omi-medical-1: The Most Accurate Medical Speech-to-Text We've Measured
General speech models guess when they mishear. In an ordinary transcript that produces a typo; in a clinical note it can invent a drug that was never said. omi-medical-1 is our flagship model built for the other outcome: the most accurate medical speech-to-text we have measured across 28 systems — every major cloud API, the dedicated medical variants, and the leading open models — with zero fabricated drug names on every test set we have ever run.
On OmiMedSTT-Bench, our sealed clinical benchmark, it ranks #1 of 28 on Medical WER — the error rate on the words that carry clinical meaning — while matching or beating the best general models on overall word error rate. It runs in our EU cloud (on AWS) today, and can be self-hosted in your own environment. The model we benchmark is the model you call.
This page is the model write-up: what it is, how it scores, and how to run it. The complete, current evaluation — leaderboard, dataset card, methodology — lives on our standing medical STT benchmark.
How it was measured
On OmiMedSTT-Bench, our sealed clinical test set: 1,513 audio segments, 7.2 hours of English medical speech across five scenario types — from short clinical utterances to complete 14-minute consultations — scored by the same open pipeline for every system and ranked medical-first: Medical WER, then drug-name errors, then overall WER. Cloud systems are called through their public production APIs; open models run self-hosted at full precision. Zero file-level overlap between our training audio and the test set. Full dataset card, scoring convention and the complete 30-system leaderboard: the benchmark page.
Results — July 2026 board
Current standing on the benchmark. omi-medical-1 is the top of the board on medical-term accuracy, and its overall word error rate sits inside the noise band of the best general model — the lead we claim is on the clinical words and on safety, not a wide raw-WER margin.
| # | System | WER | M-WER | Fabricated drugs |
|---|---|---|---|---|
| 1 | omi-medical-1 (ours · live API) | 5.99% | 0.94% | 0 / 442 |
| 2 | ElevenLabs Scribe v2 | 6.17% | 0.97% | 0 / 442 |
| 3 | Google Chirp 3 | 6.30% | 1.11% | 5 / 442 |
| 8 | Azure MAI-Transcribe-1.5 | 5.97% | 1.43% | 4 / 442 |
One system — Azure's MAI-Transcribe-1.5 — edges our raw WER by two-hundredths of a point (5.97% vs 5.99%, a difference of roughly a dozen words across the set, i.e. noise). It is not medically tuned: its Medical WER is 1.43% against our 0.94%, and it fabricates drug names where we fabricate none. That gap is the whole point of a medical model.
- #1 of 28 on Medical WER (0.94%) — ahead of every cloud API, every dedicated medical variant, and every open model on the board.
- Zero drug-name errors across all 442 drug occurrences, and zero runaway files — no looping or invented passages requiring exclusion. Only omi-medical-1 and ElevenLabs needed zero exclusions.
- Matches-or-beats the best general models on overall WER (5.99%, within noise of the top raw-WER system), while leading decisively on the clinical terms.
Full leaderboard and methodology →
Where the gap widens: real consultations
Short utterances flatter every system; long, messy, real consultations separate them. On a separate long-form companion set — 22 complete real consultations (4.1 hours), every provider on identical audio, references human-audited — omi-medical-1 leads the next best system by 1.4 percentage points of word accuracy, a bootstrap-certain margin.
| System | Word accuracy | vs omi-medical-1 |
|---|---|---|
| omi-medical-1 | 94.0% | — |
| Gemini 3.1 Pro | 93.2% | −0.8 pt |
| ElevenLabs Scribe v2 | 92.6% | −1.4 pt |
The pattern is consistent with the design of the model: the systems that lead on clean, short audio degrade faster on the long, overlapping, accented speech of a genuine clinic. omi-medical-1 is built to hold up there.
Medical branding is not medical accuracy
Several vendors ship a "medical" speech-to-text variant. On our benchmark, being labelled medical did not predict medical accuracy: the dedicated medical products generally did not lead on clinical terms — the strongest general models did, and omi-medical-1 leads both. Amazon Transcribe Medical, a Google medical-conversation model and Speechmatics' medical mode all scored worse on medical vocabulary than the best general engines on identical audio. It is a reminder that "medical" is a badge until a benchmark makes it a number — which is why we publish the scorer and the full board.
How to run it
omi-medical-1 is available today as a managed cloud API. The endpoint is OpenAI-compatible, so most existing speech-to-text integrations work by changing the base URL and the model name:
$ curl https://api.omi.health/v1/audio/transcriptions \
-H "Authorization: Bearer $OMI_API_KEY" \
-F model="omi-medical-1" \
-F file=@consultation.wavThree ways to deploy, one model:
- Batch API — live. OpenAI-compatible endpoint. Handles everything from short clips to full-length consultations.
- Realtime — available on request. Live streaming transcription for in-consultation use, enabled per API key.
- Custom vocabulary — live. Pass a focused encounter list of medicines, devices, names and local terms, with an automatic safety fallback.
- More languages — live. Spanish, Portuguese, French, German, Dutch, Arabic and Hindi, including regional BCP-47 variants.
- Speaker decoration — live. Speaker labels and word timestamps attach structure without changing the sealed transcript text.
- Self-host / your cloud. Run omi-medical-1 inside your own VPC with SSO for data-residency or air-gapped requirements.
See the models and quickstart → Read the API guide →
Data and privacy
omi-medical-1 is available as a managed service and as a customer-hosted deployment. We never train on customer data, there is no shared data plane between customers, and audio submitted to the playground is deleted after transcription. Self-hosting is available where data must not leave your environment at all. Sub-processors and the full data-handling terms are published in our legal pages.
Limitations
- Raw WER is a tie at the top. The best general model matches our overall word error rate within noise; our advantage is on medical terms and on safety (zero fabricated drugs), not a large raw-WER gap. We say "matches-or-beats on WER, ahead on medical accuracy" for exactly this reason.
- The benchmark is our own sealed set. We built and hold OmiMedSTT-Bench. The mitigations are an open scorer, published methodology, a human-audit protocol, and full-set re-runs under NDA. A fully provider-disjoint third-party holdout is planned; until it is published we do not make an unqualified "beats system X" claim — we report the numbers and let the method be checked.
- English is the only medically evaluated language. Spanish, Portuguese, French, German, Dutch, Arabic and Hindi are supported. Formal medical evaluation in each of those languages is still in progress.
- omi-medical-1 is speech-to-text only. It is not a diagnostic, triage, prescribing or clinical decision model, and it is not clinically validated. Transcripts must be reviewed before any clinical use.
What's next
Three things are in active development: expanding realtime streaming from request-based preview; completing local-language medical evaluation across the seven other supported languages; and extending custom vocabulary and structured-token support. On the evaluation side, a provider-disjoint third-party holdout is the next step in making the #1 claim independently checkable.
Get a short note when realtime access expands and new medical-language evaluations ship — no newsletter spam.
Where to find it
- Models & quickstart: omi.health/speech
- Standing benchmark: Medical Speech-to-Text Benchmark (28 systems)
- Open scorer & evaluation code: github.com/Omi-Health/medical-STT-eval
- API key / self-host: hello@omi.health
- Pairs with: omi-medical-edge-1 — the open, on-device model for when audio cannot leave the machine.
Cite this model
APA — Omi Health. (2026). omi-medical-1: The Most Accurate Medical Speech-to-Text We've Measured. https://omi.health/research/omi-medical-1
@misc{omi_medical_1_2026,
title = {omi-medical-1: The Most Accurate Medical Speech-to-Text We've Measured},
author = {{Omi Health}},
year = {2026},
url = {https://omi.health/research/omi-medical-1},
note = {Flagship medical ASR, #1 of 28 on OmiMedSTT-Bench v5 (sealed), EU cloud / self-host}
}Related research
- Medical Speech-to-Text Benchmark — 28 systems ranked by Medical WER on a sealed test set
- omi-medical-edge-1 — the open, on-device 0.6B medical speech-to-text model
- Clinical SOAP Note Safety Evaluation — 6 models, 300 dialogues, safety-first scoring