ElevenLabs alternative: Avencera vs ElevenLabs

At 100 hours a month with speaker labels, ElevenLabs Scribe v2 costs 3.1× as much as Avencera. Avencera’s speaker labels make fewer errors on all 10 recording types in pyannoteAI’s benchmark. ElevenLabs transcribes words more accurately on all 4 published tests that cover both, by up to 4.32 points.

At a glance

ElevenLabs is best known for voice generation, and its Scribe v2 model is one of the most accurate transcription models on public leaderboards. It labels up to 32 speakers and covers 90+ languages.

FeatureAvenceraElevenLabs
Transcript, at 100 hours a month$0.06 an hourScribe v2: $0.22 an hour (3.6×)
With speaker labels, at 100 hours a month$0.07 an hourScribe v2: $0.22 an hour (3.1×)
Speaker labelsIncluded in the speaker transcript priceScribe v2: included
BillingPrepaid credit, no subscriptionScribe v2: pay as you go
Real-time streamingNo, recorded audio onlyYes
LanguagesEnglish with Parakeet v2, 25 European languages with v390+ languages
Longest file2 hours or 100 MB per request, 24 hours as a job10 hours per file
Self-hostingOpen models you can run yourselfPrivate deployment in AWS or Google Cloud, through sales
APIOpenAI-compatibleElevenLabs’s own

Price with speaker labels

Audio a monthAvenceraElevenLabs Scribe v2
10 hours$0.70$2.20 3.1×
100 hours$7.00$22.00 3.1×
1,000 hours$70.00$220.00 3.1×
10,000 hours$700.00$2,200.00 3.1×

See every provider at any volume on the pricing page.

Accuracy

Speaker labels

Avencera’s speaker labels make fewer errors than ElevenLabs Scribe v2 on all 10 recording types in pyannoteAI’s benchmark. Averaged across them, the diarization error rate is 23.5% for Avencera and 39.8% for Scribe v2.

Diarization error rate by recording type, lower is better
  1. Radio interviews 12 recordings, 3 or 4 speakers

    Avencera by 15.9 points

    Avencera community-1: 10.4%. ElevenLabs Scribe v2: 26.3%.

  2. Clinical interviews 51 recordings, 2 or 3 speakers

    Avencera by 23.5 points

    Avencera community-1: 23.5%. ElevenLabs Scribe v2: 47.0%.

  3. Courtroom 12 recordings, 3 to 9 speakers

    Avencera by 11.4 points

    Avencera community-1: 11.6%. ElevenLabs Scribe v2: 23.0%.

  4. Phone calls 61 recordings, 2 speakers

    Avencera by 11.5 points

    Avencera community-1: 12.2%. ElevenLabs Scribe v2: 23.7%.

  5. Task dialogues 19 recordings, 2 speakers

    Avencera by 34.1 points

    Avencera community-1: 10.1%. ElevenLabs Scribe v2: 44.2%.

  6. Meetings 11 recordings, 3 to 6 speakers

    Avencera by 15.5 points

    Avencera community-1: 36.4%. ElevenLabs Scribe v2: 51.9%.

  7. Restaurant conversations 12 recordings, 4 to 8 speakers

    Avencera by 9.7 points

    Avencera community-1: 51.0%. ElevenLabs Scribe v2: 60.7%.

  8. Field interviews 22 recordings, 2 or 3 speakers

    Avencera by 11.2 points

    Avencera community-1: 18.2%. ElevenLabs Scribe v2: 29.4%.

  9. Lab interviews 12 recordings, 2 speakers

    Avencera by 17.4 points

    Avencera community-1: 14.1%. ElevenLabs Scribe v2: 31.5%.

  10. Web video 35 recordings, 1 to 7 speakers

    Avencera by 12.9 points

    Avencera community-1: 47.8%. ElevenLabs Scribe v2: 60.7%.

Words

ElevenLabs transcribes words more accurately on all 4 published tests that cover both, by up to 4.32 points.

Word error rate by benchmark, lower is better
  1. Open ASR Leaderboard, English Average word error rate over 11 English test sets

    Scribe v2 by 0.78 points

    Avencera Parakeet v2: 6.02%. ElevenLabs Scribe v2: 5.24%.

  2. Open ASR Leaderboard, earnings calls Word error rate on the Earnings-22 set

    Scribe v2 by 1.05 points

    Avencera Parakeet v3: 5.85%. ElevenLabs Scribe v2: 4.80%.

  3. Open ASR Leaderboard, long-form Average word error rate on long English recordings

    Scribe v2 by 4.32 points

    Avencera Parakeet v3: 13.37%. ElevenLabs Scribe v2: 9.05%.

  4. Artificial Analysis Word error rate, measured on each provider’s own API

    Scribe v2 by 2.3 points

    Avencera Parakeet v3: 4.5%. ElevenLabs Scribe v2: 2.2%.

Which to choose

Choose ElevenLabs if

  • Word accuracy matters most. Scribe v2 scores 5.24% on the Open ASR Leaderboard, English, against 6.02% for Parakeet v2; Scribe v2 scores 4.80% on the Open ASR Leaderboard, earnings calls, against 5.85% for Parakeet v3; Scribe v2 scores 9.05% on the Open ASR Leaderboard, long-form, against 13.37% for Parakeet v3; Scribe v2 scores 2.2% on the Artificial Analysis, against 4.5% for Parakeet v3.
  • You need real-time streaming. ElevenLabs offers it; Avencera transcribes recorded audio only.
  • You need more languages. ElevenLabs lists 90+ languages.
  • You want to start free. ElevenLabs has a free plan with monthly credits shared across its products.
  • You need an API today. Avencera opens soon.

Choose Avencera if

  • You want the lower bill. ElevenLabs Scribe v2 costs 3.1× as much at 100 hours a month with speaker labels.
  • You want better speaker labels for less. In pyannoteAI’s benchmark, Avencera’s speaker labels make fewer errors than Scribe v2 on all 10 recording types, 23.5% against 39.8% on average.
  • You want prepaid credit with no subscription, no minimum and no expiry.
  • Your audio stays yours. Avencera deletes it once the transcript is ready and never trains on it.

Switching to Avencera

Avencera serves OpenAI’s transcription API, so the official OpenAI SDKs work with a new base URL and model name. Set speaker_labels to true for a speaker on every segment.

from openai import OpenAI

client = OpenAI(base_url="https://api.avencera.com/v1", api_key="YOUR_AVENCERA_KEY")

with open("meeting.mp3", "rb") as audio:
    transcript = client.audio.transcriptions.create(
        model="avencera/parakeet-tdt-0.6b-v2",
        file=audio,
        extra_body={"speaker_labels": True},
    )

Questions

Is Avencera cheaper than ElevenLabs?
Yes. Avencera transcribes from $0.06 an hour, or $0.07 with speaker labels. At 100 hours a month with speaker labels, ElevenLabs Scribe v2 costs $22.00 against $7.00 at Avencera, 3.1× as much.
Is Avencera more accurate than ElevenLabs?
Avencera’s speaker labels make fewer errors than ElevenLabs Scribe v2 on all 10 recording types in pyannoteAI’s benchmark. Averaged across them, the diarization error rate is 23.5% for Avencera and 39.8% for Scribe v2. ElevenLabs transcribes words more accurately on all 4 published tests that cover both, by up to 4.32 points. Scribe v2 scores 5.24% on the Open ASR Leaderboard, English, against 6.02% for Parakeet v2. Scribe v2 scores 4.80% on the Open ASR Leaderboard, earnings calls, against 5.85% for Parakeet v3. Scribe v2 scores 9.05% on the Open ASR Leaderboard, long-form, against 13.37% for Parakeet v3. Scribe v2 scores 2.2% on the Artificial Analysis, against 4.5% for Parakeet v3.
Is ElevenLabs Scribe more accurate than Parakeet?
Yes. Scribe v2 scores a lower word error rate on every benchmark below. Avencera is the cheaper choice when that gap matters less than the bill.
How do I switch from ElevenLabs to Avencera?
Avencera uses OpenAI’s transcription API format, so any OpenAI SDK works. Send the same audio files you send ElevenLabs, and map its response fields to the OpenAI-style transcript.

Sources

Multiples in brackets compare with Avencera’s cheapest offer at the same volume.

Monthly list prices for recorded audio with speaker labels, before free credit, discounts or contracts. Each plan uses whichever of its options costs least at that volume.

Accuracy: Lower is better, and results within 0.1 points count as level. pyannoteAI ran its diarization benchmark itself, without telling any system how many speakers to expect. Avencera’s speaker labels run community-1 through speakrs, which matches pyannote’s published error rates. Averages weight each recording type equally. The Open ASR Leaderboard tests the open Parakeet weights that Avencera runs. Artificial Analysis tests Parakeet v3 as hosted by Together AI.

Vendor prices and features from each vendor’s own pages, checked October 23, 2026. Benchmark results as of October 21, 2026 for the Hugging Face Open ASR Leaderboard, October 23, 2026 for the Artificial Analysis speech-to-text and October 22, 2026 for the pyannoteAI diarization benchmark.