How to Compare Voice AI Models on AudioArena

AudioArena lets you compare voice AI models by running them against the same static multi-turn voice agent benchmarks and reading the results side by side. To get a useful comparison, match the benchmark to your real use case, then compare models across all five scoring dimensions rather than a single overall number, and finish by reading the turn-by-turn transcripts to see why one model scored higher.

Step 1: Pick the benchmark that matches your use case

AudioAgentBench includes six static multi-turn voice agent benchmarks. Each one simulates a different kind of conversation, so the right starting point depends on what you're actually building or buying for.

If your scenario doesn't map cleanly onto one benchmark, run more than one and treat the results as a range rather than a single verdict.

Step 2: Select a model and a run

On AudioArena you select a benchmark, a model, and a run to view the results. Because runs are recorded, the same model and benchmark combination can be inspected repeatedly — which is what makes side-by-side comparison possible: hold the benchmark constant, change the model, and compare.

Step 3: Compare across all five scoring dimensions

Each benchmark is scored across five dimensions. Comparing only the aggregate score hides the trade-offs that usually decide a real deployment — a model that sounds natural but drops task details will look fine on a summary score and fail in production.

A practical comparison method:

1. List the models you're considering as rows.
2. List the five scoring dimensions as columns.
3. Fill in each cell from the benchmark results.
4. Mark which dimensions are non-negotiable for your use case (for example, task completion over conversational polish).
5. Eliminate any model that fails a non-negotiable dimension, then rank the rest.

This keeps the decision tied to your requirements instead of to whichever model happens to top the overall ranking.

Step 4: Read the turn-by-turn transcripts

Scores tell you that a model struggled; transcripts tell you where. Turn-by-turn transcripts show the actual conversation, so you can spot patterns such as:

• Losing track of information the user gave earlier in the conversation
• Asking for details that were already provided
• Handling interruptions or topic changes poorly
• Completing the task but in an unnatural or overly verbose way

If two models score similarly, the transcripts are usually what breaks the tie — one may fail gracefully and recover, while the other derails the conversation.

Step 5: Turn the comparison into a decision

Use the results to answer the question AudioArena poses directly: which AI would you call for this task? A defensible decision looks like this:

• Benchmark chosen: the one closest to your real scenario
• Models compared: the shortlist you'd actually deploy
• Deciding dimension: the score that matters most for your use case
• Transcript evidence: a specific moment that shows the difference
• Condition: the choice holds only for scenarios like the benchmark you ran — a model that wins on Appointment isn't automatically the best choice for Grocery or Product

Common pitfalls

• Comparing across different benchmarks. Scores from different benchmarks aren't interchangeable; keep the benchmark fixed when comparing models.
• Trusting a single run. Look at more than one run before drawing conclusions about a model.
• Ignoring the methodology. AudioArena publishes a methodology document and links to its GitHub (Arcada Labs, by The Intelligence Company). Read it before treating scores as definitive, since it defines how the benchmarks and dimensions are scored.
• Over-reading static benchmarks. These are static multi-turn benchmarks, not live calls — they're a structured comparison tool, not a guarantee of real-world performance.