Metrik
Real-Time LLM Latency Tracker and Fastest-Model Router
Metrik launched on November 26, 2025 with 6 votes, #2,995 of 9,796 launches that month.
What it does
Monitor Time to First Token (TTFT) across 26+ LLM models from OpenAI, Anthropic, Google, and xAI for free. Track real-time performance and route to the fastest model for your Vapi voice agents.
Monitor Time to First Token (TTFT) across 26+ LLM models from OpenAI, Anthropic, Google, and xAI. Track real-time performance and route to the fastest model for your Vapi voice agents.
Metrik monitors TTFT across all major LLMs in real-time and automatically routes your Vapi voice agents to the fastest model—ensuring the lowest latency and best user experience, 24/7. We continuously track TTFT (Time to First Token) across all major LLMs—GPT-4, Claude, Gemini, and more—measuring latency every minute. Our algorithm analyzes real-time data and automatically selects the fastest performing model based on time of day, load, and your preferences. Your Vapi voice agents automatically use the optimal LLM with zero additional latency. No manual switching required—just better performance. Join the waitlist and be among the first to experience intelligent LLM routing forfrom metrik-dashboard.vercel.app
Does a similar job
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I built a sub-500ms latency voice agent from scratchMar 2026 · ntik.me · ▲570I built a voice agent from scratch that averages ~400ms end-to-end latency (phone stop → first syllable). That’s with full STT → LLM → TTS in the loop, clean barge-ins, and no precomputed responses. What moved the needle: Voice is a turn-taking problem, not a transcription problem. VAD alone fails; you need semantic end-of-turn detection. The system reduces to one loop: speaking vs listening. The two transitions - cancel instantly on barge-in, respond instantly on end-of-turn - define the experience. STT → LLM → TTS must stream. Sequential pipelines are dead on arrival for natural…
Moonshine Open-Weights STT models – higher accuracy than WhisperLargev3Feb 2026 · github.com · ▲316I wanted to share our new speech to text model, and the library to use them effectively. We're a small startup (six people, sub-$100k monthly GPU budget) so I'm proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI's largest Whisper model. Admittedly Large v3 is a couple of years old, but we're near the top the HF OpenASR leaderboard, even up against Nvidia's Parakeet family. Anyway, I'd love to get feedback on the models and software, and hear about what people might build with it.
Llmspeed – feel how fast a local LLM runs before you buy the hardware6d ago · llmspeed.dev · ▲329Pick hardware and a model, and feel the real speed of a local LLM before you buy. Simulates tokens/sec and time-to-first-token from verified benchmarks for GPUs, Macs, and more.
FastRouter.ai22d ago · fastrouter.ai · ▲364Route requests to the right LLM for cost, latency & quality
- ATA tool to benchmark LLM APIs (OpenAI, Claude, local/self-hosted)2025 · llmapitest.com · ▲55
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…

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