Gemini TTS vs ElevenLabs: compare a real video voiceover through Ofox

Choose a voiceover model by the recording you can accept, the controls available through your actual API route, and the work required to fit the result into your video. Comparing a character price with an audio-token price does not tell you which finished narration costs less. Comparing two different scripts does not tell you which voice handles the same task better.

We sent one original English script through Ofox to elevenlabs/eleven_v4 and google/gemini-3.8-flash-tts on October 10, 2026. Both requests succeeded. The ElevenLabs file lasts 10.00 seconds; the Gemini file lasts 9.76 seconds. You can download both originals below. Those are observations from one take per configuration, not a ranking of naturalness, supported languages or production reliability.

Start with the task, not a universal winner

A ten-second product explainer has different acceptance criteria from an audiobook, a live voice assistant or a multilingual training course. This comparison concerns prepared narration for a video: a script is known before the request, an audio file is returned, and an editor checks it before publication.

It does not test streaming conversation, voice cloning, emotional range or every voice in either catalog. It also does not represent a controlled listening panel. We have verified the generated artifacts and their technical properties; readers should listen to the exact files and evaluate them against their own pronunciation and delivery requirements.

The most useful question is therefore: which of these configurations should you test first for the next specific deliverable? The answer may depend on your existing speech pipeline, target voices, editing budget and verified billing, even when both models can produce the words.

The identical script and the two actual configurations

Both calls used this original text, read from the same UTF-8 file after stripping surrounding whitespace:

A clear product video starts with a clear brief. Show the real interface, explain one useful task, and check the exported video before sharing it.

We kept the words unchanged and requested speed 1.0 in both cases. The voices and output presets differ because the two providers expose different choices through the gateway. That makes this a practical configuration comparison, not an experiment that isolates only the model while holding voice identity and encoding constant.

The elapsed-time column describes a single client request, including network conditions. It is not a measurement of first-byte streaming latency. The byte-size difference is heavily affected by the requested compressed MP3 versus WAV format; it is not evidence that the larger file contains better speech.

Listen to the original files before normalizing anything

Download the ElevenLabs MP3 and Gemini WAV. The complete kit includes the script, client, configuration and metadata, so you can reproduce the test without copying credentials from an example.

ElevenLabs sample audio

Gemini sample audio

Keep these originals for provenance. If you create normalized listening copies, name and document them separately. Loudness differences can influence preference, so a fair listening comparison should use a consistent playback level. That does not mean silently processing the downloads and describing the processed files as untouched API output.

Use headphones or the actual device on which your audience will hear the video. Check whether “API,” a brand name, a date or a key action is clear. Test the full phrase in context rather than selecting the nicest two seconds. A voice that sounds attractive in a short sample may still need too much editing for your real script.

This article does not assign a naturalness score. Without a defined listening protocol and actual listener records, a numerical score would add false precision. The files are offered so that the comparison is inspectable instead of depending on an unsupported “sounds better” claim.

Reproduce the same-script test

After configuring your Ofox key and installing Python requests, FFmpeg and ffprobe, unpack the kit and run each provider once with a new output name:

python3 audio_api.py speech --engine elevenlabs \
  --text comparison.txt --output my-elevenlabs.mp3
python3 audio_api.py speech --engine gemini \
  --text comparison.txt --output my-gemini.wav

These commands make two real requests. Do not run them merely to inspect the published examples; those files are already included. The client stops if the chosen output exists and stores errors separately from audio. It does not use an automatic POST retry loop when an outcome is unknown.

The model pages show the relevant Ofox integrations: ElevenLabs and Gemini TTS. Check them before changing the payload. Native ElevenLabs speech documentation and Google’s TTS overview are useful references, but their full parameter sets should not be assumed to pass unchanged through an adapter.

If one configuration fails, resolve that failure before declaring a voice-quality winner. In earlier attempts, the ElevenLabs route returned an upstream quota error; after restoration, it generated the supplied file. That operational incident does not show that the model’s speech quality is inferior, nor does a later success establish a long-term uptime comparison.

What the 0.24-second duration difference means for editing

In these files, ElevenLabs is 0.24 seconds longer than Gemini, a 2.46% difference relative to the 9.76-second Gemini recording. That can matter near a hard video cut, but it is a tiny, configuration-specific observation. Another take, voice, language or sentence may behave differently.

If your visual ends exactly at ten seconds, inspect the last spoken word and any trailing silence. A container duration does not show where the final word ends. Our separate Scribe transcription of the ElevenLabs sample places the final word’s end at 9.78 seconds; do not transfer that timing to the Gemini file without checking it.

For a longer presentation, build scene-level narration so one revised line does not require regenerating an entire recording. Save the script segment, audio take and scene duration together. If you regenerate a sentence, recalculate the timing and subtitle alignment for that sentence rather than relying on the old file’s duration.

The video voiceover workflow explains measuring audio and combining it with an MP4. The Scribe subtitle tutorial covers deriving captions from actual word timings. Neither model’s audio length should be treated as a promise that your existing timeline will fit unchanged.

A useful listening and editing rubric

Create a short acceptance sheet before choosing a provider. Use criteria that a reviewer can actually check and attach a time range to any issue. This prevents a vague preference from becoming an unrepeatable production standard.

For an internal blind comparison, hide provider labels on listening copies, use the same playback conditions and ask reviewers to record reasons. Then reveal the configurations. If there are only two reviewers or one sentence, report that sample size rather than describing the outcome as the market’s preference.

A transcript check is useful but limited. Scribe recovered the intended words from the ElevenLabs sample, aside from punctuation. That supports text recovery for this clean example; it is not a perceptual quality score and says nothing by itself about the untranscribed Gemini take.

Compare cost per accepted narration, not incompatible unit prices

The current Ofox catalog exposes different pricing fields for these models: ElevenLabs includes a character-based input field, while Gemini TTS includes text-input and audio-output token fields. Those categories explain why a raw number comparison is misleading. They do not supply a settled request-level bill for the downloaded examples.

We have not reconciled these specific calls to individual billing-ledger entries, so the table deliberately leaves out a claimed “actual cost” or percentage saving. Catalog quotes, wallet changes and settled request charges are different evidence. A shared wallet can change because of unrelated work, and a binary audio response does not necessarily include all billable counters.

For your own evaluation, use the request identifiers and the corresponding usage records. Count the text according to the gateway’s billing definition, use the returned or recorded token quantities where relevant, and verify the applicable price snapshot and any rounding. Keep provider pricing separate from the price charged to your Ofox account.

Then include rejected takes. If provider A needs three requests and a manual repair to deliver one accepted line while provider B needs one, the usable-output economics differ from a single-call quote. A practical worksheet is:

accepted_narration_cost = sum(verified charges for all takes)
editing_minutes = time spent reviewing and repairing those takes
acceptance_rate = accepted takes / generated takes

Report the audio scope and review rules with those numbers. Do not convert milliseconds of request time into an invoice, or use download bytes as a substitute for audio-output tokens. When billing data is unavailable, the honest comparison is operational and artifact-based, with cost marked unverified.

Which configuration should you evaluate first?

If your existing project already uses a particular ElevenLabs voice, start with that compatible route and test the actual script. Keeping a known voice can reduce editorial changes, but the sample here does not prove that every account-specific or cloned voice is available through Ofox.

If you are extending the published Ofox Gemini multilingual workflow, start with its verified Gemini configuration so your script and assembly tools remain consistent. Then compare another provider only when you have a concrete reason, such as a preferred compatible voice or a failed pronunciation requirement. Familiarity is an implementation advantage, not proof of universal audio superiority.

For a new project with no established voice, test a short passage containing the hardest terms in the real script. Use both outputs in the same scene. Prefer the option that passes your acceptance criteria with a verified cost you can afford; preserve a fallback configuration and test its actual behavior before relying on it.

For live assistants, voice cloning, unusual languages or strict broadcast delivery, run a separate evaluation. This prepared English video-narration example does not establish those capabilities. Expanding the use case requires additional evidence, not a stronger adjective in the comparison title.

Turn the experiment into a repeatable workflow

Version the source text, model identifier, voice, format, generated file and review outcome as one record. When a model alias changes or you switch the voice, treat the result as a new configuration. A past successful take should not be silently relabeled with today’s model name.

Set a small test budget and a stop condition before a batch. If the route returns an unknown timeout, investigate its status rather than blindly retrying dozens of requests. If both options pass, keep the simplest maintainable workflow until new evidence justifies the cost of switching.

The next useful test is usually your hardest real sentence or a second target language, not a longer list of unsupported feature claims. This sample gives you two inspectable starting points and a method for deciding what counts as a successful voiceover.