How to Compare AI Coding Agents

There are more than thirty serious coding agents you can install today. They feel wildly different. Open four of them and you'd swear they were built by rival species.

They're mostly the same five models in different costumes.

Claude, GPT, Gemini, and a short list of strong open-weight models do almost all the actual thinking. Most agents are a harness wrapped around one of them: the part that reads your repo, decides what to look at, runs commands, edits files, and recovers when a test fails. Once you see that, the question stops being "which agent is smartest" and becomes "which harness do I want to drive, on which model, for this task." That's a much more useful question, and it's the one this post is about.

Benchmarks won't pick your agent

The standard way to compare agents is a leaderboard — SWE-bench, a terminal benchmark, a number next to each name. It's the wrong tool for this decision, for three reasons.

They measure the model, not the harness. Most of a benchmark score comes from the model underneath, and the model is the part you can swap. A ranking of agents is mostly a ranking of whichever frontier model each one happened to be pointed at that week.

They measure a task shape you don't have. The classic benchmark is "here's a GitHub issue, produce a patch that passes hidden tests." Real work is ambiguous, half-specified, spread across services, and judged by a human. An agent that tops the patch-the-issue game can still be exhausting to work with on a vague feature request.

They churn and they saturate. A new model lands, one harness gets to it first, the order reshuffles, and three weeks later it's stale. The top scores now cluster so tightly that the gaps are noise.

Use a benchmark as a coarse filter — is this model capable enough to trust with real code — and then ignore it. What you feel every day isn't on the leaderboard.

What actually differs

Strip away the model and the score, and agents separate along a handful of axes that genuinely change how it feels to work with them.

• Model strategy. Locked to one lab (Claude Code is Claude, Codex is GPT) or bring-your-own across many providers (opencode, Aider, Cline). Locked agents are tuned tightly to their model and tend to get new capabilities first. Open agents let you chase the best — or cheapest — model without changing tools, and let you run a model you host yourself.

• Context strategy. How the agent figures out what to read before it acts. Naive agents grep and hope. Serious ones build an index, do semantic search, or carry a persistent map of your codebase. On a small repo it barely matters. On a million-line monorepo it's the whole game.

• Autonomy level. Where it sits on the line from "suggests one edit and waits" to "disappears for twenty minutes and comes back with a branch." Pairing-style tools keep you in the loop on every change. Autonomous ones are leverage when the task is well-specified and a liability when it isn't.

• Permission and safety model. What it will do without asking — edit files, run shell commands, install packages, hit the network. This is the difference between an agent that feels safe to let loose and one you have to babysit.

• Where it runs. A terminal CLI you can script and drop into any workflow, versus something welded to one editor or living only in someone's cloud. Terminal-native agents compose. The rest you adapt to.

• State and cost shape. Whether it remembers anything between sessions, and whether you pay a flat subscription, metered tokens, or nothing because you brought your own key. These quietly decide whether you actually reach for it.

None of these show up in a score. All of them decide whether you keep the agent after a week.

The field, by archetype

Here's the whole field Agentastic supports — 33 agents — grouped by what kind of thing each one actually is. The takes are opinionated on purpose.

The frontier labs' own CLIs

The model makers shipping their own harness. Locked to their model, tuned for it, usually first to a new release.

Bring-your-own-model agents

Open, provider-agnostic, often self-hostable. The harness is the product; you choose the brain. This is where most experimentation lives.

Specialists

Each does one thing other agents treat as an afterthought.

Agents that meet you where your work lives

From companies whose agent plugs into a product you may already use.

Review specialists

Not builders. Point them at a diff and they tell you what's wrong.

And anything not on this list still works — point Agentastic at any terminal CLI in Settings → Connections and it becomes an agent too.

How to choose without overthinking it

You don't need the perfect agent. You need a small kit and the judgment to match it to the task.

• One heavyweight for hard, multi-file work — a frontier-lab CLI on its best model. This is where capability actually pays for itself.
• One open, bring-your-own agent for the long tail and anything cost- or privacy-sensitive — pointed at a cheaper or self-hosted model. Most tasks don't need the frontier.
• One specialist if your bottleneck has a name — search on a monorepo, memory across sessions, autonomous background runs.
• A reviewer on the diff before you merge.

The trap is treating this as a marriage. The best model moves every few weeks, the best harness for today's task isn't the one for tomorrow's, and the cost of guessing wrong compounds if switching means relearning your tools.

So don't marry one. The skill that actually compounds isn't picking the winner — it's orchestration: running two agents on the same problem and keeping the better diff, handing the boring half to a cheap agent while the expensive one does the thinking, reviewing output instead of babysitting it.

That's the bet Agentastic makes. Every agent runs in its own git worktree or container, so you can launch three of them on the same repo at once without them stepping on each other. Whatever produced the diff, you review it the same way — one surface, merge or delete. Auto-approve, resume, and plan mode are normalized across agents, so switching is a dropdown, not a weekend.

The honest conclusion of any agent comparison in 2026 is that there is no winner that stays won. The developers moving fastest aren't the ones who picked right. They're the ones who never had to pick just one.