What Are Deepgram's Flux STT and Flux TTS Models?

Updated

Flux is Deepgram's conversation-aware speech platform: Flux STT is speech-to-text built to listen for turn-taking and interruptions in live dialogue, and Flux TTS is text-to-speech built to hold tone, expression, and continuity across a conversation. Both are positioned for voice agents and are available in real time or batch, in the cloud or self-hosted. They differ from standard STT/TTS in that they are designed around the back-and-forth of a conversation rather than one-shot transcription or narration.

Flux STT: listening for the conversation, not just the words

Standard speech-to-text turns audio into a transcript. Flux STT is described as conversational speech recognition that detects end-of-turn and interruptions and streams transcripts in real time. That matters for a voice agent because the agent needs to know when the user has finished speaking before it responds, and it needs to cope with being talked over.

The practical effect: instead of building your own silence detection and barge-in logic on top of a generic transcript stream, you get signals designed for turn-taking from the recognition layer itself.

Flux TTS: speaking with continuity

Flux TTS is described as the first voice model that "thinks before it speaks," holding tone, expression, and continuity across an entire conversation. Standard TTS typically synthesizes each response independently, so tone can drift between turns. Flux TTS is aimed at keeping the voice consistent as the dialogue progresses.

Deepgram lists latency as low as 80ms for its text-to-speech offering, and positions Flux TTS for live conversations rather than pre-recorded narration.

Where Flux fits in the voice agent stack

Deepgram describes a single, unified Voice Agent API that combines speech-to-text, text-to-speech, and LLM orchestration, rather than stitching separate components together. The flow is:

  1. Audio input — a partner-provided transport layer (including telephony partners via PSTN/SIP) bridges streamed audio into Deepgram's APIs and plays TTS audio back to the user.
  2. Flux STT — detects end-of-turn and interruptions and streams transcripts in real time.
  3. LLM orchestration — manages context and memory, system and prompt updates, response injections, and function calling, connecting to language models via API.
  4. Flux TTS — converts the AI response into natural-sounding speech.
  5. Business logic and external systems — your application logic executes domain-specific actions and integrates external APIs, with results fed back into the conversation context.

Availability and access

  • Modes: real time or batch.
  • Deployment: cloud or self-hosted.
  • Flux TTS free period: build with Flux TTS free through September 12th, with up to 45 concurrent streaming connections in the US and 5 in the EU and AU.
  • Promotion: spend $1 on Flux TTS, get $1 back, up to $500.

Deepgram also offers a free trial and demo requests. Pricing beyond the promotion is not specified in the available material, so confirm current terms directly with Deepgram before committing to a production plan.

How to decide whether Flux is the right fit

Your need Flux is a good fit if… Consider standard STT/TTS if…
Live voice agent You need turn-taking and interruption handling built in You only transcribe recordings or generate standalone audio
Conversational tone Voice continuity across turns matters Each clip is independent
Architecture You want STT, TTS, and orchestration in one API You already have a stitched pipeline you're happy with
Deployment You need cloud or self-hosted, real time or batch Your workload is batch-only and simple

A concrete way to test the difference: build a short agent that lets a user interrupt mid-sentence. With standard STT you would typically add your own voice-activity detection and barge-in rules; with Flux STT, end-of-turn and interruption detection are part of the recognition layer, so you can check whether the agent stops speaking and responds appropriately without extra plumbing.