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OpenCues vs Raycast AI: what is the difference?

Raycast AI puts a chat model behind the Raycast launcher: hit the hotkey, a window appears, you ask, and the answer lands in that window. OpenCues removes the window. You type _ inside the text you are already writing and the answer lands there, because the request and its destination are the same place.

The window is the difference

Both tools are keyboard-first and fast to summon. The difference is what happens after the answer arrives. In a launcher, an ad-hoc answer appears in the launcher's window, and if you wanted it in an email, a prompt, or a document, you copy it, dismiss the window, refocus your field, and paste (Raycast's AI commands that rewrite a selection do land in place). In OpenCues the destination was declared by where you put the _, so there is no transfer step at all: 5 miles in km _ becomes 8 km in the sentence you were writing.

In the 3-axis terms this site uses: a summoned window has real end-lag (dismiss, refocus, paste, adapt), while an inline fill has close to none. For a one-off question, the difference is small. For the tenth lookup while drafting, it compounds.

Where Raycast is genuinely stronger

System-wide coverage. Raycast works over every application on the Mac (and now Windows and iOS), including native apps OpenCues has no host for. OpenCues works inside its supported hosts.
Beyond text. Raycast is a full launcher: app switching, window management, extensions, clipboard history. OpenCues does one thing (intelligence inside text fields) and nothing else.
Conversation. Raycast AI can hold a multi-turn chat. OpenCues is deliberately single-shot per _; it is not a chat surface.

Where OpenCues goes further

Context comes free. Inline, the surrounding text is the prompt: make this more formal _ needs no re-explanation of what "this" is.
The passive layer. Cues dim words with alternatives as you compose, and grammar correction runs continuously; a summoned window has no equivalent, since it only exists while summoned.
Your model, your key. OpenCues is open source and model agnostic (Cerebras, Groq, OpenAI, Anthropic, Gemini, OpenRouter, local Ollama); Raycast's hosted models sit behind its Pro subscription, though it also accepts your own OpenAI, Anthropic, or Google key and local Ollama models.

The same question, both ways

Concretely: mid-email, you need a conversion. The launcher route is hotkey, type "5 miles in km", read, copy, dismiss, refocus, paste. The OpenCues route is typing 5 miles in km _ where the number belongs and continuing to type while it resolves; if it can't resolve, the _ just stays visible and harmless. The launcher route is better when the answer isn't destined for text at all (you just wanted to know); the inline route wins whenever the destination is the field you're in. That's also why the two coexist happily: a launcher for launching and asking, OpenCues for the text you are already inside.

On the newer axes: both are extensible (Raycast through its extension store, OpenCues through cues and blanks you write as markdown and shell scripts under an open standard), both route models (Raycast per command, OpenCues per feature), and OpenCues adds per-word revertable edits plus identity tokenisation that keeps PII off cloud providers.

Where Raycast AI struggles

The window is the tax: every answer arrives in Raycast's panel and pays a transfer step back to where you were working, and selection commands write back as pastes. Even bring-your-own-key requests route through Raycast's servers, restricted to its model roster, so the key is yours but the pipe is theirs.

By the numbers

Quick AI measures four steps to an answer with the paste-back as end-lag, and five for a command on a selection, against one step and zero end-lag inline. The launcher window is the one new surface, and nothing surfaces unprompted. Counts and sequences in the full comparison.