GBrain vs Speko: Features, Pricing & Which Is Better (2026)

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

GBrain

Paid

A shared team brain that syncs memory, connected apps and scheduled skills to ChatGPT, Claude, Cursor and any AI your team uses.

Shared Team Chat: The whole team prompts in one shared chat where anyone can follow up and see how answers were worked out.
Plain-File Memory: Team knowledge lives as markdown notes in folders you can open, correct and copy, and every AI answers from the fix.
Connect Once Credentials: Connect Gmail, Calendar, Drive, Stripe and more once; AIs receive call results, never passwords.
Per-AI Permission Levels: Set each AI to Off, Read, Draft, Manage or Full per account, with every action on one record.
Scheduled Skills: One-click skills like Team Standup, Customer Comms Tracker and Inbox Cleanup run daily or once.
Multi-AI Sync: Sync the same memory to Claude, ChatGPT, Cursor, Claude Code, Codex and your terminal.
Tools Plan: A password manager for AI that puts your applications into every AI you use for individuals.

Team Standups: Summarize the team's work across GitHub, Gmail and Calendar and email it out each morning.
Customer Follow-Up: Track customers and prospects owed a reply and flag overdue ones daily.
Issue Triage: Turn customer bugs and requests from email and support chats into deduplicated tracker issues.
Meeting Prep: Build sourced briefs on candidates, guests or investors before you meet.
Inbox Cleanup: Archive mail you never read on a schedule.
Pricing Decisions: Combine call notes and billing data in a shared chat to evaluate a price change.

Freemium

A router for voice AI that benchmarks 56 speech and language models language by language, then routes each session to the one that wins.

Language-by-Language Benchmarks: 56 speech and language models measured across ten languages, exposing that a model topping the English table may have no published rank anywhere else.
Cost-Versus-Accuracy Charts: Word error rate plotted against dollars per minute for each pipeline stage — STT, LLM, TTS and speech-to-speech — so a model choice is a priced trade-off, not a guess.
Managed Router: A hosted, provider-neutral data plane at router.speko.dev that selects the winning model per session using the measurements and fails over before a response is returned.
Typed Contracts: Public OpenAPI and AsyncAPI specifications for the Router, so integrations are generated rather than hand-rolled against an undocumented endpoint.
Open Gateway Runtime: A customer-side runtime with native LiveKit and Pipecat services offering provider-direct streaming and local BYOK credentials, keeping your vendor relationships intact.
Drop-In Framework Integration: Swap STT, LLM and TTS in a LiveKit AgentSession for Speko equivalents with credential_source="auto" and model="auto" and get routing without restructuring the agent.
MCP Server: Point Claude Code or Cursor at mcp.speko.ai to query models, voices and benchmark data from inside a coding agent.
Two Pricing Paths: Either add 5% on top of a provider's published rate while routing your own spend, or take the bundled Speko infrastructure rate of $0.09 per minute across all three legs.

Multilingual Voice Products: Ship a voice agent to non-English markets and route each language to the model that actually performs best there.
Vendor Selection Research: Use the published benchmarks to decide which STT or TTS provider to sign with before committing to a contract.
Voice Cost Optimization: Trade a small accuracy delta for a large cost reduction by picking a model at the right point on the WER-versus-price curve.
Failover and Reliability: Keep voice sessions alive through a provider outage with pre-response failover across the routed model pool.
LiveKit or Pipecat Migration: Add measured routing to an existing agent stack by swapping in Gateway services rather than rewriting the pipeline.
Agent-Assisted Model Research: Query the benchmark data from Claude or Cursor over MCP while writing the voice integration.