HEAD-TO-HEAD BENCHMARK

Familiar vs ElevenLabs Dubbing: Two Paired Studies

FAMILIAR RESEARCHUPDATED ALL ARTICLES
READ THIS INEnglishEspañolPortuguês中文日本語한국어FrançaisDeutschالعربيةBahasa IndonesiaРусскийItalianoTiếng ViệtไทยNederlandsΕλληνικάУкраїнська粵語NorskCatalàHrvatskiLietuviųSlovenčinaSvenskaБългарскиСрпскиБеларускаяDanskҚазақшаLatviešu

ABSTRACT

On the same clips, Familiar sounds 27.8% more like the real speaker than ElevenLabs Dubbing v1, closer in all 11 languages (418 paired outputs, August 5, 2026). On ElevenLabs Dubbing v2 (Alpha), ElevenLabs had 270% more background-sound error (the laughter, the music, the ambience) and Familiar made 54.7% fewer important spoken translation errors (29 vs 64; 111 paired outputs across Mandarin, Spanish, and Japanese). Familiar also led on vocal events in both studies and on predicted naturalness in the Dubbing v1 study. Full data and listening examples: the full ElevenLabs benchmark.

Method

DUBBING V1 · PAIRED OUTPUTS

418

38 clips, 11 languages

DUBBING V2 (ALPHA) · PAIRED OUTPUTS

111

Mandarin, Spanish, Japanese

STUDY DATE

Aug 5, 2026

Both studies

SOURCE CLIPS

38

English, equal weight each

  1. 01

    Paired and black-box

    The same English source clip and target language went to each system; only the final audio was evaluated.
  2. 02

    Meaning review

    Every dub transcribed with word timestamps and reviewed against the same approved target meaning.
  3. 03

    Acoustic measures

    They compare the dub to the source scene: the noise bed, vocal-event envelopes, and transient timing.
  4. 04

    Statistics

    Source clips carry equal weight, 95% intervals resample source clips, and the two cohorts are never pooled.

Familiar's output also carries the lip-synced face; every measure below is audio against audio.

Results: ElevenLabs Dubbing v2 (Alpha) (their newest product)

111 PAIRED FINAL-AUDIO OUTPUTSMANDARINSPANISHJAPANESE
FIG. 01 · IMPORTANT TRANSLATION ERRORS · DUBBING V2 (ALPHA) · FAMILIAR 54.7% FEWER · 111 PAIRED DUBS
ELEVENLABS64
FAMILIAR29
FIG. 02 · FINISHED DUBS WITH ANY IMPORTANT MEANING PROBLEM · DUBBING V2 (ALPHA) · ELEVENLABS +56.5%
ELEVENLABS36/111
FAMILIAR23/111
270%

ElevenLabs produced 270% more background-sound error: the laughter, the music, the ambience.

11.92 VS 3.22 dB MAE
+221%

ElevenLabs produced 221% more laughter and reaction loudness error.

3.32 VS 1.04 dB · 18 CLIPS / 54 COMPARISONS
+28.2%

Familiar's laughter and reaction shape correlation: 0.960 vs 0.749. The paired 95% interval excludes zero.

Results: ElevenLabs Dubbing v1 (the stable product)

418 CLIP-LANGUAGE TRIALS38 CLIPS11 LANGUAGESBOTH SYSTEMS COMPLETED EVERY TRIAL
FIG. 03 · SPEAKER RESEMBLANCE · DUBBING V1 · FAMILIAR +27.8% · ALL 11 LANGUAGES FAVORED FAMILIAR
FAMILIAR0.4605
ELEVENLABS0.3604
FIG. 04 · BACKGROUND SPECTRAL ERROR · DUBBING V1 · dB, LOWER IS BETTER · VERIFIED SCENE REGIONS
ELEVENLABS12.27 dB
FAMILIAR3.40 dB
+95.5%

ElevenLabs had 95.5% more review-flagged spoken-output mistakes.

258 VS 132
+243.7%

Familiar's laughter and reaction shape correlation: 0.759 vs 0.221, verified event regions.

+10.2%

Predicted naturalness for Familiar.

2.51 VS 2.28 · EXPLORATORY
DUBBING V2 (ALPHA)
THE DIMENSIONCoverage
THE RESULTFamiliar completed 38/38 source clips; Dubbing v2 (Alpha) rejected the 10.94-second clip under its 11-second minimum (37/38).
DUBBING V2 (ALPHA)
THE DIMENSIONSpeaker resemblance
THE RESULTInconclusive at this sample size; a +3.5% point estimate for Familiar with a paired interval crossing zero.
DUBBING V1
THE DIMENSIONTransient timing (sound effects and beats)
THE RESULTAlso favored Familiar: F1 0.947 vs 0.762.
ElevenLabsElevenLabsElevenLabsTHEIR DOCS, READ OCTOBER 30, 2026

Editing the translation means their old model.

On ElevenLabs' current Dubbing v2, in alpha, editing the translation and regenerating the audio through the API is for their Enterprise plans only. Their full editor, Dubbing Studio, is "only available via the v1 model" and "in maintenance mode", in their words. Dubbing v1 is the model our 418-pair study measured, and its output is mono.

+27.8%

FAMILIAR CLOSER TO THE REAL SPEAKER · ALL 11 LANGUAGES

261%

ELEVENLABS MORE BACKGROUND ERROR · THE MUSIC, THE AMBIENCE

+243.7%

FAMILIAR ON LAUGHTER AND REACTION SHAPE

ELEVENLABS DUBBING V1 · 418 PAIRED OUTPUTS · 11 LANGUAGES · AUGUST 5, 2026Read the study Their docs

Workflow limits we hit along the way

ELEVENLABS DUBBING3 PRODUCT LIMITSOBSERVED AUG 5, 2026
11 s

Minimum clip length. The 10.94-second clip in our set could not be dubbed at all.

20

Items per batch. Four videos into six languages is already 24 outputs, more than one batch holds.

30

Concurrent requests. Ten videos into ten languages is 100 requests: at least four waves of batching and waiting.

  • Familiar accepts any clip length; one upload fans out to every selected language.

Price

ALL-IN

One Familiar render, per finished minute per target language: the translation, the speaker's own voice, the scene audio, and the lip-sync, delivered up to 4K with a true 5.1 or stereo mix.

ACCESS BY STUDIO CONTRACT
$3

ElevenLabs Dubbing, per dubbed minute per target language. Audio only.

CHECKOUT QUOTE · AUG 5, 2026
$3 buys audio only. To match one Familiar render you still stitch on lip-sync: the best standalone tool, Sync.so, runs $8 a minute, $11 combined, still without a 5.1 mix. ElevenLabs' own documentation: dubs are "at most stereo… 5.1 and other multichannel audio is not preserved." For scale, full human dubbing, translation plus voice, runs $70 to $150 per finished minute; at that rate it isn't usable for most of the world.
Product limits and prices were observed on August 5, 2026 and may change.

The full price study, with sources: How Much Does Dubbing Cost in 2026?

Translation text vs professional translators

A separate August 2026 study put Familiar's production translation against expert professional translators, scored blind against expert reference edits.

Across French, Chinese, Hindi, and Indonesian, Familiar matches their first pass.

FIG. 05 · TRANSLATION QUALITY VS EXPERT FIRST-PASS PROFESSIONAL · BLIND SCORING · AUGUST 2026
FRENCH100.0%
CHINESE98.2%
HINDI103.3%
INDONESIAN99.6%
DASHED LINE = FIRST-PASS PROFESSIONAL (100%)
100.3%

Average of the first-pass professional's translation quality.

FRENCH, CHINESE, HINDI, INDONESIAN (FIRST PASS FOR EXPERT PROFESSIONAL TRANSLATORS)

Limitations

The meaning review is model-assisted and single-pass; independent native double review is pending. Dubbing v2 (Alpha) speaker resemblance and naturalness: inconclusive at this sample size; never pooled with the v1 result. Curated real-world clips, not a probability sample of all video.

REFERENCES

  1. [1]Familiar vs ElevenLabs benchmark: full results, intervals, and listening examples
  2. [2]AI vs Human Translation: Tested Against Professionals (the full translation study)
  3. [3]ElevenLabs Dubbing documentation (pricing by source duration and language count)
  4. [4]ElevenLabs Help Center: which file formats are supported by Dubbing (audio at most stereo; 5.1 not preserved)

Multimodal Translation

Familiar Labs builds generationally better dubbing with a Multimodal Localization Decision Model (MLDM), the first foundation model for multimodal translation: one model decides what to localize across text, audio and visual, and shows you the choices. Your voice and lips, carried into new languages. They're a team of 8 with PhDs, backed by Samsung, RedBird, Gold House. Our mission

See the full benchmark →Talk with the team