Cekura Voice Benchmark: ElevenLabs Wins Latency at 1.73s, Retell Wins Reliability at 96.6%

Cekura just published what may be the most methodologically rigorous independent benchmark of voice AI platforms to date. The key insight: when you hold the model, TTS provider, and ASR constant, platform performance differences are stark — and they matter.

Six platforms, one fixed agent, 1,000+ turns each. Here’s what they found.

The Methodology That Makes This Different

Benchmarking voice AI platforms is notoriously difficult because so many variables interact: the language model, the text-to-speech provider, the speech recognition engine, the network path, the turn-taking logic. Comparing Platform A running GPT-4.1 to Platform B running Gemini tells you almost nothing about the platforms themselves.

Cekura solved this by holding everything constant except the platform:

• Fixed model: GPT-4.1 (same on every platform)
• Fixed ASR: Deepgram Nova-3 (same on every platform)
• Fixed TTS: ElevenLabs Flash (same on every platform)
• Test volume: 1,100–1,570 turns per platform, depending on availability

The resulting differences are isolating the platform’s own turn-taking logic, voice activity detection (VAD), endpointing, buffering, and network path — not the underlying AI components. That’s a genuine apples-to-apples comparison.

The Results

P50 Latency (Median Turn Latency)

ElevenLabs wins median latency by a meaningful margin. Retell is a solid second. There’s then a gap to Vapi and LiveKit, with Pipecat and Synthflow notably slower.

However, latency alone doesn’t tell the whole story.

Reliability (pass^3)

Retell wins reliability at 96.6% — the highest pass^3 score in the benchmark. This metric captures whether a call successfully completes a multi-step task sequence, effectively measuring the platform’s consistency under real-world agentic conversation conditions.

Consistency (P5–P95 Spread)

Vapi is the most consistent: P5 latency of 1.66s to P95 latency of 2.95s — a tight spread across the distribution.

ElevenLabs’s advantage in median latency comes with a significant tail: P95 at 3.19s and a recorded maximum of 10.4s. For many use cases this is acceptable, but for latency-sensitive applications like outbound sales or real-time customer service, that tail matters.

Interruption Handling Score (0–5)

Pipecat wins interruption handling — its VAD and barge-in handling allow it to yield to the caller more reliably. LiveKit is close behind. Vapi scores lowest here, suggesting its endpointing logic is less forgiving about mid-conversation interruptions.

Cekura’s methodology: the interruption score is calculated as 5 × (1 − interruptions ÷ turns), clamped to 0–5. A score of 5 means the agent never talked over the caller. The scenarios included barge-in, cough, and mid-turn silence conditions.

How to Read These Results for Your Use Case

Different use cases have different priority hierarchies:

Optimize for median latency (sales calls, customer service, assistants where perceived responsiveness matters most): ElevenLabs is the pick, accepting its higher latency tail as an acceptable tradeoff.

Optimize for reliability (automated workflows, outbound campaigns where completion rate is the primary success metric): Retell is the leader at 96.6% pass^3.

Optimize for consistency (applications where predictable timing matters, like synchronized multi-party calls): Vapi offers the tightest P5–P95 spread.

Optimize for interruption handling (conversations with highly interactive or interruption-prone callers): Pipecat scores highest, and LiveKit is very close.

The benchmark makes explicit that these are platform defaults — not tuned configurations. Cekura’s note: “expect higher peaks once you tune to your use case.” Latency and reliability numbers will improve with custom VAD settings, optimized prompts, and network proximity to provider infrastructure.

Why This Benchmark Hit the Front Page of Hacker News

The Cekura benchmark reached the Hacker News front page on July 27, 2026, because it does something the field has lacked: independent, consistent, quantified platform comparison.

Voice AI practitioners have had to rely on vendor-published numbers, one-off developer tests, or anecdotal performance reports. Cekura’s controlled methodology — same stack, same test corpus, volume sufficient for statistical validity — produces numbers that are actually comparable and reproducible.

The cross-referenced Telnyx resource page, which references the same benchmark data, suggests the numbers are credible beyond Cekura’s own publishing.

The Open Question: Stack Interactions

One significant caveat: Cekura’s benchmark uses ElevenLabs Flash as the fixed TTS provider across all platforms — including ElevenLabs itself. It’s worth asking whether ElevenLabs’s latency advantage is partially a product of running ElevenLabs TTS natively versus through API integration. Platform-native TTS paths often have lower network latency and tighter integration than third-party TTS routed through the platform.

If you’re evaluating voice AI platforms for production, running your own benchmark with your chosen TTS provider (if it differs from ElevenLabs Flash) would be the most representative test.

Sources

1. Cekura Voice Orchestration Benchmarks
2. Telnyx voice AI platform comparison resource
3. Hacker News discussion — July 27, 2026

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260726-2000

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