GPT-4o-mini TTS

The OpenAI `gpt-4o-mini-tts` model is now supported in [HyperTTS](https://www.vocab.ai/hypertts). This is a feature contributed by Claus (thank you!). You have to select it in the voice options (the default is still `tts-1-hd` for OpenAI). This voice model accepts an optional `instruction` field. You can use it to instruct the model to speak in a certain way, or indicate that the source text is in a particular language. For more details, you can consult the [OpenAi reference](https://platform.openai.com/docs/guides/text-to-speech#text-to-speech-models). As with neural and LLM models, the actual output will really vary with the situation, so you'll have to experiment. Claus' feedback is: *GPT-4o mini TTS is the first OpenAI TTS model that provides usable output for my Greek flashcards.* The common feedback with OpenAI (and also ElevenLabs) is that non-english output was not that good and suffered from an american accent. Hopefully this new model improves that.

https://preview.redd.it/qdknt6tjdh9f1.png?width=2176&format=png&auto=webp&s=b10eb1eaebbe698d7b2f15658b7d59508374abfa

So what's next ? People have been asking for Google Gemini TTS. I've been working on this for [HyperTTS](https://www.vocab.ai/hypertts) but there's a serious limitation, google limits requests to 10 per minute, even on Tier 1 paid accounts. This means mass-generating Gemini audio will be a tedious process, and HyperTTS will need to implement some retry logic, which will be welcome anyway, to handle the occasional timeout. Besides that here are the [issues](https://github.com/Vocab-Apps/anki-hyper-tts/issues?q=is%3Aissue%20state%3Aopen%20label%3Apriority) that I will tackle in the coming weeks in HyperTTS.

Besides that, in the coming months, I'd like to make progress on an idea I started before Christmas: generating long-play audio files with Anki flashcard sounds, so that you can review your deck while walking. Kind of like a podcast. I have a working prototype but I need to finish it. I'm also actively thinking about how [Language Tools](https://www.vocab.ai/language-tools) can use LLMs. This is honestly an overdue feature given how AI chatbots have become amazing at translation, but also transliteration (for Chinese, you can confidently ask gpt-4 to convert to Pinyin).

[https://www.vocab.ai/updates](https://www.vocab.ai/updates)

I'm really confused as lots of posts say it's per 1M characters but the docs say 1M tokens — which would be an incredibly competitive rate, almost 8x cheaper than elevenlabs.

Boson AI has recently open-sourced the Higgs Audio V2 model.
[https://huggingface.co/bosonai/higgs-audio-v2-generation-3B-base](https://huggingface.co/bosonai/higgs-audio-v2-generation-3B-base)

The model demonstrates strong performance in automatic prosody adjustment and generating natural multi-speaker dialogues across languages .

Notably, it achieved a 75.7% win rate over GPT-4o-mini-tts in emotional expression on the EmergentTTS-Eval benchmark . The total parameter count for this model is approximately 5.8 billion (3.6B for the LLM and 2.2B for the Audio Dual FFN)

Over the next three to six months, we will deprecate support for the older OpenAI model set to improve reliability and simplify model selection. Newer alternatives will be recommended for each model.

Access to these models in the API will be closed on the following dates:

End of Service July 23, 2026

preview-computer-use-2025-03-11

gpt-4o-audio-preview-2024-12-17

gpt-4o-mini-audio-preview-2024-12-17

gpt-4o-mini-realtime-preview-2024-12-17

gpt-4o-mini-search-preview-2025-03-11

gpt-4o-mini-tts-2025-03-20

gpt-4o-search-preview-2025-03 -11

gpt-5-chat-latest

gpt-5-codex

gpt-5.1-chat-latest

gpt-5.1-codex

gpt-5.1-codex-max

gpt-5.2-codex

gpt-5.1-codex-mini

gpt-audio-mini-2025-10-06

gpt-realtime-mini-2025-10-06

o3-deep-research-2025-06-26

o4-mini-deep-research-2025-06-26

Termination of service on October 23 2026

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gpt-3.5-turbo-0125

gpt-4-0613

gpt-4-1106-preview

gpt-4-turbo

gpt-4.1-nano

gpt-4o-2024-05-13

gpt-image-1

o1-2024-12-17

o1-pro-2025-03-19

o3-mini-2025-01-31o4-mini-2025-04-16

Improved versions of models that are outdated:

GPT-3.5 Turbo Tuned Versions

Gpt-4.1-nano-2025-04-1

Babbage-002 Tuned Versions

Davinci-002 Tuned Versions

o4-mini-2025-04-16

Tuned GPT-4 Version

We encourage you to explore our latest flagship models, which we present here, which offer improved performance and broader feature support.

Thank you for building with OpenAI. If you have any questions about model migration, please feel free to reach out to the OpenAI Developer Forum.

Hello, I am having the darndest time with this.

Recently OpenAI announced that they are retiring `gpt-4o-mini-tts-2025-03-20`, which in my experience is the most flexible TTS model I’ve ever used. My main concern is with voice acting and adherence to directions. That model lets you prompt *how* something should be spoken, and it actually follows those instructions extremely well.

I have a chatbot setup where the LLM generates both the text response and a short set of TTS instructions describing how it should sound. So if the reply is angry, it will also instruct the TTS to speak in an angry tone. If I ask it to do a character impression (like a Batman-style voice), it will describe a low, gravelly, menacing delivery and the TTS actually performs it that way for that turn. Same with accents, whispering, yelling, sounding bored, excited, sarcastic, etc. It’s not just emotion labels, it’s more like full “acting direction,” and it works shockingly well.

Another big reason I’ve been using it is speed. It usually returns first audio in about 600–800ms, which is fast enough for real-time use.

The problem is that the newer OpenAI TTS model that’s supposed to replace it doesn’t behave the same way. In my testing it mostly ignores those kinds of instructions and delivers everything in a very flat, monotone style. So I’ve been trying to find something else that can match that combination of flexibility and speed, ideally something I can run locally on an RTX 5080, but I’m open to cloud options if they’re fast enough.

So far, I haven’t had much luck. Google Gemini 3.1 TTS is actually very solid in terms of following instructions and doing expressive delivery, but it’s far too slow for my use case, around 5 seconds to first audio.

FasterQwen TTS is extremely fast (under 500ms for me), but it doesn’t really follow instructions well. You can kind of work around it by generating voices with pre-baked emotional tones and then cloning from those, but that’s nowhere near as flexible as just telling the model what to do on the fly. I also haven’t been able to get it to produce accents at all.

Fish Audio S2 looked promising from a quality standpoint, but I couldn’t get it to run on my hardware at any reasonable speed.

ElevenLabs I haven’t explored deeply yet, mostly because the pricing looked high for the amount of experimentation I’m doing.

Cartesia, Resemble, and a few others I tried didn’t seem to have the same level of control either, especially for things like accents or more specific vocal qualities like a raspy or strained voice.

At this point it feels like most systems force a tradeoff between having a consistent voice and having strong “acting” or prosody control. The older OpenAI model was the only one I’ve used that really handled both at the same time.

Is there anything out there right now, local or cloud, that can match that combination of low latency and strong instruction-following for delivery? Or is this still kind of an unsolved problem outside of that specific OpenAI model?