AI Voice Generator Comparison: 8 TTS Models Tested in Polish

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The exact same Polish passage - 62 words packed with a date, monetary value, train number, phone digits, questions, exclamation marks, and notorious tongue-twisters like the town name “Szczebrzeszyn” and the word for “beetle” - was read by 8 AI voice models across cloud endpoints, free tools, and local setups on an RTX 4090. Take the blind test first, then reveal the model tags. Only a single voice completed the test without an error, while two free local models stumbled badly on numbers.

This expands on my ranking of 14 TTS models in Polish. That piece measured cumulative error counts across three long essays. Here, the focus shifts to direct listening: a short text passage, eight voices side-by-side in a blind test, paired with pricing and commercial licensing details.

Benchmark Text

Every voice model received identical plain-text input, without numbers spelled out phonetically:

[Faithful translation of the Polish original test prompt: “Szczebrzeszyn, October 17, 2026, 9:45 AM. A beetle buzzes in the reeds, and Dr. Agnieszka Brzęczyszczykiewicz buys a ticket for 1,249.99 original Polish currency units for the IC 5316 train to Łódź. Will she make it, given that the train is already 25 minutes late? Blade of grass, cuckoo, little bee - try repeating that without stuttering! And now write down: 15% discount, number 601 234 567. Thank you and see you next Thursday in Gdańsk!”]

The text embeds 15 specific evaluation checkpoints:

• Challenging place names and tongue-twisters: the town name Szczebrzeszyn, beetle, the surname Brzęczyszczykiewicz, blade of grass, cuckoo, little bee;
• Inflected number agreement in Polish: ordinals for dates (Polish translation: “the seventeenth of October two thousand twenty-six”), clock time (Polish translation: “nine forty-five”), full currency formatting (Polish translation: “one thousand two hundred forty-nine original Polish currency units and ninety-nine smaller units”), minutes (Polish translation: “twenty-five minutes”), percentages (Polish translation: “fifteen percent”);
• The “IC” train acronym and digits, plus phone numbers split into digit groups;
• Question inflection in the sentence wondering if she will make it on time;
• Completeness - checking whether a model dropped or hallucinated phrases.

Blind Test: Listen Before Revealing Models

Files are ordered randomly, matched in volume, and filenames do not disclose the engine. Note your preferred letter before revealing the source label.

Compare your personal impressions against the structured scoring below - perceived acoustic charm does not always match strict transcription accuracy.

Results: Accuracy Across 15 Evaluation Checkpoints

Scoring methodology: every output file was transcribed through Whisper large-v3 locally and assessed alongside Gemini 3.8 Flash, instructed to transcribe phonetic output and grade every checkpoint. An error was recorded if detected across both transcripts or if Gemini identified an explicit grammatical mismatch. One algorithmic judgment was manually overridden: Piper read the train code as “IC five thousand three hundred sixteen” instead of paired digits, which remains acceptable spoken usage, so the point stood.

Frequent Pronunciation Pitfalls

• Calendar Dates - 6 out of 8 engines failed proper grammatical case agreement, defaulting to nominative structures. Both Edge voices inserted superfluous phrasing.
• Currency Amounts - Only Eleven v4 handled currency units and subunits natively. Edge opted for literal decimal fractions, XTTS and Piper spoke the word “comma”, and GPT Audio dropped the thousands entirely.
• Complex Consonants (tested using the Polish word for “beetle”) - Edge Marek distorted the opening phonemes, Piper simplified them, and Chatterbox output corrupted sounds.
• Soft Consonants (tested using the Polish word for “blade of grass”) - Multiple voices dropped subtle palatalized consonants, including Edge Zofia and GPT Audio Mini.
• Complex Polish Surnames - Almost all models handled long names like “Brzęczyszczykiewicz”, with Piper being the notable exception.
• Interrogative Inflection - Only Eleven v4, GPT Audio, GPT Audio Mini, and Chatterbox modulated pitch upward on questions. Edge, XTTS, and Piper delivered flat statements.
• Phone Numbers - Piper tried reading the 9-digit string as a single massive scalar number. Eleven v4 split it cleanly digit by digit.

TTS Comparison: Pricing, Polish Voices, Licensing, Latency

Latency represents time to first audio chunk (for streaming APIs) or complete file creation (for local inference). Measured on October 8, 2026, over single test runs; use these as baseline indicators rather than a laboratory benchmark.

Dollar prices reflect provider rate sheets and direct OpenRouter billing. Azure figures reflect public listings; confirm your specific tier before deployment.

Local Inference on an RTX 4090: Benchmarks

XTTS generates at a slower pace (53 seconds total runtime versus 35-43 seconds across cloud competitors) with a distinct foreign cadence, yet handles complex names surprisingly well. Chatterbox mandates pre-normalized text: raw numerical strings trigger severe audio distortions. Piper runs effortlessly on modest laptop hardware, though its acoustic profile resembles legacy navigation units. For software installation and environment setup, consult our local AI setup guide.

Why Gemini TTS Was Omitted

Gemini 3.8 Flash TTS and 3.1 Flash TTS scored exceptionally well in our earlier TTS model evaluation (with 3.1 Flash TTS generating an error-free run). For this specific round, access was unavailable: OpenRouter lacked Gemini endpoints with audio generation enabled, and the gateway used during earlier rounds was paused pending quota top-ups. I skipped opening an isolated account for a single audio file. Gemini will return in subsequent updates.

Which AI Voice Model Fits Your Use Case

• Commercial Video Narration and Marketing - Eleven v4. Flawless accuracy and natural cadence. Priced at $0.08 per 1,000 characters (following an introductory $0.022 promo ending October 12). Full breakdown in our ElevenLabs review.
• Real-Time Voice Agents and Conversational Bots - GPT Audio: lowest latency (0.8 s) with solid question inflection, though numbers demand oversight. GPT Audio Mini runs roughly 30 times cheaper, but risks sentence dropping.
• Free Narration and Personal Audiobooks - Edge TTS. Solid baseline quality, provided you pre-process numbers and calendar dates into plain words. Commercial deployment requires paid Azure Speech.
• Air-Gapped and Fully Offline Workloads - XTTS v2 for internal, non-commercial tasks, or Chatterbox (MIT) for commercial pipelines with phonetically normalized inputs.
• Resource-Constrained Hardware - Piper. Sounds mechanical, but runs reliably on minimal CPU power.

Frequently Asked Questions

What is the best AI voice generator for Polish audio?

In this evaluation, ElevenLabs Eleven v4 ranked highest with 15/15 checkpoints cleared and the most natural cadence. Across our wider 14-model TTS evaluation, Gemini 3.1 Flash TTS matched its output quality.

Are there free AI voice generators available?

Yes: Edge TTS (Marek and Zofia voices) and local open-source models like Piper, XTTS v2, and Chatterbox. Edge TTS relies on an unofficial wrapper around Microsoft endpoints and is unsuitable for commercial production. For self-hosted commercial workflows, Chatterbox (MIT) and select Piper voice models offer viable licenses.

Which AI voice model reads numbers and dates most accurately?

Eleven v4. Other models frequently struggle with inflected language cases or read decimals mechanically. Normalizing numbers into fully written words prior to synthesis resolves the vast majority of these errors.

Can AI-generated voices be used commercially?

Licensing dictates usage: ElevenLabs permits commercial rights on paid subscription tiers, OpenAI allows it under developer terms (provided the synthetic nature of the voice is disclosed), Chatterbox uses an MIT license, while XTTS v2 is bound by a non-commercial license. Edge TTS cannot be used commercially; you must purchase official Azure Speech access.

What does AI voice generation cost per minute?

A standard minute of spoken speech uses roughly 700 to 900 text characters. On Eleven v4, that corresponds to roughly $0.06 to $0.07 per minute. GPT Audio Mini costs fractions of a cent, while self-hosted models incur only local electricity costs.

How was this test conducted?

All audio samples were generated on October 8, 2026, using identical source text without manual post-processing. Cloud models were queried through programmatic API calls (ElevenLabs, OpenRouter, Edge TTS), while local weights were executed on a desktop system equipped with an RTX 4090 (24 GB VRAM) under Windows 11.

The link to ElevenLabs is a partner link: if you buy through it, QUANTSLANT earns a commission and you pay the same price. It has no influence on the content, including where the coverage is critical.