q Developer IDE plugin end-of-support documentation should factor into any new deployment decision.
That does not make Q irrelevant in September 2026. It does change the economics of choosing it. An existing AWS-heavy team can still get useful work from the plugin, but a team standardising on a new JetBrains assistant should include the 2027 migration in its rollout cost rather than evaluating only today’s features.
The perpetual Free tier currently provides 50 agentic chat interactions per month. IDE use on the Free tier requires a personal Builder ID rather than IAM Identity Centre authentication. Those restrictions matter in a team evaluation because a personal free setup does not reproduce the identity and administration model of a managed deployment.
Amazon Q Developer scores
| Code Accuracy | 8.7/10 | Repository Context | 8.5/10 |
| Integration Ease | 8.7/10 | Refactoring Strength | 8.5/10 |
| Debugging Assistance | 8.8/10 | Test Generation | 8.7/10 |
| Language Support | 8.6/10 | Documentation Generation | 8.6/10 |
Windsurf Plugin: choose it for the JetBrains workflow, not the old Windsurf score
Windsurf Plugin is the current JetBrains extension descended from Codeium. The relevant DIY AI dataset result is therefore the 8.4/10 Codeium row.
The higher 8.8/10 Windsurf result in the same dataset refers to the former standalone Windsurf coding environment. That product is now Devin Desktop. Applying its 8.8 result to the JetBrains plugin would mix two different products and overstate the evidence for the integration this page is comparing.
The distinction also explains something developers encounter in practice. A standalone AI editor controls the editor surface, indexing behaviour and agent interaction model. A JetBrains plugin has to work through the APIs and interfaces JetBrains exposes. The same provider and underlying model can therefore feel materially different in the plugin.
This is where a same-repository test is particularly valuable. Windsurf Plugin should earn its place by locating the right files, producing a controlled multi-file diff and recovering from failed checks inside JetBrains, not because the standalone desktop product has a strong agent reputation.
A free Windsurf plan is currently available, but the public material we could verify does not expose a sufficiently stable JetBrains-plugin-specific monthly allowance to state a numerical plugin quota here. Check the allowance shown in the account used for testing and record it with the result rather than assuming desktop-plan limits map cleanly to the plugin.
Windsurf Plugin scores
| Code Accuracy | 8.5/10 | Repository Context | 8.3/10 |
| Integration Ease | 8.7/10 | Refactoring Strength | 8.5/10 |
| Debugging Assistance | 8.3/10 | Test Generation | 8.1/10 |
| Language Support | 8.8/10 | Documentation Generation | 8.0/10 |
JetBrains AI Assistant: judge the host and the active agent separately
JetBrains AI Assistant scores 8.2/10 in the DIY AI dataset, with its strongest result being 8.9/10 for Integration Ease. That makes sense for teams that value JetBrains-native project navigation, chat and IDE controls.
The product has become harder to score as one monolithic coding assistant, however. AI Assistant can provide access to integrated or external agents, including ACP-compatible tooling. Once you select one of those agents, code quality is no longer attributable solely to JetBrains AI Assistant.
Treat JetBrains as the host layer and record the agent separately. The host controls how context reaches the agent, how changes appear in the IDE and how permissions are exposed. The selected agent handles much of the reasoning and code generation. Collapsing those into one score makes integration tests hard to interpret.
JetBrains AI Free currently provides 3 AI Credits per 30 days. There is an easy-to-miss restriction: AI Free is not available in Community Editions of IntelliJ IDEA or PyCharm, nor in PyCharm Unified when used for free. That makes “free JetBrains AI” a less universal starting point than the name suggests.
Developers experimenting with several agents through JetBrains may also find our OpenCode MCP setup guide useful for understanding how external tools and agent permissions change the workflow around the model itself.
JetBrains AI Assistant scores
| Code Accuracy | 8.3/10 | Repository Context | 8.0/10 |
| Integration Ease | 8.9/10 | Refactoring Strength | 8.3/10 |
| Debugging Assistance | 8.2/10 | Test Generation | 8.0/10 |
| Language Support | 7.9/10 | Documentation Generation | 8.1/10 |
Windsurf, Codeium and Devin Desktop: the naming history in 30 seconds
The product naming is worth explaining, but it should not delay the buying decision.
- Codeium was the former name associated with the JetBrains coding assistant. The current extension is Windsurf Plugin.
- Windsurf Plugin is the product relevant to this JetBrains comparison and maps to the 8.4/10 Codeium dataset row.
- Devin Desktop is the current name of the former standalone Windsurf editor. Its old 8.8/10 Windsurf result is not the JetBrains plugin score.
- Devin is a separate autonomous coding-agent product.
The shortcut is simple: if you want to stay inside IntelliJ, compare Windsurf Plugin with Copilot, Amazon Q and JetBrains AI Assistant. If you are willing to replace or supplement IntelliJ with another coding environment, that is a different comparison.
Do not benchmark several completion engines at once
A common testing mistake is installing every candidate and leaving all automatic completions enabled. The resulting workflow tells you very little. Plugins can compete for Tab behaviour, inline suggestions, shortcut ownership, context collection and screen space.
Run one automatic completion engine at a time. Other plugins can remain installed for a separate agent or chat test, but disable their inline completion. This gives each provider a clean run and makes it obvious which system generated the accepted suggestion.
This issue appears repeatedly in real developer workflows: the model may be capable, but the surrounding integration creates friction through competing completions or awkward context selection. Those problems should count against the integration because they affect the developer using it, even when they say little about the underlying LLM.
Repository context is only useful if the resulting diff stays controlled
Repository context is often marketed as though more context automatically produces a better result. In an IDE, indiscriminate context can be a liability. The practical goal is to select the right context and leave everything else alone.
For each repository task, save the final diff and count unrelated edits. Check formatting-only changes, dependency modifications, generated files, migrations, and configuration edits that the prompt didn’t require. A tool that correctly identifies four connected files and changes only those four is showing more useful project understanding than one that reads the entire repository and produces a sprawling patch.
This same principle applies to refactoring. For a broader comparison focused on safely changing existing code, see our AI coding tools analysis based on recurring developer feedback.
See more of our reporting in Google Top Stories, AI Overviews and AI Mode.
Agent permissions can change the risk more than code quality
Inline completion proposes text for a developer to accept. An agent with terminal and project access can edit several files, install dependencies and execute commands before the developer inspects the first change.
| Permission | Why the agent wants it | What to inspect |
|---|---|---|
| Project files | Implementing multi-file changes | Unrelated files, generated code and configuration |
| Terminal | Running builds and tests | Package installation, destructive commands and working directory |
| MCP tools | Accessing services and external context | Which tools are callable and what data they expose |
| Git | Reading history and preparing changes | Branch operations, commits and accidental inclusion of unrelated work |
| Environment | Running the application | Secrets and production-like credentials visible to the process |
Start a benchmark with the minimum permissions required by the task. Granting every candidate unrestricted access may make the demo look smoother, but it removes an important part of the product comparison: how well the integration behaves when the developer retains control.
Free-plan restrictions can distort a JetBrains comparison
| Tool | Free access verified for this refresh | Testing implication |
|---|---|---|
| GitHub Copilot | 2,000 completions per month plus limited chat and agent usage | Suitable for an initial trial, but caps can affect longer agent testing |
| Amazon Q Developer | 50 agentic chat interactions per month on the perpetual Free tier | Free IDE access uses a Builder ID and does not reproduce every managed-team setup |
| Windsurf Plugin | A free plan exists, but a stable plugin-specific monthly numerical allowance was not sufficiently documented for us to state one | Record the allowance visible in the test account before starting |
| JetBrains AI Assistant | 3 AI Credits per 30 days | AI Free is unavailable in IntelliJ IDEA Community and some other free JetBrains configurations |
Do not let one candidate hit a quota halfway through a repository task and then compare its truncated result with a fully paid run from another provider. Either test all candidates within their free limits or use paid access for the controlled benchmark.
Which AI plugin should you install in IntelliJ?
Start with GitHub Copilot if you want the strongest all-round dataset result and a low-friction route into AI assistance without replacing JetBrains.
Choose Amazon Q Developer when AWS-specific work materially improves the value of the assistant, but include the April 2027 IDE plugin end-of-support date in any team rollout plan.
Choose Windsurf Plugin when agent-led multi-file work inside JetBrains is the attraction. Judge the plugin itself rather than transferring reputation or scores from Devin Desktop.
Choose JetBrains AI Assistant when native IDE integration and the ability to work with different agents through the JetBrains interface matter most. Record which agent is active whenever you benchmark it.
The final test is deliberately mundane: give each candidate the same real repository change and inspect what it leaves behind. Did it find the correct code? Did it implement the requirement? Did it preserve everything unrelated? Did the tests pass? Those four questions are more useful to an IntelliJ developer than another feature-count table.
FAQs
What is the best AI coding plugin for IntelliJ?
GitHub Copilot is the strongest starting point in DIY AI’s current JetBrains-compatible shortlist, scoring 9.0/10 overall and 9.6/10 for Integration Ease. Repository-specific testing should still determine whether it fits your own project better than Amazon Q, Windsurf Plugin or JetBrains AI Assistant.
Is Windsurf Plugin the same as Codeium?
Windsurf Plugin is the current JetBrains plugin lineage previously associated with Codeium. This is why DIY AI maps the existing 8.4/10 Codeium dataset result to Windsurf Plugin rather than using the 8.8/10 result for the former standalone Windsurf editor.
Is Windsurf now Devin Desktop?
The former standalone Windsurf editor is now Devin Desktop. Windsurf Plugin continues as a JetBrains integration, so the two should not be treated as the same product in a JetBrains ranking.
Is Amazon Q Developer still worth installing in IntelliJ?
It can still make sense for AWS-heavy development in 2026, but AWS has announced that Amazon Q Developer IDE plugin support ends on 30 April 2027. A new team deployment should therefore consider both current capability and the later migration.
Can I use Copilot and Windsurf Plugin together?
You can install both, but enabling two automatic completion engines makes comparison and everyday use harder to interpret. Keep one automatic completion provider active and use the other only for a clearly separated chat or agent workflow.
Does the underlying LLM matter more than the JetBrains plugin?
Both matter, but they answer different questions. The model affects reasoning and generated code. The JetBrains integration controls context selection, editing, permissions, test execution and how changes are reviewed. This page compares the integration decision rather than ranking coding LLMs.
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