Is Avencera cheaper than AssemblyAI?

Yes. Avencera transcribes from $0.06 an hour, or $0.07 with speaker labels. At 100 hours a month with speaker labels, AssemblyAI Universal-2 costs $17.00 against $7.00 at Avencera, 2.4× as much.

Is Avencera more accurate than AssemblyAI?

Avencera’s speaker labels make fewer errors than AssemblyAI Universal-3.5 Pro on all 10 recording types in pyannoteAI’s benchmark. Averaged across them, the diarization error rate is 23.5% for Avencera and 33.2% for Universal-3.5 Pro. AssemblyAI transcribes words more accurately on 3 of 4 published tests, by up to 3.02 points; Avencera wins one. Universal-3.5 Pro scores 5.39% on the Open ASR Leaderboard, English, against 6.02% for Parakeet v2. Universal-3.5 Pro scores 6.05% on the Open ASR Leaderboard, earnings calls, against 5.85% for Parakeet v3. Universal-3 Pro scores 10.35% on the Open ASR Leaderboard, long-form, against 13.37% for Parakeet v3. Universal-3.5 Pro scores 3.0% on the Artificial Analysis, against 4.5% for Parakeet v3.

Which AssemblyAI model is closest to Avencera?

Universal-2 is the closer match on price; Universal-3.5 Pro is the closer match on accuracy, and it beats Parakeet on most of the benchmarks below. Avencera costs less than either.

How do I switch from AssemblyAI to Avencera?

Avencera uses OpenAI’s transcription API format, so any OpenAI SDK works. Send the same audio files you send AssemblyAI, and map its response fields to the OpenAI-style transcript.

AssemblyAI alternative: Avencera vs AssemblyAI

At 100 hours a month with speaker labels, AssemblyAI Universal-2 costs 2.4× as much as Avencera. Avencera’s speaker labels make fewer errors on all 10 recording types in pyannoteAI’s benchmark. AssemblyAI transcribes words more accurately on 3 of 4 published tests, by up to 3.02 points; Avencera wins one.

By Praveen Perera. Prices and features checked October 23, 2026.

At a glance

AssemblyAI sells two families of pre-recorded models: Universal-2, which covers 99 languages, and the more accurate Universal-3.5 Pro, which covers 18. Speaker labels are a paid add-on to either.

Feature	Avencera	AssemblyAI
Transcript, at 100 hours a month	$0.06 an hour	Universal-2: $0.15 an hour (2.5×)Universal-3.5 Pro: $0.21 an hour (3.5×)
With speaker labels, at 100 hours a month	$0.07 an hour	Universal-2: $0.17 an hour (2.4×)Universal-3.5 Pro: $0.23 an hour (3.2×)
Speaker labels	Included in the speaker transcript price	Universal-2: $0.02 an hour extraUniversal-3.5 Pro: $0.02 an hour extra
Billing	Prepaid credit, no subscription	Universal-2: pay as you goUniversal-3.5 Pro: pay as you go
Real-time streaming	No, recorded audio only	Yes
Languages	English with Parakeet v2, 25 European languages with v3	99 languages with Universal-2, 18 with Universal-3.5 Pro
Longest file	2 hours or 100 MB per request, 24 hours as a job	10 hours per file
Self-hosting	Open models you can run yourself	Self-hosted deployments
API	OpenAI-compatible	AssemblyAI’s own

Price with speaker labels

Audio a month	Avencera	AssemblyAI Universal-2	AssemblyAI Universal-3.5 Pro
10 hours	$0.70	$1.70 2.4×	$2.30 3.2×
100 hours	$7.00	$17.00 2.4×	$23.00 3.2×
1,000 hours	$70.00	$170.00 2.4×	$230.00 3.2×
10,000 hours	$700.00	$1,700.00 2.4×	$2,300.00 3.2×

See every provider at any volume on the pricing page.

Accuracy

Speaker labels

Avencera’s speaker labels make fewer errors than AssemblyAI Universal-3.5 Pro on all 10 recording types in pyannoteAI’s benchmark. Averaged across them, the diarization error rate is 23.5% for Avencera and 33.2% for Universal-3.5 Pro.

Diarization error rate by recording type, lower is better

Radio interviews 12 recordings, 3 or 4 speakers

Avencera by 8.1 points

Avencera community-1: 10.4%. AssemblyAI Universal-3.5 Pro: 18.5%.
Clinical interviews 51 recordings, 2 or 3 speakers

Avencera by 8.8 points

Avencera community-1: 23.5%. AssemblyAI Universal-3.5 Pro: 32.3%.
Courtroom 12 recordings, 3 to 9 speakers

Avencera by 6.6 points

Avencera community-1: 11.6%. AssemblyAI Universal-3.5 Pro: 18.2%.
Phone calls 61 recordings, 2 speakers

Avencera by 12.8 points

Avencera community-1: 12.2%. AssemblyAI Universal-3.5 Pro: 25.0%.
Task dialogues 19 recordings, 2 speakers

Avencera by 10.0 points

Avencera community-1: 10.1%. AssemblyAI Universal-3.5 Pro: 20.1%.
Meetings 11 recordings, 3 to 6 speakers

Avencera by 8.0 points

Avencera community-1: 36.4%. AssemblyAI Universal-3.5 Pro: 44.4%.
Restaurant conversations 12 recordings, 4 to 8 speakers

Avencera by 11.2 points

Avencera community-1: 51.0%. AssemblyAI Universal-3.5 Pro: 62.2%.
Field interviews 22 recordings, 2 or 3 speakers

Avencera by 10.4 points

Avencera community-1: 18.2%. AssemblyAI Universal-3.5 Pro: 28.6%.
Lab interviews 12 recordings, 2 speakers

Avencera by 14.5 points

Avencera community-1: 14.1%. AssemblyAI Universal-3.5 Pro: 28.6%.
Web video 35 recordings, 1 to 7 speakers

Avencera by 6.0 points

Avencera community-1: 47.8%. AssemblyAI Universal-3.5 Pro: 53.8%.

Words

AssemblyAI transcribes words more accurately on 3 of 4 published tests, by up to 3.02 points; Avencera wins one.

Word error rate by benchmark, lower is better

Open ASR Leaderboard, English Average word error rate over 11 English test sets

Universal-3.5 Pro by 0.63 points

Avencera Parakeet v2: 6.02%. AssemblyAI Universal-3.5 Pro: 5.39%.
Open ASR Leaderboard, earnings calls Word error rate on the Earnings-22 set

Avencera by 0.20 points

Avencera Parakeet v3: 5.85%. AssemblyAI Universal-3.5 Pro: 6.05%.
Open ASR Leaderboard, long-form Average word error rate on long English recordings

Universal-3 Pro by 3.02 points

Avencera Parakeet v3: 13.37%. AssemblyAI Universal-3 Pro: 10.35%.
Artificial Analysis Word error rate, measured on each provider’s own API

Universal-3.5 Pro by 1.5 points

Avencera Parakeet v3: 4.5%. AssemblyAI Universal-3.5 Pro: 3.0%.

Choose AssemblyAI if

Word accuracy matters most. Universal-3.5 Pro scores 5.39% on the Open ASR Leaderboard, English, against 6.02% for Parakeet v2; Universal-3 Pro scores 10.35% on the Open ASR Leaderboard, long-form, against 13.37% for Parakeet v3; Universal-3.5 Pro scores 3.0% on the Artificial Analysis, against 4.5% for Parakeet v3.
You need real-time streaming. AssemblyAI offers it; Avencera transcribes recorded audio only.
You need more languages. AssemblyAI lists 99 languages with Universal-2, 18 with Universal-3.5 Pro.
You want to start free. AssemblyAI gives $50 of free credit at sign-up.
You need an API today. Avencera opens soon.

Choose Avencera if

You want the lower bill. AssemblyAI Universal-2 costs 2.4× 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 Universal-3.5 Pro on all 10 recording types, 23.5% against 33.2% on average.
You want word accuracy for less. Universal-3.5 Pro scores 6.05% on the Open ASR Leaderboard, earnings calls, against 5.85% for Parakeet v3.
You want prepaid credit with no subscription, no minimum and no expiry.
You want speaker labels in the headline price. AssemblyAI adds a per-hour charge for them on top of every model.
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 AssemblyAI?: Yes. Avencera transcribes from $0.06 an hour, or $0.07 with speaker labels. At 100 hours a month with speaker labels, AssemblyAI Universal-2 costs $17.00 against $7.00 at Avencera, 2.4× as much.
Is Avencera more accurate than AssemblyAI?: Avencera’s speaker labels make fewer errors than AssemblyAI Universal-3.5 Pro on all 10 recording types in pyannoteAI’s benchmark. Averaged across them, the diarization error rate is 23.5% for Avencera and 33.2% for Universal-3.5 Pro. AssemblyAI transcribes words more accurately on 3 of 4 published tests, by up to 3.02 points; Avencera wins one. Universal-3.5 Pro scores 5.39% on the Open ASR Leaderboard, English, against 6.02% for Parakeet v2. Universal-3.5 Pro scores 6.05% on the Open ASR Leaderboard, earnings calls, against 5.85% for Parakeet v3. Universal-3 Pro scores 10.35% on the Open ASR Leaderboard, long-form, against 13.37% for Parakeet v3. Universal-3.5 Pro scores 3.0% on the Artificial Analysis, against 4.5% for Parakeet v3.
Which AssemblyAI model is closest to Avencera?: Universal-2 is the closer match on price; Universal-3.5 Pro is the closer match on accuracy, and it beats Parakeet on most of the benchmarks below. Avencera costs less than either.
How do I switch from AssemblyAI to Avencera?: Avencera uses OpenAI’s transcription API format, so any OpenAI SDK works. Send the same audio files you send AssemblyAI, and map its response fields to the OpenAI-style transcript.