Compare event interpretation
Teams Interpreter lives inside a Teams meeting. Baian gives an event audience a separate translation link. Start with where your attendees are and how they will join.
Who shows their accuracy?
Side by side · Checked 16 October 2026
Product features, not an accuracy ranking.
| Your requirement | Baian | Microsoft Teams Interpreter |
|---|---|---|
| Audience access | Open the event’s QR code or browser link | Verified Microsoft 365 account with a valid Copilot licence; not anonymous guests ↗ |
| Event format | Stage audio sent to audience phones | Teams meetings and calls; not town halls or webinars ↗ |
| After the meeting | Saved session text can be reviewed in History | Recording keeps original audio, not interpretation audio ↗ |
Results
| Service | Published accuracy evidence |
|---|---|
| Baian | Results on named public tests, with dates and methods. See them ↗ |
| Microsoft Teams Interpreter | No figures; Microsoft says accuracy isn’t guaranteed. Source ↗ |
The practical differences
Teams may suit an internal meeting where participants already have the required accounts. For an open event audience, compare that access requirement with Baian’s phone-browser route.
Customer story · QIYADA, Doha
At Qatar Foundation’s QIYADA conference, attendees scanned the code and followed Arabic and English on their own phones. The interpreter booths were taken down.
Tell us your languages, audience size and sound setup. For up to 10,000 listeners at once, we’ll confirm capacity and support before you book.
Discuss your event ↗WhatsApp More comparisons →1st in Arabic (5.1%) and Korean (2.9%), 2nd in Russian and Danish. Fewer errors than all three OpenAI real-time models in 13 of the 14 languages we publish.
FLEURS: short Wikipedia sentences read aloud. Measures how many words come out wrong.
AssemblyAI published scores for 14 real-time engines. We ran Baian’s live interpreter on the same test set.
We joined clips into 15-minute recordings with 1.5-second gaps and played them into Baian’s live interpreter in real time, like a microphone, with automatic language detection. AssemblyAI streamed each clip separately with the language set in advance. Scoring ignores punctuation, capital letters, Arabic vowel marks and how numbers are written. Our charts show 13 of AssemblyAI’s 14 engines; the full table is on AssemblyAI’s site.
Caption Tool scored above the translators in Hausa, Swahili, Tamil and Urdu (preferred on 67% to 75% of sentences). Baian’s live interpreter had about the same overall quality in Arabic, Hindi and Korean.
148 English sentences that also have professional human translations. Measures translation quality with Google’s MetricX-24 automatic judge.
Baian ran the test. The human translations come from the public FLoRes-200 set.
Baian worked from the spoken audio; the translators worked from text. We compared scores sentence by sentence and counted a result only when the 95% range did not cross zero and two runs agreed. “About the same” means the test found no clear difference; it does not prove the translations were identical.
Median 2.1 seconds across 53 recordings in 15 languages (1.3 to 2.7 seconds per recording).
How long after a sentence ends its finished translation appears.
Baian, on its production Live service.
Measured on Baian’s production Live service from 13 to 15 October 2026, from the end of each spoken sentence to the finished English line in our captured listener feed. Earlier words in a long sentence wait longer than this. Spoken audio arrives a little after the text and was not measured.
Median 2.69 seconds behind the speaker, across 180 speeches.