New model named Chatterbox by Resemble AI's first production-grade open source TTS model. Licensed under MIT, Chatterbox has been benchmarked against leading closed-source systems like ElevenLabs, and is consistently preferred in side-by-side evaluations.  Whether you're working on memes, videos, games, or AI agents, Chatterbox brings your content to life. It's also the first open source TTS model to support emotion exaggeration control, a powerful feature that makes your voices stand out.

chatterbox

The DeepSeek R1 model has undergone a minor version upgrade, with the current version being DeepSeek-R1-0528.

DeepSeek-R1-0528

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support

Qwen3-235B-A22B

Qwen3-30B-A3B

Qwen3-32B

Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support. 

Qwen3-14B

DeepSeek-Prover-V2, an open-source large language model designed for formal theorem proving in Lean 4, with initialization data collected through a recursive theorem proving pipeline powered by DeepSeek-V3. The cold-start training procedure begins by prompting DeepSeek-V3 to decompose complex problems into a series of subgoals. The proofs of resolved subgoals are synthesized into a chain-of-thought process, combined with DeepSeek-V3's step-by-step reasoning, to create an initial cold start for reinforcement learning. 

DeepSeek-Prover-V2-671B

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Maverick, a 17 billion parameter model with 128 experts

Llama-4-Maverick-17B-128E-Instruct-FP8

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Scout, a 17 billion parameter model with 16 experts

Llama-4-Scout-17B-16E-Instruct

We introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. 

DeepSeek-R1-Turbo

DeepSeek-R1

Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and extends context capabilities up to 128K tokens while maintaining top-tier text performance. Its 24 billion parameters and instruction fine-tuning deliver fast, local deployment for both text and vision tasks.

Mistral-Small-3.1-24B-Instruct-2503

Phi-4-reasoning-plus is a state-of-the-art open-weight reasoning model finetuned from Phi-4 using supervised fine-tuning on a dataset of chain-of-thought traces and reinforcement learning. The supervised fine-tuning dataset includes a blend of synthetic prompts and high-quality filtered data from public domain websites, focused on math, science, and coding skills as well as alignment data for safety and Responsible AI. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning. Phi-4-reasoning-plus has been trained additionally with Reinforcement Learning, hence, it has higher accuracy but generates on average 50% more tokens, thus having higher latency.

phi-4-reasoning-plus

Llama Guard 4 is a natively multimodal safety classifier with 12 billion parameters trained jointly on text and multiple images. Llama Guard 4 is a dense architecture pruned from the Llama 4 Scout pre-trained model and fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It itself acts as an LLM: it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Llama-Guard-4-12B

QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini.

QwQ-32B

DeepSeek-V3-0324, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token, an improved iteration over DeepSeek-V3.

DeepSeek-V3-0324

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 27B is Google's latest open source model, successor to Gemma 2

gemma-3-27b-it

Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3-12B is Google's latest open source model, successor to Gemma 2

gemma-3-12b-it

gemma-3-4b-it

Kokoro is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects.

Kokoro-82M

Dia directly generates highly realistic dialogue from a transcript. You can condition the output on audio, enabling emotion and tone control. The model can also produce nonverbal communications like laughter, coughing, clearing throat, etc.

Dia-1.6B

Orpheus TTS is a state-of-the-art, Llama-based Speech-LLM designed for high-quality, empathetic text-to-speech generation. This model has been finetuned to deliver human-level speech synthesis, achieving exceptional clarity, expressiveness, and real-time streaming performances.

orpheus-3b-0.1-ft

CSM (Conversational Speech Model) is a speech generation model from Sesame that generates RVQ audio codes from text and audio inputs. The model architecture employs a Llama backbone and a smaller audio decoder that produces Mimi audio codes.

csm-1b

Phi-4-multimodal-instruct is a lightweight open multimodal foundation model that leverages the language, vision, and speech research and datasets used for Phi-3.5 and 4.0 models. The model processes text, image, and audio inputs, generating text outputs, and comes with 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning, direct preference optimization and RLHF (Reinforcement Learning from Human Feedback) to support precise instruction adherence and safety measures. The languages that each modal supports are the following: - Text: Arabic, Chinese, Czech, Danish, Dutch, English, Finnish, French, German, Hebrew, Hungarian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, Turkish, Ukrainian - Vision: English - Audio: English, Chinese, German, French, Italian, Japanese, Spanish, Portuguese

Phi-4-multimodal-instruct

DeepSeek-R1-Distill-Llama-70B is a highly efficient language model that leverages knowledge distillation to achieve state-of-the-art performance. This model distills the reasoning patterns of larger models into a smaller, more agile architecture, resulting in exceptional results on benchmarks like AIME 2024, MATH-500, and LiveCodeBench. With 70 billion parameters, DeepSeek-R1-Distill-Llama-70B offers a unique balance of accuracy and efficiency, making it an ideal choice for a wide range of natural language processing tasks. 

DeepSeek-R1-Distill-Llama-70B

DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. 

DeepSeek-V3

Llama 3.3-70B Turbo is a highly optimized version of the Llama 3.3-70B model, utilizing FP8 quantization to deliver significantly faster inference speeds with a minor trade-off in accuracy. The model is designed to be helpful, safe, and flexible, with a focus on responsible deployment and mitigating potential risks such as bias, toxicity, and misinformation. It achieves state-of-the-art performance on various benchmarks, including conversational tasks, language translation, and text generation.

Llama-3.3-70B-Instruct-Turbo

Llama 3.3-70B is a multilingual LLM trained on a massive dataset of 15 trillion tokens, fine-tuned for instruction-following and conversational dialogue. The model is designed to be helpful, safe, and flexible, with a focus on responsible deployment and mitigating potential risks such as bias, toxicity, and misinformation. It achieves state-of-the-art performance on various benchmarks, including conversational tasks, language translation, and text generation.

Llama-3.3-70B-Instruct

Phi-4 is a model built upon a blend of synthetic datasets, data from filtered public domain websites, and acquired academic books and Q&A datasets. The goal of this approach was to ensure that small capable models were trained with data focused on high quality and advanced reasoning.

phi-4

Whisper is a state-of-the-art model for automatic speech recognition (ASR) and speech translation, proposed in the paper "Robust Speech Recognition via Large-Scale Weak Supervision" by Alec Radford  et al. from OpenAI. Trained on >5M hours of labeled data, Whisper demonstrates a strong ability to generalise to many datasets and domains in a zero-shot setting. Whisper large-v3-turbo is a finetuned version of a pruned Whisper large-v3. In other words, it's the exact same model, except that the number of decoding layers have reduced from 32 to 4. As a result, the model is way faster, at the expense of a minor quality degradation.

whisper-large-v3-turbo

You can use cURL or any other http client to run inferences:

```bash
curl -X POST \
    -d '{"text": "The quick brown fox jumps over the lazy dog"}'  \
    -H "Authorization: bearer $DEEPINFRA_TOKEN"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/hexgrad/Kokoro-82M'
```

which will give you back something similar to:

```json
{
  "audio": null,
  "input_character_length": 0,
  "output_format": "",
  "words": [
    {
      "end": 1.0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "start": 4.0,
      "text": "World"
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


You can use our command-line tool [deepctl](/docs/advanced/deepctl) to run
inferences:

```bash
deepctl infer \
    -m 'hexgrad/Kokoro-82M'  \
    -i 'text=The quick brown fox jumps over the lazy dog'
```

which will give you back something similar to:

```json
{
  "audio": null,
  "input_character_length": 0,
  "output_format": "",
  "words": [
    {
      "end": 1.0,
      "start": 0.0,
      "text": "Hello"
    },
    {
      "end": 5.0,
      "start": 4.0,
      "text": "World"
    }
  ],
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


The DeepInfra OpenAI-compatible Speech API endpoint enables users to effortlessly convert text into speech audio. This document outlines how to integrate and utilize this endpoint to quickly create speech from text inputs, leveraging various audio output formats.

## Create speech

Use the following example of pythong code to generate an audio file from your text input:

```python
from pathlib import Path
from openai import OpenAI

client = OpenAI(base_url="https://api.deepinfra.com/v1/openai",
                api_key="$DEEPINFRA_TOKEN")

speech_file_path = Path(__file__).parent / "speech.mp3"
with client.audio.speech.with_streaming_response.create(
  model="hexgrad/Kokoro-82M",
  voice="af_bella",
  input="The quick brown fox jumped over the lazy dog.",
  response_format="mp3",
) as response:
  response.stream_to_file(speech_file_path)
```

The API returns the generated audio file in the requested format (e.g., `mp3`, `pcm`). The example above saves the audio output directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Best for lowest latency streaming*        |
| mp3    | Compressed audio suitable for storage | General use and file distribution          |
| opus   | Compressed audio ideal for streaming  | Efficient streaming and real-time playback |
| flac   | Lossless audio compression            | High-quality archival storage              |
| wav    | Uncompressed audio                    | High-quality audio processing tasks        |

### Performance Recommendations

- **For lowest latency (fastest initial audio chunk):**
  - Use the `pcm` output format.

- **For general purposes:**
  - Use the `mp3` or `opus` formats for optimal balance between quality and file size.



The DeepInfra OpenAI-compatible Speech API endpoint enables users to effortlessly convert text into speech audio. This document outlines how to integrate and utilize this endpoint to quickly create speech from text inputs, leveraging various audio output formats.

## Create speech

Use the following example of js code to generate an audio file from your text input:

```javascript
import fs from "fs";
import path from "path";
import OpenAI from "openai";

const openai = new OpenAI(base_url="https://api.deepinfra.com/v1/openai",
                          api_key="$DEEPINFRA_TOKEN");

const speechFile = path.resolve("./speech.mp3");

async function main() {
  const mp3 = await openai.audio.speech.create({
    model: "hexgrad/Kokoro-82M",
    voice: "af_bella",
    input: "The quick brown fox jumped over the lazy dog.",
    response_format: "mp3",
  });
  console.log(speechFile);
  const buffer = Buffer.from(await mp3.arrayBuffer());
  await fs.promises.writeFile(speechFile, buffer);
}
main();
```

The API returns the generated audio file in the requested format (e.g., `mp3`, `pcm`). The example above saves the audio output directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Best for lowest latency streaming*        |
| mp3    | Compressed audio suitable for storage | General use and file distribution          |
| opus   | Compressed audio ideal for streaming  | Efficient streaming and real-time playback |
| flac   | Lossless audio compression            | High-quality archival storage              |
| wav    | Uncompressed audio                    | High-quality audio processing tasks        |

### Performance Recommendations

- **For lowest latency (fastest initial audio chunk):**
  - Use the `pcm` output format.

- **For general purposes:**
  - Use the `mp3` or `opus` formats for optimal balance between quality and file size.



The DeepInfra OpenAI-compatible Speech API endpoint enables users to effortlessly convert text into speech audio. This document outlines how to integrate and utilize this endpoint to quickly create speech from text inputs, leveraging various audio output formats.

## Create speech

Use the following example `curl` request to generate an audio file from your text input:

```bash
curl https://api.deepinfra.com/v1/openai/audio/speech \
  -H "Authorization: Bearer $DEEPINFRA_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "hexgrad/Kokoro-82M",
    "input": "The quick brown fox jumped over the lazy dog.",
    "voice": "af_bella",
    "response_format": "mp3"
  }' \
  --output speech.mp3
```

The API returns the generated audio file in the requested format (e.g., `mp3`, `pcm`). The example above saves the audio output directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Best for lowest latency streaming*        |
| mp3    | Compressed audio suitable for storage | General use and file distribution          |
| opus   | Compressed audio ideal for streaming  | Efficient streaming and real-time playback |
| flac   | Lossless audio compression            | High-quality archival storage              |
| wav    | Uncompressed audio                    | High-quality audio processing tasks        |

### Performance Recommendations

- **For lowest latency (fastest initial audio chunk):**
  - Use the `pcm` output format.

- **For general purposes:**
  - Use the `mp3` or `opus` formats for optimal balance between quality and file size.



The DeepInfra ElevenLabs-compatible Speech API endpoint allows users to seamlessly convert text inputs into high-quality speech audio. This document provides guidance on integrating and using this endpoint to generate realistic audio files efficiently.

Use the following examples of `py` request to generate an audio file from your text input:

## Create Speech (non-streaming)

```bash
from elevenlabs import ElevenLabs

client = ElevenLabs(
    api_key="$DEEPINFRA_TOKEN",
    base_url="https://api.deepinfra.com/",
)
client.text_to_speech.convert(
    voice_id="af_bella",
    output_format="mp3",
    text="The quick brown fox jumped over the lazy dog.",
    model_id="hexgrad/Kokoro-82M",
)
```

## Create Speech with Streaming

```bash
from elevenlabs import ElevenLabs

client = ElevenLabs(
    api_key="$DEEPINFRA_TOKEN",
)
client.text_to_speech.convert_as_stream(
    voice_id="af_bella",
    output_format="pcm",
    text="The quick brown fox jumped over the lazy dog.",
    model_id="hexgrad/Kokoro-82M",
)
```

The API returns the generated audio in the requested format, such as `mp3` or `pcm`. The example above saves the audio directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Lowest latency streaming scenarios*       |
| mp3    | Compressed audio suitable for storage | General use and easy file sharing          |
| opus   | Compressed audio ideal for streaming  | Real-time audio streaming applications     |
| flac   | Lossless audio compression            | Archival storage and high-fidelity needs   |
| wav    | Uncompressed audio                    | Audio processing and editing tasks         |

### Performance Recommendations

- **Lowest latency:** Using the `pcm` output format for streaming applications is **HIGHLY RECOMMENDED**.
- **General use:** Use `mp3` or `opus` formats for the best trade-off between quality and file size.



The DeepInfra ElevenLabs-compatible Speech API endpoint allows users to seamlessly convert text inputs into high-quality speech audio. This document provides guidance on integrating and using this endpoint to generate realistic audio files efficiently.

Use the following examples of `js` request to generate an audio file from your text input:

## Create Speech (non-streaming)

```bash
import { ElevenLabsClient } from "elevenlabs";

const client = new ElevenLabsClient({ apiKey: "$DEEPINFRA_TOKEN", base_url: "https://api.deepinfra.com/" });
await client.textToSpeech.convert("af_bella", {
    output_format: "mp3",
    text: "The quick brown fox jumped over the lazy dog.",
    model_id: "hexgrad/Kokoro-82M"
});
```

## Create Speech with Streaming

```bash
import { ElevenLabsClient } from "elevenlabs";

const client = new ElevenLabsClient({ apiKey: "$DEEPINFRA_TOKEN" });
await client.textToSpeech.convert("af_bella", {
    output_format: "pcm",
    text: "The quick brown fox jumped over the lazy dog.",
    model_id: "hexgrad/Kokoro-82M"
});
```

The API returns the generated audio in the requested format, such as `mp3` or `pcm`. The example above saves the audio directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Lowest latency streaming scenarios*       |
| mp3    | Compressed audio suitable for storage | General use and easy file sharing          |
| opus   | Compressed audio ideal for streaming  | Real-time audio streaming applications     |
| flac   | Lossless audio compression            | Archival storage and high-fidelity needs   |
| wav    | Uncompressed audio                    | Audio processing and editing tasks         |

### Performance Recommendations

- **Lowest latency:** Using the `pcm` output format for streaming applications is **HIGHLY RECOMMENDED**.
- **General use:** Use `mp3` or `opus` formats for the best trade-off between quality and file size.



The DeepInfra ElevenLabs-compatible Speech API endpoint allows users to seamlessly convert text inputs into high-quality speech audio. This document provides guidance on integrating and using this endpoint to generate realistic audio files efficiently.

Use the following examples of `curl` request to generate an audio file from your text input:

## Create Speech (non-streaming)

```bash
curl -X POST "https://api.deepinfra.com/v1/text-to-speech/af_bella" \
     -H "xi-api-key: $DEEPINFRA_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
  "text": "The quick brown fox jumped over the lazy dog.",
  "model_id": "hexgrad/Kokoro-82M",
  "output_format": "mp3",
}' --output speech.mp3
```

## Create Speech with Streaming

```bash
curl -X POST "https://api.deepinfra.com/v1/text-to-speech/af_bella/stream" \
     -H "xi-api-key: $DEEPINFRA_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
  "text": "The quick brown fox jumped over the lazy dog.",
  "model_id": "hexgrad/Kokoro-82M",
  "output_format": "pcm",
}' --output speech.pcm
```

The API returns the generated audio in the requested format, such as `mp3` or `pcm`. The example above saves the audio directly as `speech.mp3`.

### Supported Audio Formats

| Format | Description                           | Recommended Usage                          |
|--------|---------------------------------------|--------------------------------------------|
| pcm    | Raw, uncompressed audio               | *Lowest latency streaming scenarios*       |
| mp3    | Compressed audio suitable for storage | General use and easy file sharing          |
| opus   | Compressed audio ideal for streaming  | Real-time audio streaming applications     |
| flac   | Lossless audio compression            | Archival storage and high-fidelity needs   |
| wav    | Uncompressed audio                    | Audio processing and editing tasks         |

### Performance Recommendations

- **Lowest latency:** Using the `pcm` output format for streaming applications is **HIGHLY RECOMMENDED**.
- **General use:** Use `mp3` or `opus` formats for the best trade-off between quality and file size.



text

output_format

preset_voice

speed

stream

return_timestamps

sample_rate

target_min_tokens

target_max_tokens

Absolute maximum number of tokens for the output.

absolute_max_tokens

The webhook to call when inference is done, by default you will get the output in the response of your inference request

webhook

Select the desired voice for the speech output. You can select multiple to combine and mix voices.

KokoroTtsVoice

Select the desired format for the speech output. Supported formats include mp3, opus, flac, wav, and pcm.

hexgrad/Kokoro-82M

HTTP/cURL API

Input fields

`text`string

`output_format`string

`preset_voice`array

`speed`number

`stream`boolean

`return_timestamps`boolean

`sample_rate`integer

`target_min_tokens`integer

`target_max_tokens`integer

`absolute_max_tokens`integer

`webhook`file

Input Schema

Output Schema

hexgrad/Kokoro-82M

HTTP/cURL API

Input fields

textstring

output_formatstring

preset_voicearray

speednumber

streamboolean

return_timestampsboolean

sample_rateinteger

target_min_tokensinteger

target_max_tokensinteger

absolute_max_tokensinteger

webhookfile