Claude Code — best for serious software development

Best for: developers working on real codebases.

Think of it as an AI engineer working alongside you, not a chatbot writing snippets. Claude Code works directly with a codebase. It runs commands, debugs, and carries larger engineering tasks through to completion. Anthropic currently offers it across terminal, web, IDEs, GitHub, and other environments.

Where it shines: understanding existing codebases, debugging, refactoring, feature development, running tests, multi-step engineering tasks, larger projects.

Less suitable for: someone who occasionally asks AI to explain a few lines of code.

Cost: Claude Pro is $20/month billed monthly, or $17/month effective with annual billing, and Claude Code is included. Max plans start at $100/month for heavier usage. Usage limits apply.

OpenAI Codex — best for coding inside OpenAI’s ecosystem

Best for: developers already invested in OpenAI.

Codex is OpenAI’s coding-focused agent, built for real engineering work — bugs, features, complex refactoring — rather than answering coding questions. OpenAI describes it as available through desktop, web, and other development workflows.

Where it shines: coding, debugging, refactoring, feature development, working across projects, delegating engineering tasks.

Cost: Codex is included with ChatGPT plans. OpenAI currently lists Plus at $20/month, Pro at $100/month, and Business at $20/user/month billed annually. Usage varies by plan.

GitHub Copilot — best for GitHub-centered development

Best for: developers who live in GitHub, VS Code, and the Microsoft developer ecosystem.

Copilot is a different kind of choice from a standalone autonomous agent. Its biggest advantage is the environment around it. If your workflow already runs GitHub → repositories → issues → pull requests → IDE → code review, Copilot slots in without much friction.

Best for: everyday coding assistance, GitHub workflows, teams, code completion, pull-request workflows, developers who want AI inside the tools they already use.

Don’t pick it on model benchmarks. Pick it because it fits where your code already lives.

Devin — best for delegating larger coding tasks

Best for: teams that want to hand off whole tasks rather than assist their own work.

Devin sits at the more autonomous end of the coding spectrum. Instead of suggesting code in your editor, it takes a task and works through it — planning, writing, running, iterating. That makes it appealing for well-scoped work you’d rather not do yourself, and less appealing when you want to stay in the loop line by line.

Choose it if you want to delegate, not pair. Look elsewhere if you want tight, in-editor control — Claude Code or Copilot will suit you better.

The question with Devin isn’t whether it can code. It’s how much oversight you’re comfortable giving up.

ChatGPT — best general-purpose AI platform

Best for: people who want one platform that handles many kinds of work.

ChatGPT isn’t just a chatbot anymore. Depending on the plan, it covers conversation, research, files, coding, images, and agent-style tasks. OpenAI’s documentation separates ordinary ChatGPT use from Codex for software development, while plans provide different levels of access to research and coding.

Best for: general productivity, research, writing, analysis, files, coding, mixed workflows, and people who don’t want five AI subscriptions.

Cost: OpenAI currently offers Free, Go, Plus, Pro, Business, and Enterprise tiers, with capabilities and limits varying by plan.

It’s the easiest starting point if you want one platform covering a wide range of work. But that doesn’t make it the best at every individual job. A specialist coding agent beats it for software development. A specialist enterprise platform beats it for governed automation.

Claude — best for long-form reasoning and document work

Best for: analysis, writing, and working through dense material.

Claude tends to get chosen for the same reason Claude Code does: it handles long context and structured reasoning well. If your work involves reading, synthesizing, or producing substantial documents, it’s worth comparing directly against ChatGPT and Gemini rather than assuming they’re interchangeable.

Choose it if your work is document-heavy or reasoning-heavy.

Cost: Claude Pro is $20/month billed monthly, or $17/month effective with annual billing.

Gemini — best for the Google ecosystem

Best for: people and businesses already heavily invested in Google.

Gemini gets more interesting when the AI isn’t working in isolation. If your organization already runs on Google’s ecosystem, the value comes from combining AI with your existing information, applications, and workflows. Google’s enterprise offering now includes connectors, agent management, no-code workflow building, and enterprise governance.

Best for: Google Workspace users, business research, enterprise knowledge, multimodal work, Google Cloud users, and building business agents.

Cost: Gemini Enterprise lists plans starting around $30 per user/month for its Standard/Plus enterprise tier, while Google Cloud agent infrastructure and model usage can add separate consumption charges.

If your company already runs on Google, evaluate Gemini as an ecosystem — not as another chatbot.

Perplexity — best for researching the public web

Best for: questions where you need current sources, not a synthesized answer from training data.

Perplexity is built around search and citation. It’s the natural first stop when the answer depends on what’s published right now, and when you want to check the sources yourself rather than trust a summary.

Choose it if source quality and recency matter more than conversation.

ChatGPT Deep Research — best for multi-step research reports

Best for: detailed investigations that take time.

Good when you need an AI to investigate a question rather than answer it from what it already knows. Deep Research is designed for work that involves following a thread across multiple sources and producing something structured at the end.

Gemini and Claude in research

Gemini is the stronger choice if your research lives inside Google’s ecosystem. Claude is the stronger choice for long-form analysis and document-heavy work.

So if the job is “I need a serious research report,” compare these directly. Don’t pick based on the nicest first answer. Compare research depth, source quality, citation handling, ability to follow a research question, report quality, speed, and cost at your expected usage.

Microsoft Copilot — best for Microsoft 365 businesses

Best for: companies already running on Microsoft 365.

Copilot works alongside Word, Excel, PowerPoint, Outlook, and Teams, and Microsoft provides agent capabilities through its business platform.

Best for: Microsoft 365 users, internal business knowledge, meetings, email, documents, enterprise workflows, custom business agents.

Cost: Microsoft currently lists Microsoft 365 Copilot at $30/user/month paid yearly, with an eligible Microsoft 365 subscription also required. Agent usage can involve separate metered billing depending on configuration and data access.

Gemini Enterprise — best for Google-based businesses

Covered above, but worth repeating here: for enterprises, Gemini’s value is the ecosystem around it. Connectors, agent management, no-code workflow building, and governance are the parts that matter at scale.

Salesforce Agentforce — best for CRM and customer operations

Best for: organizations already operating heavily inside Salesforce.

The major advantage here isn’t the AI. It’s the business context. A CRM agent can work with customer records, sales processes, service information, and business workflows instead of operating as a disconnected chatbot.

Good for: sales, lead qualification, customer service, CRM tasks, service workflows, enterprise automation.

Choose it if Salesforce is already central to your business. Look elsewhere if you don’t use Salesforce and don’t need its enterprise ecosystem.

ServiceNow — best for enterprise IT and service management

Best for: organizations whose work runs through IT service management, incident handling, and internal service workflows.

ServiceNow occupies a specific but important niche. If your operational work already lives in its platform, agents there can act on real tickets, real assets, and real processes rather than operating at the edges of your business.

Intercom Fin — best for AI customer support

Best for: support teams that want AI handling front-line conversations.

Fin sits inside Intercom’s helpdesk and works on the customer-facing side of support: answering questions, resolving common issues, and handing off to humans when the conversation needs one. The appeal isn’t raw model capability. It’s that it’s already plugged into where support work happens.

Choose it if your support already runs through Intercom, or you want a support-specific agent rather than a general assistant bolted onto a helpdesk.

Watch for resolution quality and escalation behaviour. Those two things determine whether an AI support agent helps your customers or annoys them.

Zapier Agents — best for accessible cross-app workflows

Best for: people who want AI working across the software they already use.

Zapier’s strength has always been connecting apps. Adding agents means those connections can involve decisions rather than fixed rules. Picture it: new lead → research → update CRM → draft email → notify sales → schedule follow-up.

Good for: email, CRM, calendar, Slack, forms, lead workflows, administrative tasks.

Lindy — best for agent-first automation

Best for: users who want to describe an outcome and let the agent figure out the steps.

Lindy leans further into the agent side than Zapier does. You describe what you want to happen, and it works out the mechanics. That flexibility is the appeal, and also the thing to test carefully before trusting it with anything consequential.

n8n — best for technical automation with AI

Best for: technical users who want flexible, self-hostable workflow automation.

n8n gives you more control than the easier tools, at the cost of more setup. It’s the natural choice when you want automation you can inspect, modify, and run on your own infrastructure.

Make — best for visual, multi-step workflows

Best for: building complex workflows visually.

Make sits between Zapier’s simplicity and n8n’s flexibility. If your workflows have branches, loops, and multiple stages, its visual builder handles that better than a simple trigger-action model.

Don’t overcomplicate it, though. If your workflow is simply “when X happens, do Y,” ordinary automation may beat an autonomous agent. Use agents when the workflow needs judgement. Use plain automation when the steps are predictable.

ChatGPT Agent — best for general-purpose agentic work

Best for: tasks that span browsing, files, and multi-step reasoning.

ChatGPT Agent takes the general assistant and gives it the ability to actually carry out multi-step tasks rather than describe them. It’s the pragmatic entry point if you already use ChatGPT and want to see what agentic behaviour looks like on your own work.

Manus — best for larger open-ended projects

Best for: projects where the path isn’t obvious and you want the agent to plan.

Manus targets the more ambitious end of autonomous work: bigger tasks, longer horizons, less structure. The tradeoff is the usual one. More autonomy means more variance, and more need to check the result before you use it.

Browser and computer-use agents

This is one of the more interesting corners of the ecosystem. Traditional software integration normally requires an API. Computer-use agents interact with interfaces instead. They open websites, navigate pages, click buttons, enter information, read screens, and complete multi-step tasks. Useful for sites and software without convenient APIs.

It also introduces a major issue: reliability. A human can tell when a website has changed its layout. An agent may not.

So computer-use agents are particularly interesting for tasks where browser interaction is unavoidable. But evaluate reliability and human approval carefully before letting them perform consequential actions.