TORNEO · comparison · transcription
Faithfully transcribe audio into text (speech-to-text via API)
done · Observed · OK
Measured with intervals across 2 replicated blocks; the intervals overlap, no tool is ranked first.
Do not name a winner: the confidence intervals overlap. Report the range, the n and the observation date.
| Tool | EstimateShare of recordings transcribed with no error at all | n | Cost per unit (USD) | Latency p50 (ms) |
|---|---|---|---|---|
| gemini-3-5-transcribe | 0.520.42 to 0.62 | 90 | 0.00093 | 1500 |
| gemini-2-5-flash | 0.570.46 to 0.66 | 90 | 0.00090 | 1500 |
| gemini-2-5-pro | 0.610.51 to 0.71 | 90 | 0.0038 | 3400 |
| gemini-3-5-flash | 0.640.54 to 0.74 | 90 | 0.0048 | 2300 |
This is our experience, on our corpus, at this date. It is not buying advice.
Limits of this comparison
- Swiss accents and B2B register are not covered by this run (no open corpus available; phase 2).
- 4 solutions from a single vendor (Google): market coverage is incomplete; the other candidate providers did not run (API credits exhausted, no credential, or an anti-benchmark clause in their terms), so they were blocked, not beaten; provider by provider in categories/transcription/PROVIDERS.json and legal/.
- tnorm.v1 does not normalize numbers.
- Wall-clock latency depends on the runner's network.
- Canonical metric = share of clips transcribed perfectly (tnorm.v1, an API failure counts as an unsuccessful attempt), the only form the L1 scorer recomputes; the micro WER (the metric frozen in the protocol) and its bootstrap 95% CIs remain in RUN_REPORT.json, reproducible from events.l2stub.ndjson (amendment A1).
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