activeAgentsAudio

EVA-Bench Experience (EVA-X pass@1)

Metric: EVA-X pass@1 (%) (higher is better)Introduced: 2026

ServiceNow's open-source end-to-end benchmark for enterprise voice agents: simulated callers run full spoken conversations across airline, ITSM and HR/medical scenarios. EVA-X pass@1 is the share of trials where every conversational-experience metric (turn taking, conciseness, conversation progression) clears its threshold, averaged over scenarios with 5 trials each. Pooled across the three domains, clean audio. Kept separate from the EVA-A accuracy axis.

Models ranked

1

tracked on this benchmark

Score band

71.2 – 71.2

best → lowest tracked

Snapshot trend

—

need ≥2 snapshots

Leaderboard

Tracked models ranked by EVA-X pass@1 (%) (higher is better).

Compare candidates
#Model variant and provenanceScore
1
Grok Voice 3.0

Configuration: Grok Voice 3.0 (speech-to-speech)

Version: EVA-Bench EVA-X pass@1, clean, pooled (airline, ITSM, HR)Harness: Not recordedEvaluator: ServiceNow (EVA-Bench)Observed: Nov 9, 2026Confidence: confirmedSource

Notes: Benchmark-owner board: ServiceNow EVA-Bench results site (servicenow.github.io/eva, data file website/src/data/leaderboardStats.json in github.com/ServiceNow/eva at main 8e101906; Grok Voice 3.0 added in commit e5772533 'add grok results', 2026-10-13). System 'Grok Voice 3.0' (id grok-voice-3-0, type s2s). Clean condition, pooled over airline (n=50), ITSM (n=80) and medical_hr (n=83) scenarios, 5 trials each. EVA-X_pass pooled point 0.7120515394912985 (95% CI 0.6741-0.7496), stored as percent rounded to 2 decimals; site text: 'Grok Voice 3.0 (0.74 / 0.71)' (EVA-A / EVA-X pass@1). Retrieved 2026-11-09.

71.2

How to read this benchmark

This benchmark scores models where higher is better. Use scores for directional filtering and shortlisting, not universal quality ranking; then validate pricing, context window, provider availability, and fit for your workload.

Trust this score when

  • There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
  • The model list covers the same version family you can actually deploy today.
  • Top candidates overlap with your required routing and feature requirements.

Be cautious when

  • There is only one benchmark snapshot or the dataset appears stale.
  • The benchmark metric direction is opposite of your decision objective.
  • The score difference between options is narrow and likely within implementation variance.

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Last reviewed: Nov 9, 2026

Resources

WebsiteGitHubarXiv PaperHuggingFace