> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://developers.deepgram.com/docs/model/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://developers.deepgram.com/_mcp/server. # Model Options `model` *string* Default: `base-general` Pre-recorded Streaming:Nova Streaming:Flux Deepgram’s Model feature allows you to supply a model to use when processing submitted audio. To learn more about the pricing for our different models, see [Deepgram Pricing & Plans](https://deepgram.com/pricing/). ## Models & Model Options Below are a list of all model and model options that can be used with the Deepgram API. ### Flux **Examples** ``` https://api.deepgram.com/v2/listen?model=flux-general-en ``` ``` https://api.deepgram.com/v2/listen?model=flux-general-multi&language_hint=es ``` Flux is the first conversational speech recognition model built specifically for voice agents. Unlike traditional STT that just transcribes words, Flux understands conversational flow and automatically handles turn-taking. Flux tackles the most critical challenges for voice agents today: knowing when to listen, when to think, and when to speak. The model features first-of-its-kind model-integrated end-of-turn detection, configurable turn-taking dynamics, and ultra-low latency optimized for voice agent pipelines, all with Nova-3 level accuracy. Flux Multilingual (`flux-general-multi`) extends Flux to 10 languages with an optional `language_hint` parameter that biases output toward specified languages. See [Language Prompting](/docs/flux/language-prompting) for details. ### Nova-3 **Examples** ``` https://api.deepgram.com/v1/listen?model=nova-3 ``` Nova-3 represents a significant leap forward in speech AI technology, featuring substantial improvements in accuracy and real-world application capabilities. The model delivers industry-leading performance with a 53.4% reduction in word error rate (WER) for streaming and 47.4% for batch processing compared to competitors. Nova-3 introduces groundbreaking features including real-time multilingual conversation transcription, enhanced comprehension of domain-specific terminology, and optional personal information redaction. Notably, it's the first voice AI model to offer self-serve customization, enabling instant vocabulary adaptation without model retraining. In multilingual testing, Nova-3 demonstrated superior performance across all seven tested languages, with particularly strong results showing up to 8:1 preference ratios in certain languages. > **Info** > > Nova-3 has the following model options which can be called by using the following syntax: `model=nova-3-{option}` * `general`: Optimized for everyday audio processing. * `medical`: Optimized for audio with medical oriented vocabulary. Available in English and multilingual (`language=multi`). * `pharma`: Optimized for audio with pharmaceutical vocabulary, such as drug names, dosages, and medication terminology. ### Nova-2 **Examples** **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=nova-2 ``` **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=nova-2-phonecall ``` Nova-2 expands on Nova-1's advancements with speech-specific optimizations to the underlying Transformer architecture, advanced data curation techniques, and a multi-stage training methodology. These changes yield reduced word error rate (WER) and enhancements to entity recognition (i.e. proper nouns, alphanumerics, etc.), punctuation, and capitalization. > **Info** > > Nova-2 has the following model options which can be called by using the following syntax: `model=nova-2-{option}` * `general`: Optimized for everyday audio processing. * `meeting`: Optimized for conference room settings, which include multiple speakers with a single microphone. * `phonecall`: Optimized for low-bandwidth audio phone calls. * `voicemail`: Optimized for low-bandwidth audio clips with a single speaker. Derived from the phonecall model. * `finance`: Optimized for multiple speakers with varying audio quality, such as might be found on a typical earnings call. Vocabulary is heavily finance oriented. * `conversationalai`: Optimized for use cases in which a human is talking to an automated bot, such as IVR, a voice assistant, or an automated kiosk. * `video`: Optimized for audio sourced from videos. * `medical`: Optimized for audio with medical oriented vocabulary. * `drivethru`: Optimized for audio sources from drivethrus. * `automotive`: Optimized for audio with automative oriented vocabulary. * `atc`: Optimized for audio from air traffic control. ### Nova **Examples** **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=nova ``` **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=nova-phonecall ``` Nova is the predecessor to Nova-2. Training on this model spans over 100 domains and 47 billion tokens, making it the deepest-trained automatic speech recognition (ASR) model to date. Nova doesn't just excel in one specific domain — it is ideal for a wide array of voice applications that require high accuracy in diverse contexts. > **Info** > > Nova has the following model options which can be called by using the following syntax: `model=nova-{option}` * `general`: Optimized for everyday audio processing. Likely to be more accurate than any region-specific Base model for the language for which it is enabled. If you aren't sure which model to select, start here. * `phonecall`: Optimized for low-bandwidth audio phone calls. ### Enhanced **Examples** **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=enhanced ``` **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=enhanced-phonecall ``` Enhanced models are still some of our most powerful speech-to-text models; they generally have higher accuracy and better word recognition than our base models, and they handle uncommon words significantly better. > **Info** > > Enhanced has the following model options which can be called by using the following syntax: `model=enhanced-{option}` * `general`: Optimized for everyday audio processing. Likely to be more accurate than any region-specific Base model for the language for which it is enabled. If you aren't sure which model to select, start here. * `meeting` *beta*: Optimized for conference room settings, which include multiple speakers with a single microphone. * `phonecall`: Optimized for low-bandwidth audio phone calls. * `finance` *beta*: Optimized for multiple speakers with varying audio quality, such as might be found on a typical earnings call. Vocabulary is heavily finance oriented. The Enhanced models can be called with the following syntax: ### Base **Examples** **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=base ``` **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=base-phonecall ``` Base models are built on our signature end-to-end deep learning speech-to-text model architecture. They offer a solid combination of accuracy and cost effectiveness in some cases. > **Info** > > Base has the following model options which can be called by using the following syntax: `model=base-{option}` * `general`: (Default) Optimized for everyday audio processing. * `meeting`: Optimized for conference room settings, which include multiple speakers with a single microphone. * `phonecall`: Optimized for low-bandwidth audio phone calls. * `voicemail`: Optimized for low-bandwidth audio clips with a single speaker. Derived from the phonecall model. * `finance`: Optimized for multiple speakers with varying audio quality, such as might be found on a typical earnings call. Vocabulary is heavily finance oriented. * `conversationalai`: Optimized for use cases in which a human is talking to an automated bot, such as IVR, a voice assistant, or an automated kiosk. * `video`: Optimized for audio sourced from videos. ### Custom You may also use a custom, trained model associated with your account by including its `custom_id`. > **Info** > > Custom models are only available to Enterprise customers. See [Deepgram Pricing & Plans](https://deepgram.com/pricing/) for more details. ### Whisper **Examples** **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=whisper ``` **`Text`** ```text Text https://api.deepgram.com/v1/listen?model=whisper-SIZE ``` > **Warning** > > Whisper models are less scalable than all other Deepgram models due to their inherent model architecture. All non-Whisper models will return results faster and scale to higher load. Deepgram's Whisper Cloud is a fully managed API that gives you access to Deepgram's version of OpenAI’s Whisper model. Read our guide [Deepgram Whisper Cloud](/docs/deepgram-whisper-cloud) for a deeper dive into this offering. Deepgram's Whisper models have the following size options: * `tiny`: Contains 39 M parameters. The smallest model available. * `base`: Contains 74 M parameters. * `small`: Contains 244 M parameters. * `medium`: Contains 769 M parameters. The default model if you don't specify a size. * `large`: Contains 1550 M parameters. The largest model available. Defaults to OpenAI’s Whisper large-v2. > **Warning** > > Additional rate limits apply to Whisper due to poor scalability. Requests to Whisper are limited to 15 concurrent requests with a paid plan and 5 concurrent requests with the pay-as-you-go plan. Long audio files are supported up to a maximum of 20 minutes of processing time (the maximum length of the audio depends on the size of the Whisper model). ## Try it out To transcribe audio from a file on your computer using a particular model, run the following curl command in a terminal or your favorite API client. **`CURL`** ```bash CURL curl \ --request POST \ --header 'Authorization: Token YOUR_DEEPGRAM_API_KEY' \ --header 'Content-Type: audio/wav' \ --data-binary @youraudio.wav \ --url 'https://api.deepgram.com/v1/listen?model=OPTION' ``` > **Info** > > Replace `YOUR_DEEPGRAM_API_KEY` with your [Deepgram API Key](https://console.deepgram.com/signup?jump=keys). --- > Model options allows you to supply a model to use for speech-to-text.