Contents
The direct answer
Every model is good at some things and bad at others. The bad-at-things don't line up across labs. That's the whole reason this works.
Run all four — OpenAI, Gemini, Grok, Claude — and a panel catches bugs a single model waves off. Joint Chiefs ships this way by default. Claude moderates. The other three argue.
No benchmark tables. Those go stale in a week and the order flips with every release. You want the mental model, not the leaderboard.
The five dimensions that matter
"Intelligence" is not one axis. In code review, what determines whether a model catches a real bug is:
- Context window size. Can the model hold the whole change plus related files in memory?
- Reasoning style. Does it walk through logic, or pattern-match to priors? Both have uses.
- Blind spots. What kinds of bugs is its training set light on?
- Calibration. When it's unsure, does that show up — or does it hedge on everything equally?
- Latency and cost. If the review takes two minutes, the feedback loop is broken. Quality doesn't matter.
Every model strikes a different balance. The point of a panel is to mix models whose strengths cover each other's gaps.
OpenAI (GPT-5.x)
OpenAI is fine at most things and boring at the rest. Good on mainstream languages — TypeScript, Python, Go, Swift. Reliable on idiomatic patterns and conventional API use. Tool orchestration is clean if your review needs to execute code or call a lint rule.
Blind spots: Pattern-matches too hard. It misses logic bugs in code that looks normal but isn't. Weaker on less common languages and on library versions that came out after its training. Calibration is middling — it flags style issues with the same confident tone it uses for actual bugs, which makes triage harder.
Where it fits: A dependable generalist spoke. Not great as a moderator — it averages across views instead of picking the strongest argument.
Anthropic Claude (Opus 4.x)
Claude talks too much. That's actually useful when you need someone to think out loud. It walks through logic instead of pattern-matching, which helps on novel codebases where "looks like X" doesn't apply. Big effective context, so multi-file reviews work. Security-adjacent reasoning — auth, data handling, privacy — is a real strength.
Blind spots: Verbose. Claude's reviews run 2–3x the length of GPT's for the same input. That adds up when you're reading a stack of findings. It also likes to present multiple framings instead of committing to one diagnosis on ambiguous code. Sometimes hedges on "could be a concern" findings that are just fine.
Where it fits: The best moderator in the panel. Holding several positions in mind and picking the strongest is exactly the judge's job. That's why Joint Chiefs puts Claude in the moderator slot by default. It's also a solid spoke when you want the narrative-reasoning view on a change.
Google Gemini (3.x Pro)
Gemini has the biggest context window. Doesn't always matter. It matters when the review spans many files or needs codebase-wide pattern matching. Strong on cross-file consistency — "this function does X here but Y there." Reasonable speed on big inputs.
Blind spots: Over-indexes on style consistency and misses real logic bugs. Output tends to be structured and listy in a way that skips the connective reasoning. Its self-confidence is mixed — you can't always trust the tone.
Where it fits: A strong spoke for any multi-file review. Weaker as moderator. Its synthesis feels mechanical, and the judge slot rewards narrative reasoning over pattern-matching breadth.
xAI Grok (3.x)
Grok is weird. It catches security bugs other models wave off — SQL injection patterns, auth logic errors, deserialization traps. I don't know why. The training is less documented than Anthropic's or OpenAI's. Use it anyway. The behavior is stable enough to count on.
Blind spots: Less reliable on stylistic and idiomatic stuff. Will sometimes produce a confident-sounding finding that's just wrong. Calibration is weaker than the other three. You can't take its confidence at face value without a cross-check.
Where it fits: A distinctive spoke. Grok gives you the security lens without paying for a dedicated security tool. Bad as a moderator — the directness that makes it a good spoke makes it a poor judge.
Combinations that work
A few panels that actually produce good review:
The default four
OpenAI + Gemini + Grok as spokes, Claude as moderator. Covers the generalist, cross-file, and security angles on the spoke side. Gets Claude's narrative synthesis on the decision side. This is what Joint Chiefs ships as the default, and it's strong.
The budget panel
One mid-tier model from each of two different labs plus a small moderator. If cost matters, what you're buying is lab diversity — not model size. Two medium models from different labs beat one large model talking to itself.
The security-heavy panel
Grok as primary spoke at weight 1.5, OpenAI and Claude at 1.0, Claude as moderator. Amplifies the security lens in voting-threshold mode without losing the sanity check from the other labs. Good fit for auth code, crypto handling, anything with a real attack surface.
The large-context panel
Gemini + Claude as paired spokes, optionally GPT as a tiebreaker. Useful when the review target is a big diff, a multi-file refactor, or a change that needs a lot of project state in working memory.
The through-line: at least two different labs, and put a narrative-reasoning model in the moderator slot.
Key takeaways
- No single model wins. Every one has blind spots the others don't.
- OpenAI is a dependable generalist spoke. Claude is the best moderator. Gemini is strongest on multi-file context. Grok catches security bugs other models miss.
- Lab diversity beats model size. Two mediums from different teams beat one big model talking to itself.
- Calibration varies a lot. Grok hedges least. Claude hedges most. Neither is right — the variance itself is signal.
- Pick the moderator for narrative synthesis, not pattern-matching breadth. That's why Joint Chiefs defaults to Claude.
Frequently asked questions
What is the best AI model for code review?
There isn't one. Every model is good at some things and bad at others. The bad-at-things don't line up across labs, which is exactly why a panel of several models catches bugs a single model waves off. Pick the model that fits the job, or run a few in parallel and let them argue.
Which LLM has the biggest context window for code review?
Gemini. Claude's Opus tier is next, then OpenAI's GPT-5 family. Grok is smaller. A big window only matters if the review actually needs it — most per-file reviews don't.
Does Claude "understand" code better than GPT?
Claude thinks out loud. It walks through logic step by step, which helps on unfamiliar code. GPT pattern-matches, which is faster on conventional code but misses subtle stuff. Neither is better overall. They miss different bugs.
Is Grok worth including?
Yes. Grok catches security-adjacent bugs other models wave off. I don't know why. Use it. Its style output is hit or miss, but in a panel what you want is a different set of blind spots — and Grok brings that.
One large model or several smaller ones?
Several smaller ones from different labs. The failure modes are independent, which is the whole point. Save the big model for the moderator slot, where deep reasoning matters more than cross-model variety.
How do I avoid paying four API bills for every review?
Set per-provider weights in Joint Chiefs. Weight 0.0 excludes a provider. Run the full panel on high-stakes changes and a two-model panel on routine ones. Most reviews don't need all four.