Coding AI Decision Hub: Model, Agent, IDE or Platform?

“AI coding tool” is now a remarkably broad category.

A terminal agent, an AI editor, a cloud worker that returns a pull request, a multi-agent command centre and an open BYOK harness can all receive the same label.

This is convenient right up to the point where you try to compare them.

Before choosing a product, separate four layers.

Model

The model is the reasoning and generation layer.

It matters. It is not the whole product.

The same coding surface may support several models, while the same model can appear inside different agent harnesses.

That means a model leaderboard does not automatically tell you which development workflow will suit you.

Agent Harness

The harness decides what the model can see and do.

It can manage repository context, file editing, shell access, tests, git operations, browser or MCP tools, approvals, permissions and delegation loops.

Cursor’s own documentation is a useful illustration: its Agent is described as a combination of instructions, tools and the model selected for the task.

This is why changing the surface around the same model can change the practical result.

IDE / Development Surface

The next question is where you want to work.

Editor / IDE

If you want AI inside the place where you already read and edit code, an editor-first product such as Cursor or the IDE side of Copilot may fit naturally.

Terminal

If repository work, shell commands and explicit command-line control are central, Claude Code, Codex CLI or OpenCode-type surfaces become more relevant.

Desktop / command centre

If your work increasingly means supervising several agents or long-running tasks, agent-management applications such as the Codex app or Antigravity-type command centres represent another interaction model.

GitHub / cloud

If you want to hand over an issue or bounded task, continue with your day and receive a branch/PR/diff to review, cloud-agent and GitHub-native surfaces are a different category again.

The question is not merely where the model runs.

It is where delegation begins and review ends.

Platform

Once agents work asynchronously, in parallel or across the PR lifecycle, the product starts behaving like part of the development platform.

Then the relevant questions include:

• background work
• parallel work
• PR lifecycle
• policies and permissions
• audit
• team controls
• scheduled tasks
• integrations

At this point, “which coding assistant has the nicest chat?” is no longer quite the procurement question.

Choose by purpose

Stay in flow

Prefer an editor/IDE-oriented surface when you want constant, low-friction assistance while you remain the primary operator.

Delegate a bounded task

Prefer a native/cloud agent when the valuable capability is handing over a task and reviewing the resulting work later.

Choose your own provider/model

Consider an open or BYOK harness when provider choice, local models or API control are part of the requirement.

OpenCode currently documents broad provider support, including local models. Cline also exposes provider/model configuration. That flexibility comes with more responsibility for keys, costs and compatibility.

You want an application, not a coding workflow

Then this may be the wrong category.

If the desired output is a working application and the repository is only an implementation detail, an app-builder specialist may fit the purpose better.

Small, high-accountability work

Human + limited assistance is a perfectly normal result.

A small production change with a high failure cost does not become safer because the agent can edit more files.

Five comparison axes

Development Surface

Terminal / IDE / Desktop / Web / GitHub / Cloud.

Agent capability

Repo understanding, multi-file work, shell, tests, git/PR, browser, MCP, subagents, parallel and background execution.

Control

Permissions, sandboxing, approvals, diffs, rollback and audit.

Control is part of agent evaluation. It is not a universal product-quality score.

Model/provider

Fixed model, model choice, BYOK or local.

Economics

Subscription, included allowance, credits, API use and overage.

Do not freeze the economics into a permanent table. Coding-agent plans and included usage have been moving far too quickly for that to age gracefully.

Current Fact Box — GitHub AI Credits

GitHub’s current usage-based billing uses AI Credits. A legacy exception remains for existing Copilot Pro and Pro+ annual subscribers who stayed on request-based billing after 1 June 2026. Do not combine those billing models in one supposedly universal allowance table; check the account’s actual plan and billing model.

Representative products — not a ranking

The table deliberately has no Overall Score.

Why the model should not be the first decision

Coding quality obviously depends on the model.

It also depends on what context the agent receives, which tools it can call, what it can execute, how permission is handled and how the human reviews the change.

A brilliant model with the wrong context is still wrong with confidence.

A highly autonomous agent with poor review ergonomics may be impressive and inconvenient at the same time.

A perfect editor companion may be the wrong choice for work you want to delegate overnight.

So the sequence is:

1. Define the job.
2. Choose where you want to work.
3. Choose how much you want to delegate.
4. Choose the control boundary.
5. Then compare the models available inside that setup.

Calling all of these “AI coding tools” is technically convenient in the same way that calling a compiler, an IDE and a developer “computer stuff” is convenient.

Conclusion

Choose the interaction and delegation model first.

Do you want an AI next to you in the editor? A terminal agent? A cloud worker returning a PR? A multi-agent platform? A flexible harness where you choose the provider?

Once that is clear, the relevant product set shrinks dramatically.

Then model capability becomes a much cleaner comparison.

And for small high-accountability changes, Human + limited assistance remains valid.

What would change our mind?

• the editor/terminal/cloud boundary largely disappears
• task/context portability becomes standard across agents
• permission and review models converge
• model differences overwhelmingly dominate harness differences
• BYOK/local becomes frictionless in mainstream surfaces
• usage and pricing models materially change