If you searched "AI code review" six months ago, the landscape looked roughly like CodeRabbit, a handful of GitHub-bot startups, and your IDE's built-in assistant. Today it's a much wider field — Qodo, Greptile, Bito, Coderabbit, Codium, Sourcegraph's Cody, plus every IDE shipping its own "review this change" button — and the answer to "which one should I use?" depends on questions nobody seems to be asking out loud.
I run 2ndOpinion, a multi-model AI code review CLI. So yes, I'm biased. I'm going to try to be honest about it anyway, because what I actually want is for you to pick the right category of tool for how you work — and then, within that category, pick the one that matches your tradeoffs. If that's not us, that's fine.
Here's how I think about the landscape after building in it for the better part of a year.
The three categories that actually exist
The category labels matter more than the brand names. Almost every tool falls into one of three buckets:
- Async PR reviewers. Bot-on-GitHub, bot-on-GitLab. Reviews show up as comments after you push. CodeRabbit, Qodo Merge, Bito, Greptile are the loudest names here.
- In-editor copilots. "Review this change" inside Cursor, VS Code Copilot, Cody, JetBrains AI. Synchronous, in-flow, ephemeral.
- CLI / CI reviewers. Run locally on a diff or in a pipeline step. Output is structured, scriptable. This is where 2ndOpinion lives, alongside tools like Aider's review modes and a growing pile of homegrown CI wrappers.
These aren't competing products as much as competing times in the day when AI reviews your code. Some teams use all three. Most should use at least two.
What each category is actually good at
Async PR reviewers are best when the reviewer is supposed to be a teammate-shaped entity — leaving inline comments, approving or requesting changes, surfacing in the same UI where humans review. The strength is integration with the social workflow of a PR. The weakness is timing: feedback arrives after you've context-switched. By the time the bot comments, you're already in your next branch.
In-editor copilots are best for shipping velocity. The review happens while the code is still warm. The weakness is the same model bias I keep writing about — the model that helped you write the code is the worst possible reviewer of that code. If your editor's copilot and your editor's reviewer are the same model, you're getting a confidence boost, not a review.
CLI / CI reviewers are best for policy — making review a gate, not a suggestion. They run on every diff, with consistent thresholds, in an environment you control. The weakness is that they're harder to set up than installing a GitHub app, and the output is less pretty than inline comments.
If you only pick one, pick based on whether your bottleneck is catching bugs (CI), velocity (editor), or team review hygiene (PR bot).
The single-model vs multi-model split
Cutting across all three categories is a more interesting axis: how many models is the tool actually consulting?
Most of the well-known tools today are single-model. CodeRabbit publishes its model choices, Qodo lets you swap, Cursor uses whichever model you've selected in the sidebar. The review you get is one model's opinion.
A smaller group runs more than one model. 2ndOpinion runs Claude, Codex, and Gemini and surfaces both the individual reviews and a synthesized consensus verdict. A handful of newer tools are starting to do similar things.
I've written about why this matters in detail before, but the short version: each model has systematic blind spots that don't show up until you compare its review to another model's. Single-model review feels comprehensive because the model is confident. Multi-model review feels noisier because it actually surfaces the disagreement that was there all along.
If your tolerance for false negatives is low — security-sensitive code, infra, anything touching money — multi-model is worth the extra cost. If your tolerance is high — internal tools, prototypes, anything you'll rewrite in a month — single-model is probably fine.
What I'd actually recommend, by team shape
Solo developer, fast iteration. In-editor review only. Cursor or Copilot's review feature, plus whatever you're already using to write the code. Don't add a CI gate that blocks your own merges — you'll bypass it within a week.
Small team (2–5 engineers), shipping to production. PR bot for the team-review surface, plus a CLI/CI step for the actual gate. The PR bot gives you the social workflow. The CLI gives you the consistent policy.
Mid-size team, security-sensitive code. All three layers, with multi-model at the CI gate. The CI step is where you can afford the latency and cost of running multiple models — every PR runs through it once, and the cost is bounded.
Large org, monorepo. This is the case where I'd most strongly recommend a CLI/CI tool over a PR bot. PR bots tend to scale badly on monorepos — they choke on large diffs, or they review files the change didn't actually touch, or they cost a fortune because every PR pulls in the whole context. CLI tools let you scope the review precisely.
Where 2ndOpinion fits (and where it doesn't)
The honest pitch: if you want multi-model consensus, in a CLI or MCP server form factor, with first-class CI integration, that's what we do. We don't have a GitHub PR bot. We're not in your editor as a sidebar. We're a CLI and an MCP server.
If you want a pretty PR comment with inline annotations, you probably want CodeRabbit or Qodo Merge. If you want a sidebar reviewer inside Cursor, Cursor's own review is the right answer.
What we're good at: running every diff through Claude, Codex, and Gemini in parallel, getting back three independent reviews plus a synthesized verdict, and either running it locally as a CLI or wiring it in as an MCP tool inside Claude Code, Cursor, or any MCP-compatible editor. Setup is one npm install -g 2ndopinion-cli and three API keys.
How to actually decide
A working heuristic:
- If your last production bug was the kind of thing a careful reviewer would have caught and AI didn't, you need either a different model or more models. Try multi-model.
- If your last production bug was the kind of thing nobody would have caught, you don't need more models — you need better tests, observability, or rollback infrastructure. AI review won't save you.
- If your bottleneck is "PRs sitting unreviewed for two days," any of the async PR bots will help. The specific brand matters less than picking one and getting your team to actually trust it.
- If your bottleneck is "we ship a lot but we ship buggy code," that's a CI gate problem. Single-model is a start; multi-model is the upgrade.
The thing nobody in the AI-tooling space wants to say out loud is that the tool isn't the constraint. The constraint is whether your team treats the review output as signal or noise. Pick the tool that produces a kind of output your team will actually act on — and then enforce that they act on it.
If you want to try multi-model consensus review on your next diff, the CLI is one command: npm install -g 2ndopinion-cli. Setup walkthrough and the MCP server config at get2ndopinion.dev.