Best AI Coding Assistants in 2026: Copilot, Cursor, Claude Code, Windsurf, JetBrains AI and Gemini Compared

Key takeaways

• Windsurf no longer exists as a brand. Cognition renamed it Devin Desktop on 2 June 2026 and retired the Cascade agent on 1 July 2026, replacing it with a Rust rewrite called Devin Local.
• Gemini Code Assist is a paid Google Cloud product only. Google's deprecation notice confirms its IDE extensions stopped serving the individuals, AI Pro and AI Ultra tiers on 18 June 2026.
• GitHub Copilot moved to token-metered billing on 1 June 2026, replacing premium request units with AI Credits priced against published per-model API rates.
• GitHub does not use Copilot Business or Copilot Enterprise customer data to train AI models, per its documentation, but Free, Pro and Pro+ interaction data has fed training by default since 24 April 2026.
• Anthropic does not train on code or prompts sent to Claude Code under commercial terms, while consumer Free, Pro and Max accounts follow the account's training setting.
• Only 55–56% of AI code-generation tasks produce secure code, per Veracode's Spring 2026 update and its full 2026 report published 28 July 2026, with cross-site scripting passing 15% of the time and log injection 12–13%.

Three structural changes in the first half of 2026 matter more than any benchmark, and all three affect procurement rather than code quality.

First, metering shifted. GitHub announced that from 1 June 2026 Copilot moved from premium request units to AI Credits, consumption calculated on token usage including input, output and cached tokens against published per-model API rates. GitHub's framing is blunt: under the old model, a quick chat question and a multi-hour autonomous session could cost the same. Your bill now scales with agent autonomy, not headcount.

Second, one vendor disappeared into another. Cognition's 2 June 2026 announcement states that Devin Desktop is the new name for Windsurf, delivered as an over-the-air update with plans, pricing and extensions carried over. Cascade stayed available until 1 July 2026 and was replaced by Devin Local, described as a Rust rewrite delivering up to 30% greater token efficiency with subagent support.

Third, Google withdrew the free consumer path. Its deprecation page states that starting 18 June 2026, Gemini Code Assist IDE extensions stopped serving the individuals tier, directing those users to Antigravity. Standard and Enterprise access is unchanged. Any 2025 evaluation done on a personal Google account no longer describes a product you can buy.

"AI coding assistant" covers three things with different failure modes and different pricing logic. Deciding which you are buying resolves most of the comparison before you look at a vendor.

• Inline completion. The model predicts the next few lines from the open file, and a wrong suggestion costs a keystroke. Copilot Free still ships 2,000 completions a month at no cost, which shows how commoditised this layer is.
• In-editor agentic editing. The model plans a change, edits several files and runs commands: Cursor's agent, Copilot's agent mode, Devin Local, JetBrains' Junie. Cost scales with how much it reads and writes, which is why every vendor here meters usage.
• Delegated agent. The work happens elsewhere, in a cloud VM, a pull request or a CI job. Review discipline stops being optional here, because nobody watched the diff being written. Our guide to AI agents that take actions covers the governance pattern.

Every figure below comes from vendor pricing, documentation or announcement pages checked in August 2026. Where a vendor publishes a tier as a usage multiplier rather than a price, that is what the table says.

Context window size is the wrong metric. What matters is retrieval strategy: a persistent semantic index, an agentic search loop, or a server-side index you do not control.

Cursor takes the index route and documents the security model in unusual detail: it creates embeddings without storing filenames or source code, code content is never stored in plaintext but held in memory during indexing then discarded, filenames are obfuscated and chunks encrypted, and retrieval decrypts on the client side. You can supply your own path-encryption key through a .cursor/keys file.

GitHub Copilot uses a server-side semantic code search index over GitHub-hosted repositories, built automatically when you open a chat with repository context. It is warm for code you have pushed, but describes the repository as GitHub knows it, not your working tree.

Claude Code explores at query time instead, reading files and running commands during the session, with persistent instructions in a CLAUDE.md file it reads at every session start. Nothing to build or invalidate, which suits fast-moving repositories and costs more tokens per cold start. Gemini Code Assist Enterprise documents code customization against your private repositories on GitHub, GitLab and Bitbucket, the clearest difference between its editions.

Anthropic's documentation shows Claude Code composing with shell pipelines directly, piping log tails into claude -p or feeding git diff --name-only into a review prompt. Hooks run shell commands before or after its actions, and it runs in GitHub Actions and GitLab CI. That is why it turns up in pipelines where the other five mostly do not.

Copilot ships a CLI alongside the IDE, included even on Free. Cursor's enterprise tier exposes auto-run, browser and network controls, the admin-side acknowledgement that an editor agent running commands is a security surface. Devin Desktop went furthest, rebuilding the launch screen around a kanban command centre rather than an editor canvas.

All six reach external systems through the Model Context Protocol. Our MCP guide covers the transport and the risks, and the agentic AI foundation article covers how MCP, AGENTS.md and the newer Agent Client Protocol fit together. ACP is what lets Devin Desktop run Codex, Claude Agent and OpenCode in the same interface as its own agent.

Model choice splits the six into aggregators, single-vendor tools, and one that lets you bring your own key or run the model locally.

GitHub Copilot is the broadest aggregator: its model comparison documentation lists Anthropic Claude, OpenAI GPT, Google Gemini and xAI Grok side by side, and notes that different models consume AI credits at different rates. Choice is real, and it is priced.

Cursor mixes third-party frontier models with its own. It published Composer on 29 October 2025 as a mixture-of-experts model trained with reinforcement learning inside real codebases, claiming it "achieves frontier coding results with generation speed four times faster than similar models." Cursor's own chart placed it below GPT-5 and Sonnet 4.5 on capability while beating them on speed.

Claude Code is single-family by design, but routes through the Anthropic API, Amazon Bedrock, Google Cloud's Agent Platform or Microsoft Foundry, the escape hatch that matters for data residency. Gemini Code Assist is single-family with no such routing.

JetBrains AI is the outlier, and the reason it survives in regulated shops: its documentation supports Ollama, LM Studio and OpenAI-compatible endpoints such as llama.cpp and LiteLLM, plus bring-your-own-key credentials for Anthropic, Google and OpenAI. The documented catch is that AI Assistant cannot invoke tools from configured MCP servers when using local models. If everything must run locally, read our roundup of the best open-source AI models first.

This is where the six diverge most, and where the answer changed for two of them in 2026. Read the plan tier, not the brand.

GitHub Copilot. GitHub's documentation states that GitHub does not use Copilot Business or Copilot Enterprise customer data to train AI models. Individual plans went the other way: from 24 April 2026, interaction data including inputs, outputs and code snippets from Free, Pro and Pro+ users trains its models unless the user opts out, with the toggle in settings under Privacy.

Claude Code. Anthropic's data-usage page separates the two worlds: it does not train generative models on code or prompts sent to Claude Code under commercial terms (Team, Enterprise, API and third-party platforms) unless the customer opts into the Development Partner Program, while consumer accounts follow the account's training setting. Retention is 5 years for consumer accounts that allow training and 30 days otherwise, including commercially. Zero data retention exists for Claude for Enterprise but is not part of the standard plan.

Cursor. Privacy Mode is the control. Cursor's security page states it can be enabled in settings or by a team or enterprise admin, is available to anyone including free and Pro users, and that with it on Cursor will not train on your data. Cursor holds a SOC 2 Type II attestation available on request.

The rest. Cognition's enterprise page states that all inputs and outputs are the customer's intellectual property, that customer data is never used for training, and that Devin can be deployed in a customer VPC. Google documents IP indemnification and source citations in the IDE on both Gemini editions; anything stricter is a Cloud contract question. JetBrains publishes licensing and credit terms rather than a training commitment, so confirm that in your contract, or use its local-model path, since a model running through Ollama never sends code anywhere.

Every vendor ships SSO. The differences that decide a rollout are agent-scoped: which repositories an agent can touch, which models it may call, and whether it can run commands or reach the network.

Cursor's enterprise tier is the most explicit, listing pooled usage, SCIM seat management, repository, model and MCP access controls, auto-run, browser and network controls, audit logs, service accounts and an AI code tracking API. That last item, for tracking how much of your codebase is AI-authored, is a governance primitive most vendors do not offer.

GitHub's answer is organisational policy plus Agent HQ, a mission control surface for assigning and tracking agent work across GitHub, VS Code, mobile and the CLI, with third-party agents runnable inside a paid Copilot subscription. JetBrains routes governance through JetBrains Central, where organisations draw on a pooled credit balance that expires after a year. Cognition's answer is deployment-shaped, and Gemini Code Assist inherits Google Cloud IAM.

Four metering units are in play across these six tools, so a seat-price comparison is close to meaningless. Translate each into your own usage shape first.

GitHub bills tokens dressed as credits. Its plans page (checked August 2026) lists Pro at $10 with $15 in monthly AI credits, Pro+ at $39 with $70, and Max at $100 with $200. Its billing announcement puts Business at $19 per user with $19 in credits and Enterprise at $39 with $39, plus a promotional doubling to $30 and $70 through August 2026.

JetBrains is the most transparent about what a credit is: its documentation defines quota as a monthly resource measured in AI Credits and states that one AI Credit equals one US dollar, worth roughly 10 AI Chat code-generation requests, 40 editor code-generation requests or 140 commit messages. AI Free gets 3 credits per 30 days with no top-up, AI Pro 10 to 20, AI Ultimate 35 to 70.

Cursor and Devin sell tiers as usage multiples. Cursor lists Hobby free, Pro at $20 and Teams Standard at $40 per user, then describes Pro+ as 3x Pro agent limits, Ultra as 20x and Teams Premium as 5x Standard without printing figures. Devin lists Free, Pro at $20, Max at $200, Teams at $80 plus $40 per developer.

Anthropic bundles Claude Code into the Claude subscription: Free includes it, Pro is $17 a month billed annually or $20 monthly, Max starts at $100 with 5x or 20x Pro usage, and Team seats are $20 standard or $100 premium annually. Google sells Gemini Code Assist Standard and Enterprise as per-seat Cloud subscriptions with monthly or annual commitment terms; we could not render a current list price from Google's own pricing page in August 2026, so confirm the seat figure before budgeting.

Three independent bodies of evidence point at the same failure modes: perceived speed that is not real speed, security defaults that have not improved, and structural erosion of the codebase.

The speed you feel is not the speed you get

METR's randomised controlled trial, published July 2025, put 16 experienced open-source developers through 246 real tasks in repositories they had worked on for around five years. Developers took 19% longer when allowed to use AI tools, then estimated afterwards that AI had made them roughly 20% faster. METR now flags the result as historical and has revised its experiment design. The durable finding is the 39-point gap between perceived and measured effect, which is reason enough to distrust your own impression during a two-week trial.

Security defaults have not moved in two years

Veracode's Spring 2026 GenAI Code Security update, published 24 March 2026, tested 80 coding tasks across Java, JavaScript, C# and Python against four vulnerability classes. Only 55% produced secure code without explicit security guidance. The distribution should shape your review: SQL injection passed 82% of the time and insecure cryptography 86%, but cross-site scripting passed just 15% and log injection 13%. By language, Python reached 62%, C# 58%, JavaScript 57% and Java 29%. Veracode's full 2026 GenAI Code Security Report, published 28 July 2026, put the field average at 56% with the best-performing model at 68%, cross-site scripting at 15% and log injection at 12% — the same shape, five months and several model releases later. Pass rates sit roughly where they were two years ago despite far more capable models.

Codebases are getting less maintainable

GitClear's January 2026 "Maintainability Gap" research analysed 623 million changed lines from 2023 to 2026. Duplicated code blocks rose from 40.3 per million changed lines in 2023 to 73.0 in 2026, an 81% increase. Copy-pasted code rose from 9.4% of new code in 2022 to 15.7% in the first half of 2026, while moved code, the signal of genuine refactoring, fell from 21% to 3.8%. That is what you would expect when generating a fresh implementation is cheaper than reusing the existing one.

What developers report

Stack Overflow's 2025 Developer Survey found 84% of respondents using or planning to use AI tools, while only 3.1% highly trust the accuracy of AI output and 45.7% somewhat or highly distrust it. The biggest frustration, cited by 66%, is "AI solutions that are almost right, but not quite", and 45.2% say debugging AI-generated code is more time-consuming.

Review AI output against its known failure distribution rather than reading it like human code. The evidence above gives a specific checklist rather than a general instruction to be careful.

1. Weight review toward output encoding and injection paths. Veracode's data shows cross-site scripting and log injection failing 85% and 87% of the time, while SQL injection and cryptography mostly pass.
2. Check whether the change duplicates something that exists. GitClear's duplication and refactoring numbers describe the dominant structural regression. Ask "does this logic already live somewhere" before "is this logic correct".
3. Make the agent state its plan before it writes. Use CLAUDE.md, AGENTS.md or the equivalent for architecture decisions and review checklists, not style rules a linter already enforces.
4. Turn on the public-code filter. GitHub's duplication detection system identifies suggestions matching public code and can block them or show repository and licence information. On a commercial codebase there is no argument for leaving it off.
5. Review with a different agent than the one that wrote the code. Cursor ships Bugbot, Copilot offers code review on pull requests and file diffs, and Claude Code can run review in GitHub Actions.
6. Measure the diff, not the vibe. Track cycle time and defect escape rate before and after rollout, because self-reported speed was wrong by 39 points in METR's controlled setting.

The decision rule is about where your governance already lives and what shape your work takes, not about model quality, which converges within weeks of any release.

• GitHub Copilot if your repositories and identity provider are already on GitHub. Business and Enterprise carry the documented no-training commitment and the model picker spans four vendors. Budget for token-metered credits, and check when the promotional allowance expires.
• Cursor if the editor should be the agent on a large codebase where retrieval quality decides everything. Its indexing security model is the best documented of the six and its agent access controls the most granular.
• Claude Code if the work is terminal-shaped: scripted refactors, CI review jobs, log triage, repo-wide migrations. Commercial terms carry a clear no-training commitment. The trade is a single model family.
• Windsurf, now Devin Desktop, if you want several agents running in parallel with the editor as a command centre, and can accept a brand and default agent that both changed inside eight weeks.
• JetBrains AI if your team lives in IntelliJ, PyCharm or WebStorm, or if local and bring-your-own-key models are a hard requirement. Verify the features you need work locally first.
• Gemini Code Assist if you are a Google Cloud shop needing IP indemnification, buying Standard or Enterprise seats. Enterprise is the only tier with private-repository code customization.

Still deciding which underlying model to standardise on? Our comparison of which AI model is best in 2026 covers the frontier families these tools route to.