# One model or a panel of reviewers?

URL: https://codepanel.ai/guides/single-model-vs-panel

When a panel of AI reviewers from different model families beats one stronger model, what the orchestrator adds, and what each setup costs.

A panel of small reviewers from different model families, followed by one step that checks their findings against the code, usually beats a single model for the same money. The reason is that models make different mistakes. Two reviewers from the same family tend to miss the same bugs; two from different families miss different ones. A single strong model is still the right call for very small changes, for latency-critical checks, and when you have no filtering step, because a panel without a filter is just more noise.

## Why one model misses things

A model reviewing a diff reads it once, from one angle, with one set of blind spots. Run the same model twice on the same pull request and the findings overlap heavily but not completely: some bugs it catches one time and misses the next. That run-to-run variation is real, and anyone evaluating reviewers has to account for it.

Different model families differ more than repeat runs of one model. They were trained on different data, tuned with different feedback and fail on different patterns. One family may be strong on concurrency and weak on SQL; another the reverse. A reviewer is a sample of what a model notices. More samples from more distributions cover more of the space.

## What a panel looks like

| Part | Job | Typical model |
| --- | --- | --- |
| Reviewers (3 to 5) | Read the whole diff in parallel and report everything they notice, with a confidence level | Small models, ideally from more than one provider (CodePanel's starter panel uses one) |
| Filter (orchestrator) | Read every finding and the code it names, merge duplicates, fix line numbers, drop what the code contradicts, settle disagreements | One mid-size model |
| Output | One review, each finding once, most severe first | |

The reviewers can also differ by role. One looks for bugs and security issues, one for test gaps and maintainability, one checks your written rules. Two reviewers on the same model help only if they look for different things. Two models with the same role give you a second opinion. Both kinds of diversity add coverage.

## The filter is not optional

The union of five reviewers' findings has more true positives than any one of them. It also has more false positives, and more duplicates. Posted raw, it is worse to read than a single review.

The filter fixes that, but only if it reads the code. A filter that only sees the reviewers' text judges findings by how convincing they sound. A filter that sees the diff and the surrounding lines can open the line a finding names and check: is the null case already handled three lines up? Is the "removed" check still there in the new revision? Does the function the reviewer describes exist?

CodePanel's orchestrator gets the numbered diff and the surrounding code for every file a finding names. Its instructions tell it that removing a bad finding is as valuable as keeping a good one, and that it should treat claims about library behaviour with suspicion when the code does not settle them.

Agreement is a signal the filter can use. When three reviewers from three families flag the same line independently, that finding is very likely real. When one reviewer flags something the others did not, it may still be right, and the filter has to judge it on the code rather than on the vote. Vote counting alone would drop exactly the bugs that only one family notices, which are the reason to have a panel at all.

## Disagreement

Reviewers contradict each other more often than you would expect. One says a lock is missing; another says the operation is idempotent so no lock is needed. Reporting both leaves the coding agent to referee, and it will usually pick whichever is easier to act on.

A good filter picks a side and says why. CodePanel's orchestrator is told to resolve disagreement explicitly, weigh each reviewer's stated confidence, and judge the argument on the code, because a confident reviewer can still be wrong. The author gets one finding with one recommendation.

## The cost math

Take a medium pull request: about 30,000 input tokens per reviewer and 3,000 output. At list price on 2026-10-16:

| Setup | Calls | Cost per round |
| --- | --- | --- |
| One Claude Opus 5.5 | 1 frontier | $0.18 |
| One Claude Sonnet 5.5 | 1 mid-size | $0.09 |
| Four Haiku 5.5 reviewers + Sonnet 5.5 filter | 4 small + 1 mid-size | $0.088 |
| Two Haiku 5.5, one GPT-6 Luna, one Gemini 3.5 Flash-Lite + Sonnet 5.5 filter | 4 small across 3 families + 1 mid-size | $0.10 |

The filter's row assumes 15,000 input tokens and 4,000 output. Its input is the reviewers' findings plus the code they point at, so it grows with how much was reported, not with the size of the pull request. A quiet review costs less; a noisy one more.

At these prices, the panel costs about the same as one mid-size call and about half of one frontier call. Most of its cost is the filter, not the reviewers.

## Failure isolation

A panel also degrades better. If one provider is down, out of credit or slow, a single-model reviewer posts nothing. A panel loses one reviewer and posts the rest. If the filter itself fails, the findings can still be merged mechanically: cluster duplicates, count agreement and post them marked as unfiltered. That is worse than a filtered review and far better than none.

Reviewers in parallel also finish when the slowest does, so adding one more small reviewer costs almost no time.

## When a single model is the better choice

- **Tiny changes.** A five-line config change does not need five opinions. The filter call alone may cost more than a single small review.
- **Latency budgets measured in seconds.** A panel takes two steps, reviewers then filter. If you need an answer inside an editor as you type, one fast model is the right tool.
- **No filter.** If your tool posts every reviewer's output as-is, stick with one model. You will get fewer findings and fewer false alarms.
- **One dominant risk.** A repository where every change touches cryptography may be better served by one frontier model at high effort than by several small generalists. Even then, consider that model as one seat on a panel rather than a replacement for it.

## How to decide for your repository

Measure both on the same changes. Take 20 to 30 merged pull requests where a bug was later fixed, plus a handful that were correct, and run each setup on all of them at least three times. Compare recall and false alarms per correct change on the same fixtures, paired, not as two separate averages. The guide on [evaluating an AI code reviewer](/guides/evaluating-ai-code-reviewers) covers the method, and the [benchmark methodology](/benchmarks/methodology) shows the same approach applied to single models.

CodePanel's [starter panel](/docs/reviewers) is a panel of this shape: four small reviewers by role, all on one provider's small model (the first of Anthropic, OpenAI and Google you hold a key for), and an orchestrator on a mid-size model. The [How CodePanel works](/docs/how-it-works) page describes each step and what happens when one fails.