Benchmarking language models and AI performance for voice agents is not about abstract scores. It is about whether your agent actually works in real conversations.
Cekura is built for teams shipping voice agents into production who need confidence across the full stack. Not just text quality. Not just offline benchmarks. Real calls, real users, real failure modes.
Cekura benchmarks language models and AI performance by running your voice agent end to end, exactly the way it operates in the wild.
End-to-end voice agent benchmarking, not isolated model tests
Voice agents fail in places traditional LLM benchmarks never touch. Accents. Interruptions. Latency. Tool calls. Long conversations. Network issues.
Cekura evaluates performance across the entire voice agent stack in one system:
Speech recognition behavior
Instead of scoring raw WER in isolation, Cekura measures how recognition quality affects outcomes. Accents, background noise, fast or hesitant speakers, overlapping speech, fillers, and partial utterances are all simulated using real caller personalities. Streaming behavior is tested live, including partial hypotheses and barge-in timing at the audio level. If ASR struggles, you see it where it matters: interruptions, silence failures, mis-timed responses, and broken flows.
Reasoning and language understanding
Cekura benchmarks how well your agent actually completes tasks. Multi-turn coherence, instruction following, hallucination handling, tool and function calls, and recovery from ambiguity are evaluated across full conversations. The system checks whether the agent stays grounded in its prompt and knowledge, even under stress, contradictions, or incomplete information.
Dialogue management under real pressure
Turn-taking is tested with interrupters, pausers, and impatient callers. Context retention is validated across long conversations. Repair strategies are exercised when users correct themselves or change intent mid-call. Barge-in handling is measured precisely, including sub-second interruptions during active speech.
Text-to-speech and voice delivery
Cekura evaluates naturalness, consistency, and pacing across long responses. Metrics like talk ratio, words per minute, pitch, and voice quality capture whether the agent sounds human, calm, and appropriate. Latency to first audio byte is tracked so you can see how model or infrastructure changes affect responsiveness.
Voice-specific performance metrics that matter in production
Cekura benchmarks voice agents using metrics designed for real-time systems:
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Response latency with percentiles, not just averages
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AI interrupting user and user interrupting AI, with timestamps
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Silence failures and infrastructure stalls
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Repetition, over-talking, and premature call termination
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Sentiment, CSAT, and overall conversational experience
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Tool call success and failure under real conditions
These metrics are computed consistently across simulated calls and production traffic, so your benchmarks reflect reality, not lab conditions.
True model and prompt benchmarking, side by side
Cekura makes it easy to benchmark different language models, prompts, or infrastructure using the exact same scenarios.
Run GPT-4o vs GPT-5. Compare Gemini vs OpenAI. Test prompt revisions or routing changes. Every variation is evaluated against the same callers, the same edge cases, and the same success criteria.
You see where one version improves latency but hurts instruction following. Where another sounds smoother but breaks tool calls. Decisions are based on measured behavior, not assumptions.
Regression testing and drift detection built in
Voice agents change constantly. Models update. Prompts evolve. Infrastructure shifts.
Cekura lets you define a steady-state baseline and automatically replay full test suites when something changes. Performance trends are tracked over time, and alerts fire when accuracy, latency, or experience drifts outside your thresholds.
Production calls can be replayed as new simulations, so real failures become permanent regression tests.
Load, stress, and resilience benchmarking
Cekura benchmarks how your voice agent performs under pressure:
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Parallel call stress testing
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Latency degradation under load
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Infrastructure failures, timeouts, and pauses
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Fallback behavior when APIs slow down or fail
This makes it possible to evaluate failure rates and performance limits before customers experience them.
Designed for teams building real voice systems
Cekura integrates with modern voice stacks including VAPI, Retell, LiveKit, ElevenLabs, and custom infrastructures. It supports chat, voice, SMS, and hybrid workflows. Everything is accessible via UI, API, and CI pipelines.
The result is a single platform for benchmarking language models and AI performance where it actually matters: live voice conversations.
If you are building voice agents that customers depend on, Cekura gives you a clear answer to the only question that matters:
Does this version perform better in the real world?