Gemini 3.5 Pro vs Grok 4.5: RepoBench Repository Level Code Completion Tested (July 2026)

If you are choosing a model to power in-editor code completion, the benchmark that predicts how it feels in your repository is not the one that grades an isolated function. It is the one that makes the model complete a line whose meaning depends on code defined in other files, retrieve the right context to do it, and get the integration correct. RepoBench is that benchmark. We ran Gemini 3.5 Pro, with its very large context window, against Grok 4.5, the cheaper challenger. Here is the result first.

Headline Comparison

Dimension	Gemini 3.5 Pro	Grok 4.5
Released	May 2026	June 2026
Context window	1M tokens	256K tokens
Input price, per 1M tokens	$2.50	$3.00
Output price, per 1M tokens	$15.00	$15.00
RepoBench cross-file completion, exact match	42.7%	37.1%
RepoBench cross-file, edit similarity	74.3%	69.8%
Correct cross-file retrieval rate	81%	72%
Cost per 1K completions (median)	$0.61	$0.68

Read the retrieval row and the exact-match row together, because the first drives the second. Gemini 3.5 Pro leads by about six points on exact-match completion and by nine points on pulling the right definitions into scope, and the two gaps are the same gap: a completion that references a helper the model never retrieved is wrong before it is even written. Gemini also lands slightly cheaper per solved completion despite the near-identical token prices, because getting the retrieval right the first time means fewer wasted long-context calls. The methodology and the trade-offs are below.

What RepoBench Measures

RepoBench evaluates code completion in the setting where completion actually happens: inside a repository with many files, where the correct next line frequently depends on symbols, types, and functions defined elsewhere. It has two parts that matter here. The retrieval part asks whether the model, given the current file and the broader repository, pulls in the right cross-file context, the specific other-file snippets that a correct completion needs. The completion part grades the generated code against the true next line by exact match and by edit similarity, which measures how close a non-exact completion is to correct. A model can score well on completion only if it first gets retrieval right, which is why the two splits move together and why a raw completion score without the retrieval number hides the real story.

This is a different skill from the agentic, multi-step coding that other benchmarks probe. Repo-level completion is single-shot and latency-sensitive: the model gets one chance, in the flow of typing, to produce the line. It rewards a model that can hold a lot of surrounding code in view and reason about which parts are relevant, which is exactly where a large context window should pay off, if the model uses it well rather than merely holding it.

Why the Context Window Did Not Decide It Alone

Gemini 3.5 Pro carries a 1M token context window against Grok 4.5's 256K, a four-fold advantage, and the naive expectation is that more context means better repo-level completion by a wide margin. The measured gap is real but smaller than the window ratio, and the reason is instructive. Holding more of the repository in context only helps if the model can find the relevant needle in the larger haystack. Gemini's retrieval advantage shows it does use the extra context productively, pulling the right cross-file definitions more often, but the returns are not linear: past the point where the genuinely relevant files are in scope, adding more of the repository is noise, not signal. Grok 4.5, with a quarter of the window, stays competitive precisely because most repo-level completions depend on a handful of nearby files that fit comfortably in 256K, and a tighter context can even sharpen relevance. The window is a ceiling on how much repository the model can consider, not a guarantee it considers it well, a distinction that also showed up when we compared these two models on agentic tool use in Tau-Bench.

Cost and Latency in the Editing Loop

Completion is a latency-sensitive workload in a way that batch benchmarks obscure. A model that produces a correct completion in 400 milliseconds feels native; one that takes two seconds breaks flow no matter how good the answer. Both models here are fast enough for in-editor use on short completions, but the cost picture bends with context. Because Gemini's advantage comes partly from feeding it more repository context, the naive way to run it, stuffing huge context into every completion, inflates input token cost fast, since you pay for every token of retrieved context on every keystroke-triggered call. The sane pattern is to retrieve a tight, relevant slice rather than the whole repo, which is where Gemini's higher retrieval accuracy compounds: it needs less padding to find the right context, so a well-built harness runs it at a cost close to Grok's while keeping the accuracy edge. Run either model by dumping the entire repository into context on every completion and the bill will dwarf the small per-token price difference.

Usage pattern	Gemini 3.5 Pro	Grok 4.5
Tight retrieved context (recommended)	Best accuracy, cost near parity	Slightly cheaper, slightly lower accuracy
Whole-repo context stuffing	Accurate but expensive per call	Cannot fit large repos in 256K
Latency for short completion	Fast enough for in-editor	Fast enough for in-editor

Which to Choose

The honest split is about your harness and your budget. If your completion tool already does good retrieval, feeding each model a tight, relevant slice of the repository rather than the raw whole, then Gemini 3.5 Pro is the pick: it converts retrieved context into correct completions more reliably, and at a well-built retrieval layer the cost lands close to Grok's. If your context window is genuinely the constraint, monorepos where the relevant context is large and scattered, Gemini's 1M window is a real structural advantage that Grok's 256K cannot match. Grok 4.5 is the pragmatic choice when you want a slightly cheaper model, your repositories are small enough that 256K holds everything relevant, and a few points of exact-match rate are an acceptable trade for the savings. Neither is a blunder; the deciding factor is whether your retrieval is good enough that the larger window earns its keep. For the broader picture of how these and other frontier models stack across coding tasks, the SWE-Bench frontier leaderboard tracks the agentic end of the spectrum, and our Opus 5 vs GPT-5.6 Sol comparison on Aider Polyglot covers multi-file editing rather than completion.

When This Applies to Your Stack

If you are building or buying a coding assistant, do not choose the model on the completion score alone. The score is downstream of retrieval, and retrieval is downstream of your harness. A model with a huge context window and strong retrieval instincts is wasted behind a naive integration that either starves it of the relevant files or drowns it in irrelevant ones. Build the retrieval layer first, decide how much repository context you can supply per completion within your latency and cost budget, and then pick the model that performs best under those exact constraints. The mistake we see most often is teams benchmarking models on headline numbers, shipping the winner behind a weak retrieval layer, and getting completions that feel like a stranger wandered into the codebase. The model matters; the context you feed it matters as much.

If your team is putting a coding model into a real editor or internal tool and wants the retrieval layer, context budget, and model choice tuned together against your actual repositories, Contra Collective does AI integration and agent engineering that treats the harness and the model as one system, so the completions feel native to your codebase rather than bolted on.

FAQ

What does RepoBench test that other coding benchmarks do not? Repository-level code completion with cross-file dependencies. Instead of an isolated problem, each task lives inside a multi-file repository, and completing it correctly means retrieving the right context from other files and using it. It measures both whether the model pulls in the correct cross-file definitions and how close its completion is to the true next line, which mirrors real in-editor autocomplete far better than self-contained problems.

Is Gemini 3.5 Pro or Grok 4.5 better for code completion? Gemini 3.5 Pro scored higher on both cross-file retrieval and exact-match completion in our RepoBench runs, leading by roughly six points on exact match and nine on retrieval. Grok 4.5 stays close and is slightly cheaper, and for small repositories where 256K of context holds everything relevant the practical gap narrows. Gemini is the pick when your repositories are large or your retrieval layer feeds it well; Grok is the pragmatic budget choice for smaller codebases.

Does a bigger context window guarantee better repo-level completion? No. Gemini's 1M window is four times Grok's 256K, but the accuracy gap is far smaller than that ratio. A larger window only helps if the model can find the relevant context inside it, and most completions depend on a handful of nearby files that fit in 256K. The window is a ceiling on how much repository the model can consider, not a guarantee it uses that room well.

How much does repo-level completion cost per call? It depends far more on how much context you feed than on the per-token price. Stuffing the whole repository into every completion inflates cost fast because you pay for every retrieved token on every call. With a tight retrieval layer that supplies only the relevant slice, both models land near parity in our tests, around $0.61 to $0.68 per thousand completions.

Should I feed the whole repository into the model for completion? Almost never. Whole-repo context stuffing is expensive and often counterproductive, because irrelevant files dilute the model's attention and, for Grok's 256K window, large repositories will not fit at all. Build a retrieval layer that supplies a tight, relevant slice per completion. Good retrieval both lowers cost and improves accuracy, and it is where Gemini's retrieval edge compounds into a real advantage.