MentionBird vs Speko: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of MentionBird and Speko — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
MentionBird
MentionBird
AI search visibility platform that shows which sources ChatGPT, Gemini, Perplexity and Claude cite for your prompts and how to get your brand named.
Key features
- AI Prompt Tracking: Monitor the exact prompts buyers ask AI, with daily runs across supported AI platforms.
- AI Citation Tracking: See which pages, reviews and YouTube videos AI cites for each prompt and whether your brand appears in them.
- AI Ranking Advice: Get a per-prompt plan of recommended actions rated by effort and impact to earn mentions.
- Competitor Tracking: Compare share of AI recommendations against each competitor, week over week.
- Multi-Platform Coverage: Covers ChatGPT, Gemini, Perplexity and Claude, with Grok, AI Overviews, Copilot and DeepSeek on the Custom plan.
- MCP and API Access: Connect Claude or ChatGPT directly to your visibility data and ask questions about it.
- Free AI Tools: No-login tools such as an AI Crawler Accessibility Checker and an AI Visibility Report.
Best for
- Generative Engine Optimization: Finding which sources AI relies on for category prompts and earning placements in them.
- Competitive Benchmarking: Seeing which rivals AI recommends instead of your brand and for which prompts.
- Agency Client Reporting: Reporting client AI visibility and the next fixes to make.
- Content Planning: Using ranking advice to decide which guides and product-page details to publish.
- Launch Monitoring: Tracking whether a new product starts getting named in AI answers over time.
Speko
Speko
A router for voice AI that benchmarks 56 speech and language models language by language, then routes each session to the one that wins.
Key features
- 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.
Best for
- 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.
