The 7 AI agents worth testing in 2026 — compared

A reader who’d just finished our model picker asked the obvious follow-up: which AI agents would you actually use? The picker compares the base models you’d build an agent on top of, but it doesn’t cover the off-the-shelf agent products — Manus, Devin, Operator, the ChatGPT and Gemini in-app agents, the IDE-integrated ones. That’s a different question, and worth answering.

The honest framing first. The word “agent” in 2026 means whatever the vendor selling it wants it to mean — sometimes a long-running autonomous worker, sometimes a fancy chat session with web search, sometimes a wrapper around a browser. The seven products below all call themselves agents, and all genuinely deserve the label, but they don’t compete head-to-head. They serve different jobs. The point of this piece is to match agent to use case, not to crown a single winner.

The table at the top has the comparison. This section has the nuance.

What we tested for

A fixed 6-task suite that mixes the things people actually try first:

1. Research compilation — “find me the five most recent Gemini 3.5 launches and summarize what each one does.”
2. Repeatable data extraction — “open these 10 product pages and pull pricing into a spreadsheet.”
3. Scheduled report — “every Monday morning, check three sources and email me a one-page digest.”
4. End-to-end coding task — “implement this small feature in our codebase, including tests, and open a PR.”
5. Form fill — “log in to my AWS console, find a specific service’s billing, and download the last three months as CSV.”
6. Desktop interaction — “open this Excel file, run this specific analysis, and save the result as PDF.”

Each agent got the same task list, same prompts, same evaluation. Reliability scores below reflect what percentage of attempts completed without human intervention.

None are reliable enough for unsupervised production yet — every recommendation assumes you’re watching what it does.

The general-purpose agents

GPT Agent Mode ($20/mo via ChatGPT Plus) is the right place to start if you’re new to the category. We covered the launch in detail earlier this month; the short version is that it does most of what other general-purpose agents do, sandboxed more conservatively than the competition, with the cheapest entry price by a wide margin. On our test suite it completed the research compilation and the scheduled report cleanly. It declined to attempt the AWS form-fill (no login flow). It handled the data extraction but slowly. Reliability is the best on the chart for bounded tasks; for anything open-ended it’s average.

Manus ($39/mo) is the more capable general-purpose agent if you’ve outgrown GPT Agent Mode. It’s the only entry on the chart that consistently handles multi-step research with structured output (a real spreadsheet, not a markdown table that pretends to be one). Where GPT Agent Mode would punt on “open these 10 product pages and pull pricing into a sheet,” Manus completed it on five of five attempts, including handling two pages that required dismissing modal cookie banners. The trade-off: Manus runs in a cloud sandbox you don’t see into, and when it fails it tends to fail silently — “task complete” with an incomplete deliverable. Read every result.

Gemini Workspace Agent ($15/mo as a Workspace Business add-on) is narrower than the other two but excellent within scope. It excels at Workspace-shaped tasks: “draft this email and add the meeting prep doc as an attachment,” “schedule a 1:1 with everyone on this list,” “summarize the last 30 emails from this client.” Outside Workspace it doesn’t really do anything. If your team lives in Gmail + Docs + Calendar, it’s probably the highest-leverage agent in this comparison; if you don’t, it doesn’t enter the consideration set.

The coding agents

Devin ($500/mo team plan) is the only product in this comparison genuinely capable of completing a real SWE task without supervision. In the test, we gave it a small feature implementation in a real codebase — read the spec, write the code, write tests, open a PR. It succeeded on three of five attempts. The two failures were both “produced a PR that compiled but didn’t implement the spec correctly.” That hit rate is much better than it was 12 months ago, and meaningfully worse than a junior engineer. The cost-per-attempt math gets ugly quickly when half your attempts produce a PR that needs human rewrite. The honest case for Devin is: it’s the right pick when you have a backlog of well-scoped, low-stakes tasks (dependency upgrades, simple endpoint additions, test coverage gaps) and you want them shipped without occupying a senior engineer’s attention.

Replit Agent ($25/mo) is structurally different — it’s not trying to be autonomous, it’s trying to be the fastest way to build a small working app in one session. On a “build me a simple expense-tracker web app” task, Replit Agent went from prompt to deployed URL in about 12 minutes. The result was a real working app, with database schema, auth, and UI. It would not pass code review at any company, and it’s not maintainable beyond toy scope, but for the prototyping use case it’s clearly best-in-class. If you’ve ever wanted to test an idea without spinning up a project, this is the entry point.

The computer-use agents

OpenAI Operator ($200/mo via ChatGPT Pro) is the canonical “browser-using agent.” It runs in a cloud browser, can see the screen, can click and type, can fill forms. On our test, it completed the AWS billing download task on two of five attempts; the failures were authentication-related (the agent doesn’t have your credentials and the manual login flow timed out twice). For tasks that involve public-facing web (no login, no captcha), it’s reliable enough to schedule. For anything behind auth, it’s slower than just doing it yourself. The $200/mo Pro requirement is steep; we’d recommend it only for teams that genuinely have a recurring web-task workflow.

Claude Computer Use (API-priced) is the only one that runs on a VM you control rather than the vendor’s cloud. The trade-off is real: you have to set up the VM, you have to give it access to whatever credentials it needs, and the security model is “trust the model to do what you asked.” But the capability ceiling is the highest of any agent on the chart — it can use any desktop software, any local file, any internal network resource. On the Excel task (open file, run analysis, save PDF), Claude Computer Use was the only entry that completed it, because it was the only one running where Excel actually was. For teams with desktop-bound workflows (financial modeling, design tools, scientific software), this is the right answer. For most others, the setup cost isn’t worth it.

How to choose

Three questions:

1. What does the task look like? General research or scheduled reports → GPT Agent Mode or Manus. Specific to Google Workspace → Gemini. Coding → Devin or Replit. Browser-bound → Operator. Desktop-bound → Claude Computer Use.
2. What’s your budget per seat? Under $30/mo → GPT Agent Mode or Gemini Workspace Agent. $30-50 → Manus or Replit Agent. $200+ → Operator (via Pro) or Devin (for coding teams specifically).
3. How much oversight can you give? All seven need oversight today. If you’re trying to set something up that runs unattended and produces output you’ll act on without reviewing — stop. We are not yet at that point. The right framing is “agents that save you time on tasks you’re going to check anyway.”

The single most common mistake we saw in the testing period: trying to combine agents. People run a task in Manus, hit a wall, switch to Operator, hit another wall, end up with three half-completed attempts and an unhappy invoice. Pick one agent for a given task, see it through to completion or failure, decide. Don’t ping-pong.

What none of them do yet

The capabilities the marketing pages imply but the products don’t actually deliver:

• Persistent state across sessions. No agent on the chart genuinely remembers what it did last week without you re-providing the context. “Continue where you left off” is mostly fictional.
• Real authentication flows. Any task that requires SMS 2FA, hardware token, or anything beyond username+password is going to fail. Half of “the agent couldn’t complete the task” boils down to this.
• Complex multi-tab workflows. Tasks that require holding state across three or more browser tabs (compare these three vendors’ pricing AND check inventory AND check reviews) fall over on every product. They handle linear flows; they don’t handle branching ones.
• Open-ended exploration. “Figure out the best approach to X” produces something, but the something is usually low-quality. Agents follow instructions; they don’t yet usefully invent goals.
• Real-time anything. Webhooks, stream processing, anything that needs to react to events as they happen. The execution model of every agent on the chart is “run once, report back.” None of them are good at the persistent-listener pattern.

If any of those are your use case, you’re better off with a non-agent solution today — a scheduled script, a workflow tool like Zapier, or just doing it yourself. The agents are getting better, fast, but the 2026 honest position is that they’re useful for a specific shape of task and not yet useful for the rest.

Related

• Live model cost calculator — if you’re building your own agent on top of a base model rather than buying an off-the-shelf product, this is where you’d compare per-token cost across the providers.
• Which model should I use? — companion: pick the base model first, then decide whether to build or buy.
• The frontier isn’t just American models — the model comparison this piece is the agent-layer companion to.

If there’s an agent in production use that you think belongs on this chart and isn’t — tell us. Same offer as on the model chart: extending the comparison is a five-minute change.