SpeakoFlow vs Zero: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of SpeakoFlow and Zero — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

SpeakoFlow

Free

Free, open-source voice-to-text app for dictation, AI assistant, and voice writing across any app on Windows, macOS, and Linux.

Cross-App Dictation: Press a hotkey (Left Ctrl + Left Super by default) and speak — your words type straight into any app: email, editor, or chat, live as you talk.
Hey Flow Assistant: Say 'Hey Flow' to reply to a message, write an email, or draft a prompt without touching the keyboard; trigger name is renamable.
Live Translation: Speak Spanish, Hindi, French, Japanese, or any language and get clean English typed at your cursor, running fully on-device.
AI Cleanup: Strips 'um's and false starts, fixes grammar, and matches a chosen tone (Professional, Friendly, Concise, or custom) so speech reads polished.
Screen-Aware Answers: One hotkey summons a floating assistant that can look at your screen and answer about the error, chart, or email in front of you.
Private Memory & Profiles: Separate profiles per part of your day; remembers how you like to work locally without sending data off-device.
Cross-Platform Downloads: Ships as a Windows installer, macOS build (one Terminal command on first install), and .deb for Ubuntu and Debian.

Faster Writing: Developers, writers, and support agents dictate at 150+ WPM instead of typing at 45 WPM.
Multilingual Reply: Speak in your native language and reply in polished English without switching keyboards or translators.
Private On-Device Voice Work: Users who don't want their voice sent to a cloud get local dictation and translation.
Voice-Driven Prompts: Draft LLM prompts, emails, or Slack replies by voice inside any editor or chat app.
Screen Q&A: Ask the assistant about the specific error or chart on screen without pasting it in manually.

Vercel Labs

Free

An experimental graph-first programming language where agents edit a compiler-checked program graph instead of raw source text.

Graph as the Program: A compiler-owned semantic graph of symbols, calls, types, effects and node IDs is the source of truth, so agents reason over program structure rather than parsing and regenerating text.
Hash-Guarded Patches: Every edit carries an expected graph hash and expected field values, so a stale or conflicting patch is rejected before it reaches the store instead of silently corrupting the program.
Compiler in the Loop: Shape, type, stale-state and repository metadata checks run as part of applying a patch, collapsing the write-build-test-inspect cycle into a single checked operation.
Readable Text Projections: The graph renders to reviewable .0 source projections so humans can read diffs, audit what an agent changed and make rare manual edits.
Structured JSON Diagnostics: The compiler emits machine-readable diagnostics rather than prose error text, so agents can act on failures without parsing terminal output.
Explicit Effects via World: Side effects are passed through an explicit World capability parameter, making what a function can touch visible in its signature.
Runtime Constraints by Design: Targets token efficiency, low memory, fast startup, fast builds, low latency and zero dependencies rather than relaxing systems goals for agent ergonomics.
Query and Patch CLI: zero init, zero query, zero patch and zero run give agents a direct command surface over the graph, with agent skills carrying the graph discipline instead of rigid human prompts.

Reliable Agent Code Edits: Let a coding agent make semantic changes that are rejected outright if its view of the program is stale, instead of producing plausible-looking but broken text diffs.
Reducing Agent Token Spend: Query the specific symbols, types and nodes relevant to a task rather than feeding whole files into context on every turn.
Outcome-Driven Development: Describe a desired result in conversation — add auth, fix a failing route, build a CRM API — and review the resulting projection rather than writing the code.
Auditable AI-Written Code: Review what changed through readable .0 projections and graph hashes, keeping a human checkpoint over agent-authored programs.
Language and Tooling Research: Explore what a compiler and program representation look like when machine editors, not human typists, are the primary writers.
Sandboxed Experimentation: Prototype agent-driven codebases in an isolated environment where breaking changes and pre-1.0 churn are acceptable.