VAmoS Bench

veris.ai · 2026-07-31 · by Long Nguyen ·generic
VAmoS Bench: Voice Agent Simulation Bench. End-to-end task completion scores for customer-facing voice agents, with audio samples and methodology.

VAmoS Bench

Leaderboard

| Voice agent | Task completion | Connect | Latency | Turns | Barge-in/call | Cost | STT | LLM | TTS | | -------------------- | --------------- | ------- | ------- | ----- | ------------- | -------- | ------------------------- | --------------- | ----------------- | | pipecat | 71.0% | 100.0% | 1.95s | 9 | 0.17 | \~$0.045 | Deepgram nova-3-general | gpt-4.1-mini | ElevenLabs | | livekit | 70.3% | 100.0% | 2.37s | 7 | 0.07 | \~$0.048 | Deepgram nova-3-general | gpt-4.1-mini | ElevenLabs | | vapi | 69.3% | 97.7% | 2.25s | 13 | 0.54 | \~$0.135 | Deepgram nova-3 | gpt-4.1-mini | ElevenLabs | | elevenlabs | 67.3% | 99.7% | 1.19s | 7 | 0.27 | \~$0.114 | Scribe | gpt-4.1-mini | eleven\_flash\_v2 | | cartesia | 67.0% | 100.0% | 2.23s | 7 | 0.02 | \~$0.146 | Ink | gpt-4.1-mini | Sonic | | openai realtime | 67.0% | 100.0% | 1.53s | 7 | 0.15 | \~$0.074 | gpt-realtime-2 | | | | gemini 2.5 native | 64.0% | 99.3% | 15.95s | 7 | 0.01 | \~$0.023 | Gemini 2.5 Native Audio | | | | gemini 3.1 live | 62.3% | 99.7% | 1.38s | 7 | 0.02 | \~$0.016 | Gemini 3.1 Flash Live | | | | retell | 61.3% | 100.0% | 5.76s | 9 | 1.26 | \~$0.208 | Vendor ASR | gpt-4.1-mini | ElevenLabs | | openai realtime mini | 51.0% | 96.3% | 1.79s | 9 | 0.56 | \~$0.032 | gpt-realtime-2.1-mini | | | | nemotron\* | 43.0% | 89.0% | 2.12s | 7 | 0.10 | n/a | Nemotron | Nemotron 3 Nano | Magpie |

Cost vs. completed tasks

45% 55% 65% 75% $0.00 $0.05 $0.10 $0.15 $0.20 Estimated cost per call (USD) · Y: task completion (% of calls) pipecat livekit vapi elevenlabs cartesia openai realtime gemini 2.5 native gemini 3.1 live retell openai realtime mini

Submit an implementation

This is a living benchmark. Every row is one implementation of Riley, the card-ops agent. Build your own on any framework, platform, or model, including ones already on the board. We’ll run it through the same 100 scenarios and publish the results. Feedback on the methodology is just as welcome.

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