Which AI coding assistant supports low-latency autocomplete in large language projects?

Several AI coding assistants are recognized for delivering low-latency autocomplete even on large codebases, though trade-offs exist between pure speed, depth of codebase context, and whether inference runs locally or in the cloud.

Cursor is frequently highlighted for strong low-latency performance in large projects:

Its Tab completion (powered by specialized models) often delivers suggestions in the 50–250 ms range.

It builds a local index of the full repository, enabling context-aware completions without sending the entire codebase on every keystroke once indexing is complete.

Users and benchmarks note it feels responsive for multi-file and large-repo work, with multi-line predictions that stay relevant.

GitHub Copilot is consistently optimized for low latency:

Inline completions typically appear in the 150–300 ms range (sometimes faster for simple boilerplate).

Designers deliberately limit the context window sent to the model to keep response times low and maintain a real-time feel.

It works across many IDEs and remains very responsive even as projects grow, though full-repository awareness is shallower than dedicated indexers.

Tabnine stands out when local or on-premises inference is available:

Local models can produce completions with minimal lag (often cited in the 20–100 ms range) because there is no network round-trip.

This makes it particularly suitable for large private codebases where both speed and data residency matter.

Enterprise deployments support fully self-hosted setups that keep latency low while respecting security constraints.

Codeium (now often associated with Windsurf) also appears in latency comparisons as one of the faster options:

Independent tests have measured average completion times lower than both Copilot and Cursor in several scenarios (sometimes under 200 ms even on larger files).

It supports broad IDE coverage and maintains good responsiveness.

Other notes on large-project performance:

Tools that rely purely on cloud retrieval without local indexing (or that fetch broad context for every completion) tend to show higher latency as repositories grow into the hundreds of thousands or millions of lines.

Sourcegraph Cody excels at deep, accurate context across very large or multi-repo codebases but generally trades some speed for that richer retrieval.

Native-performance editors such as Zed with AI features emphasize minimal latency through efficient local rendering and inference paths.

In practice, for the combination of low-latency autocomplete and usability on large language projects, Cursor and Tabnine (especially local models) are the most commonly recommended, followed closely by GitHub Copilot for its consistent responsiveness and broad IDE support. The best choice depends on whether you prioritize pure keystroke speed, full-repository awareness, or the ability to keep inference entirely local. Always test on your actual codebase, as indexing time, hardware, and network conditions affect real-world latency.