A foundation model for mobiles, wearables, robots, smart home, automotive and microcontrollers. The whole model is a single 8-29 MB file, and we trade general chat capacity to beat models 10x its size on mobile tool calls and match 2-3x bigger models on extraction. - **Tool calls**: given the functions your app exposes, Needle picks the right ones and fills every argument from what the user said. Ask for two things and you get two calls in order; ask for something no tool covers and you get an empty list, not a guess. - **Structured extraction**: declare a shape, hand over messy text, get typed fields back: an invoice, a booking, a notification, a form. The decode grammar guarantees the output parses, and extraction generalises to classification. - **Text embedding**: the same model returns a vector for a sentence, so an app can search, match and route locally. The same engine also runs Whistle, our 16.9 MB speech-to-text model in seven languages. Load `whistle.cact` beside `needle3.cact` and the one binary transcribes a clip on its own, or answers it against your tools in a single call and returns the calls and the transcript in one JSON object.

Text generation

Input Text→ Output Text

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whistle

Cactus-Compute

A speech-to-text model for mobiles, wearables, robots, smart home, automotive and microcontrollers. The whole model is a single 16.9 MB file, and it runs on the same CPU engine as Needle, from the same container and the same quantisation, with no dependencies and no GPU. - **Transcription**: 16 kHz mono audio of any length, one pass up to 30 seconds and windowed beyond it, in English, German, French, Spanish, Italian, Dutch and Polish. The language is detected unless you name it, and silence and steady noise return an empty transcript rather than an invented sentence; other sounds, music especially, can still come out as text, so put a voice activity detector or a wake word in front of an always-on microphone. Live audio streams for as long as you like, committing the words two passes agree on. - **Word timestamps**: every word with its start, end and probability, aligned from the decoder's own attention, so an app can highlight, seek or cut on a word. - **Speech embedding**: the encoder output, one row per 80 ms frame, for matching and retrieval without decoding a transcript at all. Keyword biasing takes the names, places, product words and jargon your users actually say and favours them during the search, so the ones that matter survive.

Speech recognition

Input Audio→ Output Text

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Monthly input tokens 10 MMonthly output tokens 2 M1 M = 1 million tokens · USD
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