AI Coding Agents Compared: Inline, Terminal, or Asynchronous
Three categories, distinguished by how much review they generate. Leaderboard position predicts almost nothing about your repo.
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Quick answer: AI coding agents fall into three categories: IDE-embedded assistants that complete and edit inline, terminal and CLI agents that run multi-step tasks against a repository, and asynchronous agents that work from an issue and open a pull request. Pick by task shape — inline for editing, terminal for multi-file changes, async for well-specified independent work.
Benchmark scores for coding agents have climbed steeply and tell you remarkably little about which one to adopt. The scores measure a model plus a harness on curated GitHub issues. Your repository is not a curated GitHub issue.
This comparison covers the three categories of AI coding agent as they exist in late 2026, what each is genuinely good at, why benchmark leaderboards mislead when choosing between them, and how to evaluate one against your own codebase.
The three categories of coding agent
They differ in how much autonomy they take and how much review they generate, which turns out to be the axis that matters operationally.
| Category | Works on | Human involvement | Best for |
|---|---|---|---|
| IDE-embedded | Current file and nearby context | Continuous | Editing, refactors, completion |
| Terminal / CLI | Whole repository | Supervised, interactive | Multi-file changes, debugging |
| Asynchronous | An issue, then a PR | Review at the end | Well-specified isolated tasks |
IDE assistants are the lowest-risk and lowest-ceiling option. They work inside what you can see, you review every suggestion as it appears, and they rarely produce a large change you did not anticipate. The failure mode is subtle: plausible completions that compile and are wrong.
Terminal agents are where most of the capability gain has happened. They read the repository, run tests, iterate on failures, and make coordinated changes across files. Supervision is interactive rather than continuous, which changes the review model from per-line to per-step.
Model choice sits underneath all three categories and is frequently configurable. The same tool pointed at a flagship model versus a mid-tier one produces different results at very different costs, which means a tool comparison run with different backing models is not a comparison of the tools at all — a version of the scaffolding problem described in our comparison of the two flagship models that launched at identical prices. Fix the model before comparing harnesses.
Asynchronous agents take an issue and return a pull request. The ceiling is high and the review burden lands entirely at the end, in one block, on a diff nobody watched being written.
Why coding leaderboards mislead when choosing a tool
Because the score belongs to a system, not a product. SWE-bench evaluates whether a patch resolves a real GitHub issue from a Python repository and passes its tests — a genuinely useful measurement that resembles almost nobody's daily work.
Four gaps separate a leaderboard position from a purchasing decision. The scaffolding around the model — retry budget, test harness, verifier — moves scores by double digits on identical weights. The benchmark's repositories are Python and open-source, and your codebase probably is not both. Contamination is plausible for any public repository. And the issues are well-specified, which is exactly what real tickets are not.
Recent research has argued that coding benchmarks are structurally misaligned with agentic software engineering, and the methodology literature on arXiv's software engineering section covers this in detail. Model Drop has not reproduced any published agent score independently, and we would treat the gap between benchmark and repository as the default assumption rather than the exception.
What actually predicts performance is context gathering. An agent that finds the right five files in a large repository outperforms one with a stronger model that reads the wrong ones. That capability is architectural — retrieval, indexing, repository navigation — and no leaderboard isolates it.
What agentic runs actually cost
More than people budget for, because agents emit a great deal of output and output is the expensive half.
A multi-step agent run that reads files, proposes changes, runs tests, and iterates can easily emit 40,000 output tokens. At flagship pricing near $50 per million output tokens that is about $2.00 per run, before input. Fifty runs a day across a team is $3,000 a month, and failed runs cost the same as successful ones.
We have watched that failed-run cost surprise teams the most. A run that iterates through six failed test attempts before giving up consumed roughly the same tokens as one that succeeded on the first try — the meter runs the same either way, and a team that only tracks cost on merged changes is quietly undercounting its real spend by the abandoned-run share, which on a new codebase can run 20-30% of all attempts.
Tier routing matters as much here as anywhere. Many agent steps — reading a file, summarizing a diff, formatting output — do not need a flagship model, and the ladder that makes this worth engineering is covered in our breakdown of LLM API pricing across the 2026 tiers.
Watch the retry multiplier too. An agent configured to retry aggressively produces better benchmark scores and proportionally larger bills. Benchmark configurations and cost-sensible production configurations are frequently not the same configuration.
How do you actually evaluate a coding agent properly?
Run it against ten real closed tickets from your own tracker, using issues already resolved by a human so you have a known-good diff to compare against. Measure cost per merged change and how long the review took, not just whether the patch passed tests — on your repository, with your tests, not a public leaderboard.
- Pick ten real closed tickets. Use issues already resolved by humans, so you have a known-good diff to compare against. Mix trivial, moderate, and genuinely hard.
- Measure completion, not correctness alone. Track how many produced a mergeable change, how many needed one round of correction, and how many were abandoned.
- Time the review. An agent that saves 40 minutes of writing and costs 30 minutes of careful review saved 10 minutes, not 40.
- Log token spend per task. Cost per merged change is the number to compare across tools.
Test coverage is the hidden variable in all of this. Agents that can run tests and iterate on failures perform dramatically better in well-tested repositories, because the test suite acts as the verifier. In a repository with thin coverage, the same agent produces changes nobody can validate cheaply — and the review burden absorbs the productivity gain.
A team we have talked with ran the same coding agent against two internal services: one with roughly 80% test coverage, the other closer to 20%. The well-tested service saw a genuinely useful completion rate on real tickets; the thinly-tested one produced changes that looked plausible and required a full manual re-verification almost every time, which erased essentially all of the time saved. The agent did not get worse. The verification signal it depended on simply was not there.
Repository size is the other variable that separates demos from production. Agents perform well on small, conventionally-structured codebases and degrade on large ones with unusual layouts, generated code, or deep inheritance. If your repository is a monorepo with a decade of history, evaluate there rather than on a service you extracted last quarter.
Security review deserves explicit handling as well. An agent that pulls in a new dependency, changes a permission check, or touches authentication code needs a different review standard than one renaming a variable. Standards frameworks such as the NIST AI Risk Management Framework are a reasonable starting point for deciding which categories of change require a human sign-off rather than a skim.
At Model Drop our observation is that teams evaluating agents usually discover a testing problem before they discover an agent preference. That is a useful finding even if it is not the one they were looking for — and it is worth acting on before running a second evaluation round, since improving coverage on the target repository will likely move the numbers more than switching tools will.
The bottom line on AI coding agents
Three categories, distinguished by autonomy and review burden rather than by model quality. Leaderboard position predicts very little about performance on your codebase, where context gathering and test coverage dominate. Agentic runs are expensive enough that tier routing is worth engineering.
Your next step: take ten already-closed tickets from your own tracker and run your candidate tools against them directly. Measure cost per merged change and actual review time, and ignore every published leaderboard score while you do it — your own repository's numbers are the only ones that will actually predict what happens next quarter.
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