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Mercury Coder Next Edit

Sub-100ms next-edit prediction for VS Code and JetBrains — powered by diffusion LLMs

PriceModels Add-On subscription required for Continue. API: $0.25/M input tokens, $1/M output tokens. Free tier available.Reviewed2026-04-04

Expert verdict

Ship

2-1
2 Ships1 Skips
Visit www.inceptionlabs.ai

The Panel's Take

Inception Labs launched Next Edit inside the Continue extension, bringing Mercury Coder's diffusion-based architecture to VS Code and JetBrains. Unlike autoregressive autocomplete that generates left-to-right, Mercury predicts multi-line edits across your entire file simultaneously — deletions, additions, and structural changes at once. Common patterns it handles: converting callbacks to async/await, extracting functions, renaming variables across call sites, and squashing code smells. Latency is under 100ms so suggestions appear before you finish thinking. The diffusion architecture ($0.25/M input, $1/M output) is 5-10x faster than comparable autoregressive models. Available via Models Add-On in Continue.

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Mercury Coder Next Edit verdict: SHIP 🚀

2 ships · 1 skip from the expert panel

Full review: shiporskip.io/tool/mercury-coder-next-edit-inception-labs-diffusion

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Ship · 6.7/10
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The reviews

I've used next-edit features in other tools but the sub-100ms latency here is genuinely different — it's below my perception threshold, which means it doesn't break flow. The multi-line simultaneous edit understanding is real; it caught a refactor pattern I was about to manually do across 6 call sites.

Helpful?

The benchmarks are impressive but 'trained on real edit sequences' is doing a lot of work here. Until I see how it handles domain-specific refactors in large codebases with complex type hierarchies, I'm skeptical it beats Cursor's native next-edit on anything beyond textbook patterns.

Helpful?

Diffusion LLMs applied to code editing is the most underrated architectural bet in AI tooling right now. Autoregressive generation was always the wrong primitive for editing — you don't write a diff token by token. Mercury's approach is structurally correct and the speed numbers suggest it scales without compromise.

Helpful?

Even for non-heavy-coders, the 'fix code smells' and 'rename across call sites' use cases are exactly the tedious tasks that make coding feel like work instead of creation. Sub-100ms means zero cognitive interrupt. This is the kind of AI assist that disappears into the background in a good way.

Helpful?

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