Claude Code vs. Codex vs. Gemini vs. Cursor: Choosing the Right Agent for Each Task

The Question Is Wrong

"Which AI coding agent is best?" is the question everyone asks, and it's the wrong one. It's like asking which tool is best — a hammer, a saw, or a wrench. The answer depends entirely on what you're trying to do. The agents that lead on long-horizon terminal work aren't always the ones you'd reach for to refactor an unfamiliar repository, and the one you trust for a careful security review isn't necessarily the cheapest way to grind through a hundred mechanical edits.

The teams getting the most out of AI agents have stopped looking for a single winner. They've realized the leverage is in matching the task to the agent — using each tool where it's strongest and not where it isn't. This guide is about how to think that way: what the major agents are good at, and how to route work accordingly.

A caveat before the breakdown: the specific capabilities of each model move fast, and any ranking is a snapshot. So treat the strengths below as a framework for reasoning about fit, not a permanent leaderboard. The durable skill is knowing how to match work to a tool — that outlasts any individual release.

A Field Guide to the Major Agents

Claude Code

Claude Code tends to shine on work that requires understanding a codebase before changing it — multi-file refactors, bug localization in unfamiliar territory, and the kind of careful, rigorous review where committing to the wrong file early is costly. It's strong at holding a lot of context and reasoning about how parts of a system relate before it acts. When the hard part of the task is figuring out what to change rather than typing it out, it's a natural fit.

Codex

Codex is the terminal and execution specialist. It's fast and decisive at getting a concrete thing working and verifying it by running code — building, compiling, wrangling CI, setting up environments, and iterating in a run-it-read-the-output-fix-it loop. For well-specified, self-contained implementation work and for bulk or mechanical changes, it's often the quickest path to a working result, and frequently the cheapest.

Gemini

Gemini brings very large context windows and strong multimodal reasoning, which makes it useful when a task involves a lot of material at once — large documents, sprawling context, or inputs that aren't purely text. When the challenge is breadth of context rather than depth of a single-file change, it's worth reaching for.

Cursor

Cursor is built around the tight, interactive inner loop of editing code — fast, in-editor assistance where you're steering closely and want quick, responsive edits in the flow of writing. It's strong for the kind of hands-on work where you're collaborating with the agent line by line rather than handing off a whole task.

Kimi

Kimi rounds out the set as another capable long-context option, useful for adding model diversity to your routing — a different set of strengths and failure modes to draw on when a task isn't a clean fit for the others.

Why One Tool for Everything Costs You

Standardizing on a single agent is tempting. One subscription, one interface, one mental model. But it quietly costs you on two fronts.

Quality. Every model has tasks it's mediocre at. If you route everything to one agent, you eat that mediocrity on every task outside its strengths — the unfamiliar-repo refactor that needed deeper code reasoning, the long-horizon build that needed an execution specialist. You never see the better result you'd have gotten from a tool that fit, because you never ran it.

Money. Using a premium model for a hundred trivial, mechanical edits is like hiring a senior architect to rename variables. It works, but you're overpaying for capability the task doesn't need. Conversely, sending genuinely hard reasoning work to the cheapest model to save a few cents produces output you throw away — the most expensive kind. Matching the task to the agent is as much a cost decision as a quality one.

The single-tool approach optimizes for convenience. Multi-agent routing optimizes for outcomes.

How to Route Work in Practice

You don't need a rigid rulebook — a few heuristics get you most of the way:

• Match the task's hard part to the agent's strength. Is the difficulty understanding the code, executing and verifying it, holding a lot of context, or staying in a fast interactive loop? Route to the agent that owns that dimension.
• Send mechanical and bulk work to fast, cheap, execution-focused agents. Don't pay premium reasoning rates for repetitive edits.
• Reserve your strongest reasoning agents for the genuinely ambiguous work — design, unfamiliar-codebase navigation, rigorous review.
• Use model diversity as a feature. For high-stakes work, a second agent with different failure modes is a cheap way to catch what the first one missed.

The obstacle has never really been knowing this. It's that acting on it traditionally meant juggling five different tools, five interfaces, five subscriptions, and a constant tax of context-switching — copying work between environments just to use the right agent for each step. That overhead is exactly why most people give up and standardize on one tool.

How Medley Makes Routing Practical

Medley exists to make multi-agent routing the easy path instead of the hard one. It's a single command layer that sits over Claude Code, Codex, Gemini, Cursor, and Kimi, so you can use the right agent for each task without leaving one workflow.

You describe a mission; Medley decomposes it into a plan and routes each piece to an appropriate agent — and you can see and adjust those assignments rather than having them hidden from you. Multiple agents can work in parallel, and everything flows through a single Attention Queue so you're not babysitting five terminal tabs. Because it tracks each task with its own cost, you can actually see how a given kind of work plays out on different agents and tune your routing over time.

In other words, Medley turns "use the best agent for each task" from a nice idea that's annoying to execute into the default way you work.

The Best Agent Is a Portfolio

The frontier of AI coding isn't a single model pulling away from the pack — it's a set of strong, specialized agents, each best at different things, all improving fast. Betting your entire workflow on one of them means betting against that reality. The teams that win with AI agents treat them as a portfolio: the right tool for each task, switched without friction, measured so the routing gets smarter over time.

You don't have to pick a winner. You have to pick well, task by task — and use something that makes picking well effortless.

That's what Medley is for: every major AI coding agent, one command layer, routed to fit the work in front of you. It's free, local-first, and runs on your Mac. Start at medley.sh.