Best AI Coding Agents in 2026: How They Handle a Large Codebase

The best AI coding agents in 2026 are Claude Code and Codex for terminal work, Cursor for an editor-first workflow, and GitHub Copilot for teams that live on GitHub, with Cline, Roo Code, Aider, OpenCode and Gemini CLI as the open-source, bring-your-own-model options. On a large repository the difference between them shrinks next to a bigger factor: how much of the codebase the agent has to rediscover on every question.

This is a roundup, not a bake-off. We checked every agent below against its own pricing page and docs on 29 October 2026. The only agents we measured are Claude Code and Codex, in the published repowise benchmarks; the other agents were not run.

The short list:

• Claude Code: terminal-first agent from Anthropic, also in VS Code, desktop and on the web.
• OpenAI Codex: CLI, IDE extension, web and desktop, bundled with ChatGPT plans.
• Cursor: an editor with an agent on every plan and cloud agents on paid ones.
• GitHub Copilot: IDE agent plus a cloud agent that opens pull requests.
• Augment Code: pooled-usage pricing and a Context Engine that other agents can call.
• Devin and Windsurf: Windsurf's pricing URL now redirects to Devin's.
• Gemini CLI, Cline, Roo Code, Aider, OpenCode: open-source agents you point at your own model.
• Amp and Kiro: remote-first and credit-based agents with their own pricing.

How we evaluated

An agent is the model plus its harness, so we compared the parts a team can actually decide on, each read from the vendor's own page.

• Pricing and who pays for the model. Flat plan, credits, or your own API key.
• Where it runs. Terminal, editor, a cloud sandbox, or all three.
• MCP support. Whether you can attach a server such as a codebase index.
• How it finds context. Its own search, a repo map, a semantic index, or nothing beyond file reads.
• Measured evidence. Only where someone ran the agent with and without a context layer, with conditions. For repowise that means our published benchmarks under Claude Code and Codex.

We left out model-quality rankings. They change with every release and a single leaderboard run does not predict how an agent behaves on your repository.

The comparison table

Prices are monthly and list prices as of 29 October 2026, from each vendor's page. "Not stated" means the vendor pages we read did not say, not that the feature is absent. We dropped a "how it finds context" column because most vendor pages do not describe it; Aider is the exception and is covered in its section.

Sources: claude.com/pricing, learn.chatgpt.com/docs/pricing, cursor.com/pricing, github.com/features/copilot/plans, augmentcode.com/pricing, devin.ai/pricing, kiro.dev/pricing, the Gemini CLI repository, cline.bot/pricing, aider.chat, opencode.ai and ampcode.com.

Claude Code

Claude Code is Anthropic's terminal agent, and as of October 2026 it is included in every paid Claude plan. Per claude.com/pricing, Pro is $20 a month ($17 on annual billing), Max starts at $100, Team standard seats are $20 a seat on annual billing and Premium seats $100, and Enterprise is $20 a seat with a 20-seat minimum plus usage at API rates. It runs from a terminal, the VS Code extension, the desktop app and on the web, and shares usage limits with the rest of the plan.

MCP is first-class: claude mcp add registers a server at local, project (a committed .mcp.json) or user scope (Claude Code MCP docs). That matters on large repositories because the project scope lets a whole team share one codebase server. We cover the setup in Claude Code MCP: how to add servers.

Pick it if you work mostly in a terminal, want the agent tied to a subscription instead of metered API spend, and plan to extend it with MCP servers.

OpenAI Codex (CLI and cloud)

Codex is OpenAI's coding agent, and the CLI, IDE extension, web and desktop surfaces come with ChatGPT plans from Plus ($20) up. Per learn.chatgpt.com/docs/pricing on 29 October 2026, Free and Go ($0 and $8) list only the desktop app, Plus gets web, CLI, IDE and cloud integrations, Pro runs from $100 to $500, and Business is $20 a user billed annually. Cloud features are not available when you sign in with an API key.

The CLI is scriptable (codex exec in CI) and takes MCP servers through codex mcp (Codex CLI docs). OpenAI is also retiring older models: per the same page, GPT-5.5 stops working in Codex with ChatGPT sign-in on 14 October 2026.

Pick it if your team already pays for ChatGPT, or you want one agent that works in a terminal, an IDE and a cloud sandbox.

Cursor

Cursor is an editor-first agent with agent mode on every plan, including Free. Per cursor.com/pricing on 29 October 2026, plans run Free, Plus ($8, Grok models only), Premium ($30), Super ($100), Ultra ($200) and Teams ($30+ a user). All tiers draw on a shared usage pool that refreshes weekly, cloud agents and automations start at Premium, and third-party models bill at API list price on top.

MCP servers go in .cursor/mcp.json per project or ~/.cursor/mcp.json globally, over stdio, SSE or streamable HTTP (Cursor MCP docs). Because it speaks MCP, an external codebase index attaches the same way as under any other agent.

Pick it if you want the agent inside the editor you already review code in, and agent mode on every plan.

GitHub Copilot

GitHub Copilot now spans IDE agents and a cloud agent that works on a task and opens a pull request. Per github.com/features/copilot/plans, Pro is $10, Pro+ $39 and Max $100, measured in GitHub AI Credits at $0.01 each (Pro includes $15 of credits with flex allotment). Assigning work to Copilot to create a pull request is included on Pro, Pro+ and Max, and delegating to third-party agents such as Claude and OpenAI Codex (Preview) is on Pro+ and Max. Business and Enterprise prices are not listed on the page.

According to GitHub's docs, the cloud agent runs tests and linters in a cloud environment, GitHub Actions sessions cap at 59 minutes and a single repository, and MCP servers are supported in both environments.

Pick it if your issues, reviews and CI already live on GitHub and you want delegation to happen where the pull request does.

Augment Code

Augment Code sells pooled usage instead of per-seat plans, and exposes its Context Engine to other agents over MCP. Per augmentcode.com/pricing, Standard is $20 a month flat with $20 of usage and up to 50 seats, Business is $100 flat with $100 of usage, and model usage bills at provider API price plus a flat 40% service fee.

The Context Engine MCP is semantic code search that Augment says runs its indexer locally and works with Claude Code, Cursor, Zed, Kilo Code and Roo Code. Augment reports benchmark gains from it on 300 Elasticsearch pull requests; those are self-reported and the page does not give full methodology. We compare it with other context layers in Augment Context Engine alternatives.

Pick it if you want a team plan where usage is pooled across up to 50 people and you value an indexer that other agents can call.

Devin and Windsurf

Windsurf is now part of Devin: windsurf.com/pricing redirects to devin.ai/pricing, and Cognition says the two brands were unified under Devin Desktop. Cognition's 14 July 2026 post says "we finally unified into one brand with the launch of Devin Desktop"; it gives no launch date and does not use the word "renamed", and the Devin pricing page does not mention Windsurf. Secondary coverage dates Windsurf 2.0 (with Devin built in) to 15 April 2026 and the Devin Desktop name to June 2026; we have not confirmed those dates with Cognition.

Per devin.ai/pricing: Free is $0 with a light quota, Pro is $20 with frontier models from OpenAI, Claude, Gemini and SpaceXAI plus Devin Cloud access, Max is $200, and Teams is $80 for the team plus $40 per full developer seat.

Pick it if you want an editor plus an agent that can be handed whole tasks in a cloud environment.

Gemini CLI

Gemini CLI is Google's Apache 2.0 terminal agent. Per its GitHub repository on 29 October 2026, install is npm install -g @google/gemini-cli, MCP servers are configured in ~/.gemini/settings.json, and persistent project context lives in a GEMINI.md file. The README says free individual access moved to Antigravity CLI on 18 June 2026 and that the CLI is not tuned for the Gemini 4 series, so check which models your key reaches before standardizing on it.

Pick it if you already use Google's models through an API key and want an open-source terminal client.

Cline and Roo Code

Cline and Roo Code are open-source VS Code agents you run on your own model key. Per cline.bot/pricing, the open-source Cline extension is free and you pay only for inference, with a VS Code extension and CLI and an MCP Marketplace; JetBrains support is listed under Enterprise. Roo Code's docs describe an open-source VS Code assistant with MCP over stdio, streamable HTTP and SSE, configured globally in mcp_settings.json or per project in .roo/mcp.json.

Pick them if you want the model choice, the prompts and the cost fully in your hands.

Aider

Aider is a terminal pair-programmer that builds a repo map before each change. Per aider.chat/docs/repomap.html, it sends the model a map of files and key symbols chosen by a graph ranking over file dependencies, with a token budget set by --map-tokens that defaults to 1k. That is a different answer to the context problem than an index the agent queries. Neither Aider page we fetched mentions MCP, so we do not claim it either way.

Pick it if you want a small, git-native tool you drive with your own API key or a local model.

OpenCode

OpenCode is an MIT-licensed agent for the terminal, desktop and IDEs that talks to 75+ model providers. Per opencode.ai, you can reuse an existing ChatGPT Plus/Pro or GitHub Copilot subscription, and OpenCode itself is free. MCP servers, local or remote, go under the mcp key in opencode.json (OpenCode MCP docs).

Pick it if you want an open-source terminal agent that is not tied to one model vendor.

Junie, Kilo Code and OpenHands

Junie, Kilo Code and OpenHands all rank for this query, and each takes a different angle.

• Junie is JetBrains' agent. Per junie.jetbrains.com, it runs from a terminal, in JetBrains Air, and from GitHub and GitLab issues and merge requests, with 10+ models through bring-your-own-key. JetBrains AI Pro is $8.33 per user per month (10 AI Credits per 30 days) and AI Ultimate $25.00 (35 credits); the page does not say whether those prices are annual or monthly and does not mention MCP. Pick it if your team is on JetBrains IDEs.
• Kilo Code is an MIT-licensed agent. Per kilo.ai, it runs in VS Code, JetBrains IDEs, a CLI and cloud agents, with 500+ models, bring-your-own-key and zero inference markup. The page lists an MCP FAQ without its answer, so we do not state MCP support. Pick it if you want one open-source agent in both VS Code and JetBrains.
• OpenHands is MIT-licensed. Per its GitHub README, it is a self-hosted control center, Agent Canvas, that runs the OpenHands agent or other agents such as Claude Code, Codex and Gemini CLI, locally, in Docker or on OpenHands Cloud (pricing not listed). Pick it if you want one self-hosted place to run several agents.

Amp

Amp runs each thread on a fresh remote machine it calls an orb, and its tiers are Hobby (free), Individual ($20) and Teams. Per ampcode.com/pricing on 29 October 2026, Hobby is pay-as-you-go for Amp-hosted orbs with unlimited self-hosted orbs and bring-your-own-keys, Individual is $20 a month with 45,000 minutes of orb time, Teams adds shared billing, pooled credits and SSO at no extra charge on top of member plans, and Enterprise is custom. Per ampcode.com, you can also run the CLI locally, link a ChatGPT subscription instead of spending credits, and run amp --no-tui on servers and CI.

Pick it if you want agents that keep working after you close the laptop.

Kiro

Kiro is a credit-priced agent with IDE, CLI and web surfaces. Per kiro.dev/pricing, Free is 50 credits, Pro is $20 for 1,000 credits, Pro+ $40 for 2,000, Pro Max $100 for 5,000 and Power $200 for 10,000, with add-on credits at $0.04 each. Credits do not roll over month to month. MCP servers are defined under mcpServers in a workspace or user JSON file (Kiro MCP docs).

Pick it if you want predictable monthly credits with an explicit per-credit overage price.

Why context is the bottleneck on a large repository

On a large repository, an agent spends most of its effort finding code, not writing it. Every agent above can read files and run grep. What differs is whether anything hands it the call graph, the history of a file or the decisions behind it, without it re-deriving them on each question.

That is the layer repowise provides. It indexes a repository once, locally, and serves it over MCP through 10 tools, so it runs under any agent in the table that speaks MCP. It is not a competitor to them. Its integrations page lists which agents get which depth: Claude Code and Codex CLI at the Full tier (MCP, hooks, instructions, skills and transcript mining), and Cursor, OpenCode, Kiro, VS Code and GitHub Copilot CLI at Good (MCP plus a managed instructions file). Any other MCP client is served with repowise agents print-config <id>.

repowise init --yes --no-prose
repowise agents print-config claude-code

What we measured, and on which agents

We measured the context layer under two agents, Claude Code and Codex, on the django/django repository, as published on /benchmarks. We did not measure Cursor, Copilot, Augment, Gemini CLI or any other agent.

In that benchmark, repowise on Codex gave an output-token change of -31.6% against a bare agent (n = 43 · p = <0.0001 · Codex (gpt-5.6-sol) · django/django at a single pinned commit · 2026-08), and 3.8 tool calls to an answer on average. On Claude Code the change was -15.9% (n = 15 · p = 0.035 · Claude Code (claude-sonnet-5) · django/django at a single pinned commit · 2026-08). The Claude Code figure is smaller and rests on fewer questions. The benchmark notes that Sonnet reaches for MCP tools less often than Codex does, so harness and model cannot be separated by that design.

Every question in django's question set, six arms, byte-identical prompts, each tool's full advertised surface, and a freshly built index on the same pinned commit. Every tool was called on every question, so this is like for like.

Codex (gpt-5.6-sol) on django/django at one pinned commit. Correcting for testing five tools at once, three reductions are solid and two are marginal. 5 of the 48 questions are missing from every arm equally because the run hit an API usage cap, so the figures are over the 43 questions all six arms completed; because all arms lost the same 5, the paired comparisons are unaffected.

This compares context tools on one agent, not agents against each other. A saving on one repository, one commit and one prompt is not a universal one; the benchmark page lists the caveats and the raw cells are in the public repowise-bench repository. The full method is on /benchmarks.

How to choose

Pick by where you work first, then by who pays for the model.

• Solo developer, terminal: Claude Code ($20) or Codex (ChatGPT Plus, $20), whichever subscription you already hold.
• Solo developer, editor: Cursor, or Cline and Roo Code if you want your own key.
• Team on GitHub: Copilot for delegation that ends in a pull request; Claude Code or Codex Business seats for terminal users.
• Team that wants pooled usage: Augment Code Standard or Business, where usage is shared across up to 50 seats.
• Model-agnostic or local models: Aider, OpenCode, Cline or Roo Code.
• Large or multi-repo codebase: whichever agent you choose, add a codebase index over MCP. Start with the options in Claude Code codebase indexing MCP options, and see Claude Code with repowise for the setup.