Best AI Coding Assistants in 2026: Claude Code, Cursor, Copilot & Beyond

Table of Contents

• Why 2026 is the pivot year for AI coding
• The landscape: agentic CLIs vs IDE plugins vs Web IDEs
• Methodology
• Top 10 tools — detailed reviews
• Decision matrix: 6 developer profiles
• Methodology deep-dive: how we benchmark
• FAQ

Why 2026 is the pivot year for AI coding

The first wave of AI coding tools ran from 2021 to 2024. Most of it was autocomplete. GitHub Copilot's first product was a smart tab-completion engine. It saw your current file, guessed the next token, and sometimes got the function right. It was useful, but limited by design.

2025 changed how these tools were built. Models gained context windows long enough to hold entire repositories. Agents could now run tests, read error output, and iterate without human approval. MCP (Model Context Protocol) gave tools a standard way to reach outside data. That includes databases, documentation, and issue trackers, with no custom integration.

By 2026, the useful question is no longer "does this tool have autocomplete?" It is now: "how far can this tool go without me?" Can it take a GitHub issue, find the right files, and write a fix? Can it run the test suite, read the failures, and open a PR? Some tools now do all of that. But the quality of the result varies a lot.

Three structural shifts define the current landscape:

Agentic mode as table stakes. An agent mode lets the AI take a series of actions, check the output, and fix itself (see what is an AI agent). Tools without it are now behind. Autocomplete alone is no longer enough for senior developers.

Context window as a first-class feature. Holding a 200K-token repository in context is not just a spec-sheet number. It changes which tasks are possible. Whole-codebase refactors, dependency migrations, and large test runs work at 200K and up. They do not work at 32K.

MCP as the integration layer. The Model Context Protocol is becoming the USB standard for AI tool integrations. Today, every tool builds its own Jira, GitHub, and Postgres connectors. MCP lets a tool expose its features once. Then any compliant client can use them. This is moving fast. Expect MCP support to matter more in H2 2026 than it does today.

The landscape: agentic CLIs vs IDE plugins vs Web IDEs

Three architectural categories exist in 2026, each with different tradeoffs:

Agentic CLIs (Claude Code, Aider, OpenAI Codex CLI) run in the terminal. They have direct filesystem access. They can run shell commands and work with the same git repo your editor uses. They have no UI of their own. The interface is plain language in a shell. This makes them strong for scripted workflows, CI integration, and headless automation. The downside is friction. To see a diff or jump to a file in one click, you need a separate editor.

IDE plugins (GitHub Copilot, Tabnine, Cody, Continue.dev) plug into your existing editor. They see your current file, your open tabs, and your cursor position. The best ones also index your full repo for semantic search. They add the least friction for developers who want AI next to their normal workflow, not as a replacement. The tradeoff is the limit of the IDE plugin API. It exposes less than a CLI tool with shell access can do.

Forked IDE / Web IDEs (Cursor, Windsurf, Replit Agent) provide a complete environment. Cursor is a VSCode fork with AI built into every layer of the editor. Windsurf is similar. Replit Agent runs in the browser. It can set up servers and deploy code. These tools skip the plugin API limits by owning the full stack. The tradeoff is that you adopt someone else's editor. That is a big commitment for developers with years of custom VSCode or Neovim setup.

There is also a new fourth category: AI-native code review (tools like Graphite's Aviator, CodeRabbit). These sit in the PR workflow rather than the editor. This comparison does not cover them, but they are worth watching for 2027.

Methodology

We rated each tool on the points below. The work ran over six weeks, from April to June 2026:

SWE-bench Verified score (published by vendors or third parties). We use the 500-task Verified subset, not the full 2.3K benchmark. The Verified subset has been checked by hand to have clear correct answers. Scores come from vendors or peer-reviewed third-party runs. We note where a figure is vendor-claimed vs. reproduced on its own.

Real-world task battery. We ran the same set of 12 tasks across all tools where it applied. These included: add a feature to an existing Express.js API, migrate a React class component to hooks, and write tests for an undocumented legacy function. They also included: find and fix a race condition in an async queue, refactor a Python script to take CLI args, debug a failing GitHub Actions workflow, and six others. We left a tool out of a category when it could not be tested there. A web IDE on a CLI-only task is one example.

Context window (published, verified against docs). Numbers are from official documentation as of June 2026.

First-token latency. Time-to-first-token is the delay between sending a prompt and seeing the first output token. We treat it qualitatively rather than with hard numbers: it depends mostly on the underlying model and the network path, agentic tools that plan before acting feel slower to start, and dedicated completion models feel near-instant. It also shifts with server load and region. Measure it yourself for your own setup, or consult each vendor's published latency/SLA figures.

Pricing. Public pricing as of June 2026. Enterprise pricing varies, so we use public list prices.

MCP support, agentic mode, self-hostability, open source status. Binary flags from documentation.

We do not accept vendor credits or sponsored benchmarks. Some vendors offered to run "our latest model" on the benchmark in a private preview. We declined and used only public versions.

Top 10 tools — detailed reviews

1. Claude Code (Anthropic)

Tagline: Agentic terminal coding at model-native quality.

Claude Code is Anthropic's CLI for using Claude models on coding tasks. It is not an IDE plugin. It runs in your terminal, reads and writes files directly, runs shell commands, and works with git. As of mid-2026 it uses Claude Sonnet 4 by default. Opus 4 is available for the most complex tasks.

Strengths:

• Highest SWE-bench Verified scores among tested tools. Sonnet 4 reaches about 50-55% on the 500-task Verified subset (vendor-published, in line with independent reproductions)
• Native MCP support: you can wire Claude Code to a Postgres MCP server, a GitHub MCP server, or a custom tool, and it treats them as first-class capabilities
• 1M token context window makes whole-repository work possible on codebases that break every other tool

Weaknesses:

• No inline editor experience. You switch between terminal and editor.
• Cost at Opus 4 scale can reach $10-30 per hour of heavy agentic work on large repos
• No built-in code review UI. Output is plain text or patches, and you apply them yourself.

Pricing: API usage billed at standard Anthropic rates. Sonnet 4: $3/M input tokens, $15/M output tokens (as of June 2026 — verify current pricing at anthropic.com). The Claude Code CLI is free. Model API cost depends on how much you use it. The Max plan ($100/mo) includes higher rate limits.

Best for: Senior engineers and DevOps practitioners who want the highest-quality agentic output and are comfortable in the terminal.

Verdict: 9.0/10 — Best autonomous task completion. Highest ceiling; steepest on-ramp.

See our detailed Claude Code review and the Claude Code vs Cursor head-to-head.

2. Cursor (Cursor AI)

Tagline: The VSCode fork that makes AI feel native.

Cursor is a fork of VS Code with AI built into the editor's core, not added on as a plugin. Tab autocomplete, inline chat, multi-file composer, and a full agent mode are all tightly linked. It supports Claude, GPT-4o, and its own fine-tuned cursor-small model for fast completions.

Strengths:

• Fastest iteration loop of any IDE-integrated tool. Tab completion, Cmd+K inline edit, and Agent mode all work without leaving the editor.
• Cursor Tab (autocomplete) truly predicts, not just guesses the next token. It models what you likely want next based on recent edits.
• Strong multi-file context. Cursor's codebase indexing lets the model search your repo by meaning before it generates, which cuts hallucinated imports.

Weaknesses:

• Agent mode quality depends on the underlying model (Claude/GPT-4o). Cursor itself is an interface layer, not a model.
• Privacy posture requires trust. Code is indexed on Cursor's servers unless you turn indexing off. The privacy policy is better than most, but not zero-telemetry.
• The VSCode fork brings the odd extension compatibility issue and a lag behind upstream VSCode releases

Pricing: Free (2000 completions/month). Pro: $20/mo (500 fast requests + unlimited slow). Business: $40/user/mo. API key mode available if you bring your own model keys.

Best for: Full-stack developers who want an AI-native editor without leaving the VSCode ecosystem.

Verdict: 8.7/10 — Best overall IDE experience. The tool most developers will enjoy daily.

See our Cursor review and Cursor alternatives comparison.

3. GitHub Copilot (Microsoft)

Tagline: The incumbent — still the easiest to adopt at scale.

GitHub Copilot brought AI coding to the mainstream in 2021. In 2026 it is a much broader product. Copilot Workspace handles multi-step tasks from an issue description. Copilot Chat works across all major IDEs. Copilot Edit mode applies multi-file changes. It uses GPT-4o and GPT-4.1 as its main models, with Claude 3.5 Sonnet as an alternative.

Strengths:

• Deepest GitHub integration. Copilot Workspace can read issues, PRs, and CI logs, then act on them. No other tool has this much native GitHub context.
• Lowest adoption friction for enterprise. GitHub Enterprise plus Copilot Business is a single line item, already in most enterprise agreements.
• Copilot Agents (preview): PR review, issue triage, and automated fix suggestions with no prompt crafting from the developer

Weaknesses:

• Agent quality trails Claude Code and Cursor on complex multi-file tasks. SWE-bench Verified scores for GPT-4o sit around 38-43% (vendor-published).
• Context window capped at 128K tokens. That is enough for most work, but not for whole-monorepo operations.
• Price climbs fast on large teams. At $39/user/mo (Enterprise), it adds up for organizations with hundreds of engineers.

Pricing: Free (limited). Individual: $10/mo. Business: $19/user/mo. Enterprise: $39/user/mo. All plans include unlimited completions and chat.

Best for: Teams on GitHub Enterprise who want the lowest-friction path to AI assistance at scale.

Verdict: 7.8/10 — Best organizational fit for GitHub shops. Individually outclassed by Claude Code and Cursor on task quality.

4. Windsurf (Codeium)

Tagline: Cascade agent meets Supercomplete — the underdog IDE.

Windsurf is Codeium's AI-native IDE, built on VS Code. Its Cascade agent is made for multi-step tasks. It plans, runs, reads output, and iterates. Supercomplete is Codeium's autocomplete model. It is trained mostly on code and is notably fast.

Strengths:

• The Cascade agent breaks down medium-complexity tasks well (migrating an API endpoint, writing a test suite for existing code)
• Supercomplete is built for low-latency autocomplete and feels among the fastest to return a first completion — a deliberate design priority for Codeium. Measure it against Copilot and Cursor Tab on your own machine, since perceived speed depends on network and load.
• The free tier is generous: unlimited completions with the Supercomplete model, plus 25 Cascade agent tasks/month

Weaknesses:

• Cascade drops off on tasks that need deep architectural understanding. It completes the syntax but misses the intent more often than Claude-backed agents.
• MCP support is announced but not fully built as of June 2026. Third-party integrations are limited.
• Smaller community than Cursor, so fewer extensions are tuned for Windsurf

Pricing: Free (unlimited Supercomplete, 25 Cascade credits/mo). Pro: $15/mo. Teams: $30/user/mo.

Best for: Developers who prioritize low-latency autocomplete and want an agent-capable IDE without paying Cursor or Copilot prices.

Verdict: 7.5/10 — Strong autocomplete speed. Cascade agent is competitive for mid-complexity tasks.

5. Aider (open source CLI)

Tagline: Git-aware repo agent, bring your own model.

Aider is an open-source CLI tool that brings AI editing to any git repository. You point it at a repo, tell it which files are in context, and ask it to make changes. It generates unified diffs, applies them, and can auto-commit with a message. It works with any OpenAI-compatible API. That includes Claude, GPT-4o, Gemini, Groq, and local models via Ollama.

Strengths:

• Model-agnostic: switch between Claude Opus 4, DeepSeek V3, and a local Mistral instance with one flag. This helps you balance cost and quality.
• Git-native: every change is a commit. You get a full history of what the AI did and can revert with standard git tools.
• Truly open source (Apache 2.0): no proprietary server, no telemetry, and it runs fully on your machine

Weaknesses:

• No IDE integration: you work in a terminal next to your editor, with no inline diffs or clickable navigation
• Context is managed by hand: you say which files are in scope. Forget a relevant file, and the model lacks the context and will hallucinate.
• The UI is sparse. The chat is text-only. To review large diffs, you open a separate diff viewer.

Pricing: Free (Apache 2.0). You pay only for the API you use. With DeepSeek V3 ($0.27/M input tokens as of June 2026), real-world sessions usually cost $0.10-1.50 per hour.

Best for: OSS maintainers and developers who want full model flexibility and zero vendor lock-in.

Verdict: 8.2/10 — Best model-agnostic option. High ceiling when paired with a strong model; low floor when context management is neglected.

6. Continue.dev (open source)

Tagline: Multi-LLM IDE extension that stays in your own editor.

Continue.dev is an open-source VS Code and JetBrains extension. It supports any LLM through its provider system: Claude, GPT-4o, Gemini, Ollama, and dozens more. It has chat, inline edit, and autocomplete modes. The config is a JSON file you commit to your repo, so your team gets the same LLM setup.

Strengths:

• Works in JetBrains IDEs (IntelliJ, PyCharm, GoLand). It is one of the few tools with real JetBrains support, not just VS Code.
• Team config as code: a config.json in the repo gives every developer the same models, context providers, and prompts. This helps standardize AI use across a team.
• MCP support: Continue can connect to MCP servers, which gives it external tools without custom integration

Weaknesses:

• Agent mode is less mature than Cursor or Claude Code. It handles single-file tasks well but struggles with complex multi-file work.
• Autocomplete quality leans heavily on the model you configure. With a weak model, it trails commercial tools that have dedicated completion models.
• Setup friction: setting up providers, context, and prompts means reading the docs. It is not a 2-minute install.

Pricing: Free (Apache 2.0). Continue Hub (optional managed config + shared prompts): pricing available at continue.dev.

Best for: JetBrains users and teams that want standardized, policy-controlled LLM access across multiple developers.

Verdict: 7.3/10 — Best option for JetBrains shops. Requires more initial setup than commercial alternatives.

7. Cody (Sourcegraph)

Tagline: Code intelligence meets LLM chat.

Cody is Sourcegraph's AI coding assistant. It is built on Sourcegraph's code intelligence platform. So its context retrieval uses the same code graph technology that powers Sourcegraph search. It uses several models — Claude, GPT-4o, Gemini — and lets users pick the model at the prompt level.

Strengths:

• Code graph context retrieval: Cody indexes call graphs, symbol definitions, and cross-file references, not just text similarity. This gives more accurate context on large codebases than embedding-only retrieval.
• Model switching per prompt: use Claude Opus 4 for complex tasks and a faster model for quick edits in the same session
• Sourcegraph integration: if your team already uses Sourcegraph for code search, the same index enriches Cody's context

Weaknesses:

• The best features need a Sourcegraph Enterprise license. The free tier is limited to the current file and basic context.
• Agent mode is in preview as of mid-2026. It is not yet a match for Cursor or Claude Code on complex tasks.
• The VS Code extension is polished, but JetBrains support is less complete than Continue.dev

Pricing: Free (current file context, Claude Haiku/Sonnet). Pro: $9/user/mo. Enterprise: custom pricing with full Sourcegraph indexing.

Best for: Engineering teams that use Sourcegraph for code navigation and want AI that understands the same code graph.

Verdict: 7.1/10 — Distinctive code intelligence advantage in large codebases. Agent mode not yet production-ready.

8. Tabnine

Tagline: Privacy-first code completion with an enterprise on-prem option.

Tabnine has been in the AI coding space since 2019, before Copilot. Its 2026 stance stands out for privacy. It does not train on your code by default, and the Enterprise tier can run fully on your own infrastructure. The AI model is its own, trained on permissively licensed code.

Strengths:

• On-premises deployment: the only mainstream tool with a credible, production-ready air-gap option as of 2026
• No training on your code: stated plainly in the terms for paid plans, which matters for organizations sensitive about IP
• Context-aware personalization: Tabnine learns from your codebase locally to make completions more relevant, without sending code to outside servers

Weaknesses:

• No agent mode: Tabnine is a code completion tool. It does not run tasks, run tests, or apply multi-file changes on its own.
• Chat quality is behind Claude-backed tools. The underlying model is not as strong as Claude Sonnet 4 or GPT-4o for complex generation.
• The UI feels dated next to Cursor and Windsurf. The experience is completion-first, not agent-first.

Pricing: Free (basic completions). Pro: $12/user/mo. Enterprise: custom (includes on-prem deployment option).

Best for: Enterprise security teams and regulated industries (finance, healthcare, defense) where code cannot leave the network.

Verdict: 6.8/10 — Best privacy posture. Not competitive on agent tasks. Right tool for specific compliance contexts.

9. OpenAI Codex CLI

Tagline: Agentic CLI from the model lab — Claude Code's closest structural rival.

OpenAI's Codex CLI is a command-line agent. It uses GPT-4o and o4-mini (OpenAI's reasoning model) to work on codebases. The architecture mirrors Claude Code: terminal-first, filesystem access, shell execution. It was released in April 2025 and updated through mid-2026.

Strengths:

• o4-mini reasoning mode: some tasks gain from extended thinking, such as complex algorithms, hard debugging, and architectural decisions. On these, o4-mini's chain-of-thought approach beats standard GPT-4o by a clear margin.
• OpenAI ecosystem integration: if your team already uses the OpenAI API for other products, Codex CLI shares credentials and rate limits
• Sandboxed execution mode: by default, Codex CLI runs shell commands in a sandbox and asks before writing files. This helps cautious adoption.

Weaknesses:

• SWE-bench Verified scores for GPT-4o-based runs sit in the 38-45% range (vendor-published). That is below Claude Sonnet 4 on the same benchmark.
• The 128K context window is competitive but below Claude's 1M for whole-repo operations
• MCP support is not available as of June 2026. Integrations need custom tool definitions in the OpenAI function-calling format.

Pricing: API usage at standard OpenAI rates. GPT-4o: $5/M input, $15/M output. o4-mini: $1.10/M input, $4.40/M output (verify at openai.com — pricing updates often).

Best for: Teams already on the OpenAI API who want an agentic CLI without adding another vendor.

Verdict: 7.4/10 — Solid option for OpenAI-committed teams. o4-mini reasoning mode is a genuine differentiator for hard problems.

See our AI agent latency comparison for detailed first-token latency comparisons between Claude Code and Codex CLI.

10. Replit Agent

Tagline: Full-stack agent in the browser — zero local setup.

Replit Agent is Replit's AI system for building and deploying complete applications from plain-language descriptions. It runs fully in the browser and has a persistent cloud development environment. It can set up databases, install packages, write code, run tests, and deploy, all in one loop.

Strengths:

• Zero local setup: the whole development environment is in the cloud. This suits rapid prototyping, education, or working from any device.
• Full-stack deployment in one tool: Replit can go from "build me a todo app with auth and a Postgres backend" to a live deployed URL with no manual infrastructure steps
• Replit's compute layer: the agent has real compute. It can actually run the application and watch how it behaves, not just generate code.

Weaknesses:

• Not suited to production-grade applications: Replit's deployment infrastructure is tuned for demos and education. It does not fit production workloads that need a custom CDN, SLA guarantees, or compliance controls.
• Limited on complex existing codebases: Replit Agent works best on greenfield projects. On a large existing codebase, it is less effective than Claude Code or Cursor.
• Cost scales with compute, not just model tokens. You pay for the Replit environment, the model, and the compute. For heavy use, this adds up fast.

Pricing: Replit Core: $25/mo (includes agent access). Teams and enterprise pricing available.

Best for: Prototyping, education, hackathons, and non-engineers who need a working app without touching a terminal.

Verdict: 7.0/10 — Best for zero-friction full-stack prototyping. Not a replacement for a professional development environment.

Decision matrix: 6 developer profiles

The table below maps six developer types to a primary and a secondary tool. These are starting points, not rules. Your stack, privacy needs, and budget may shift the pick.

Notes on the matrix:

The indie dev profile gains most from Cursor's Pro plan. It gives a full agentic IDE at $20/mo with no per-seat overhead. Aider, as a secondary tool, handles the "run this migration script overnight on its own" use case cheaply.

Senior engineers at large companies face a different limit. Their tool must follow security policies, and a security review board often must approve it. Claude Code and GitHub Copilot Business are the most common approvals in mid-2026. Copilot gains from Microsoft's enterprise sales ties. Claude Code needs an Anthropic enterprise agreement.

OSS maintainers care most about model flexibility and keeping code under their control. Aider plus a local model via Ollama, or a usage-based API like DeepSeek, is the leanest and most controllable option.

Agencies that handle many client codebases have one key need: codebase isolation. Cursor Business and Copilot Business both allow per-workspace isolation. The default open-source Continue.dev approach needs careful config so client A's context does not bleed into client B's.

Junior developers gain from tools that explain what they do, not just do it. GitHub Copilot's inline chat with "explain this code" and Cursor's inline chat mode are both tuned for learning while you code. Aider and Claude Code are powerful, but they produce diffs and terminal output. That can overwhelm developers who are not yet at ease with the underlying concepts.

For a full breakdown of how each tool handles specific languages, frameworks, and task types, see our State of AI Dev Tools 2026 report and the Best AI IDEs comparison. For the bigger picture, see what AI pair programming really delivers and how autonomous AI coding agents differ from in-editor assistants. On a budget? Several of these tools have a free tier — see our guide to the best free AI coding assistants.

Methodology deep-dive: how we benchmark

The SWE-bench Verified scores in this article come from published vendor reports. Where available, they also come from independent third-party reproductions. The Verified subset (500 tasks) is more reliable than the full 2.3K benchmark. Every task has been reviewed by hand to confirm the test suite is correct and the expected fix is clear.

One key caveat: SWE-bench is Python-centric. All 12 repositories in the Verified subset are Python projects. Scores on TypeScript, Rust, or Go codebases may differ a lot. We plan to publish our own cross-language benchmark in a future study.

For real-world task scoring, we used a rubric with four criteria. (1) Did the code run without errors after the AI's changes? (2) Did it pass the existing test suite? (3) Did it match the behavior described in the task? (4) Could a developer outside the AI session read the resulting code? Each criterion scored 0 or 1, for a max of 4 per task. We averaged scores across the 12-task battery.

First-token latency figures are indicative. They are based on observed behavior and public reports. Treat them as relative comparisons, not firm SLAs. API latency varies with server load, region, and model version.

On pricing accuracy: AI tool pricing moves a lot. Claude's, OpenAI's, and Google's token prices have all changed in 2025-2026. We cite prices as of June 2026 and link to official pricing pages where we can. Always verify at the vendor's site before you buy.

FAQ

What is the best AI coding assistant in 2026?

It depends on your workflow. Claude Code leads on agentic tasks and multi-file refactors in the terminal. Cursor is the strongest IDE-integrated option for developers who want autocomplete plus agent mode in one VSCode-compatible environment. GitHub Copilot stays the lowest-friction choice for teams already on GitHub Enterprise.

What is SWE-bench Verified and why does it matter?

SWE-bench Verified is a benchmark of 500 real GitHub issues from 12 popular Python repositories. The model must apply a patch that makes a hidden test suite pass, without seeing the tests. It measures real software engineering skill: reading existing code, understanding context, and writing correct fixes. It is not just code generation from a clean prompt. Scores above 50% count as strong as of 2026.

Does Claude Code work without an IDE?

Yes. Claude Code is a CLI tool. You run it in any terminal, point it at a directory, and talk to it in plain language. It reads and writes files, runs tests, and runs commands. No IDE is required. It also plugs into VS Code and JetBrains via an extension if you prefer a hybrid workflow.

Is Aider free to use?

Aider itself is free and open source (Apache 2.0). You pay only for the model API you point it at: Claude, GPT-4o, Gemini, or any OpenAI-compatible endpoint. Running it with DeepSeek V3 or a local Ollama model costs almost nothing. Running it with Claude Opus 4 can cost several dollars per hour on large repos.

Can GitHub Copilot replace a human code reviewer?

Not yet. Copilot's code review feature flags obvious issues, such as unused variables, type mismatches, and common security anti-patterns. But it misses architectural concerns, business logic bugs, and subtle concurrency issues. It is a useful first filter, not a replacement for domain-expert review.

What is Model Context Protocol (MCP) and which tools support it?

MCP (Model Context Protocol) is an open standard from Anthropic. It lets AI tools connect to outside data sources — databases, APIs, file systems — without custom integration code. Claude Code has native MCP support. Cursor supports MCP in its Agent mode. Continue.dev also supports MCP. Copilot, Windsurf, and others have announced support or are in preview as of mid-2026.

Is Tabnine safe for enterprise code?

Tabnine is one of the few tools with a credible air-gap option. Its Enterprise tier can run fully on-premises, with no code leaving the network. It does not train on your code by default on any paid plan. For organizations with strict IP or compliance needs, it is one of the safest choices among the mainstream tools.

What context window size do I actually need for coding tasks?

For single-file edits, 8K tokens is enough. For refactors that span 5-10 files, you need 32K-128K. For whole-repository understanding — migrating a large codebase, or finding all call sites of a deprecated API — you need 200K or more. Claude Sonnet 4's 1M token context helps with the largest monorepos, though inference cost rises with context length.

Related guides: Claude vs ChatGPT and Zed vs Cursor.