Migrating from Nova-3 to Flux

Key Benefits of Flux

• Model-integrated turn detection (StartOfTurn, EagerEndOfTurn, TurnResumed, EndOfTurn)
• Ultra-low latency ~260ms end-of-turn detection (p50 at defaults)
• EagerEndOfTurn events let you start LLM responses early
• Turn-based transcripts for clean agent logic
• Same Nova 3 transcription quality
• Simplified development one API replaces complex STT+VAD+endpointing pipelines, and conversation-native events.
• High configurability - Configurable end-of-turn detection sensitivity, eager response thresholds, and turn-taking dynamics for optimized conversational flow

Audio Requirements

• Encoding: linear16 (16-bit signed little-endian PCM)
• Sample rates: 8000, 16000, 24000, 44100, 48000
• Channels: Mono only
• Chunk size: ~80 ms recommended

Migrating from Nova 3 to Flux

This guide will help you migrate from Nova 3 to Flux by highlighting key differences, setup changes, and implementation patterns.

Differences

Endpoint Usage

Nova 3:

Uses the listen v1 endpoint with the nova-3 model option.

wss://api.deepgram.com/v1/listen?model=nova-3

Flux:

Uses the listen v2 endpoint with the flux-general-en model option.

wss://api.deepgram.com/v2/listen?model=flux-general-en

Response Message Structure

Nova 3

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{
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  "type": "Results",
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  "channel": "transcript",
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  "alternatives": [...]
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}

Flux

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{
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  "type": "TurnInfo",
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  "request_id": "2ba892a1-6c0d-4d92-9b89-0000000000",
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  "event": "Update",
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  "turn_index": 0,
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  "audio_window_start": 0,
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  "audio_window_end": 0.47999996,
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  "transcript": "",
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  "words": [...],
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  "end_of_turn_confidence": 0.0009,
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  "sequence_id": 2
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}

In addition to the transcript, flux responses include the:

• event field for turn-state changes
• turn_index to track turn lifecycle
• audio_window_start and audio_window_end to track the audio window.
• end_of_turn_confidence to track the confidence of the end of turn.
• sequence_id to track the sequence id of the messages.

Implementation Pattern Changes

Nova 3 Approach

Requires custom logic for barge-in and turn-taking.

• Send audio
• Receive streaming partial transcripts
• Decide when to interrupt your agent manually

Flux Approach

Listens for structured events and removes the need for custom VAD or barge-in logic.

• StartOfTurn: Interrupt agent if it’s speaking
• EagerEndOfTurn: Medium-confidence end → start LLM reply
• TurnResumed: User kept talking → cancel reply
• EndOfTurn: High-confidence end → send transcript to LLM

By default, Flux only emits Update, StartOfTurn, and EndOfTurn.

Simple Approach: Enabling End of Turn

This is a simple approach using only EndOfTurn (lower latency, less complex, less LLM calls).

To enable end of turn use the eot_threshold parameter which allows for a confidence of (0.5–0.9) for EndOfTurn events.

Example

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wss://api.deepgram.com/v2/listen?model=flux-general-en&sample_rate=16000&encoding=linear16&eot_threshold=0.8

Optimized Approach: Enabling EagerEndOfTurn + EndOfTurn

This is an optimized approach using both EagerEndOfTurn and EndOfTurn (lower latency, slightly more complex, more LLM calls)

To enable eager end of turn use the eager_eot_threshold parameter which allows for a Confidence of (0.3–0.9). You can also set the eot_threshold with a confidence of (0.5–0.9) to handle EndOfTurn events and use the eot_timeout_ms which defaults to 5000 ms to force a timeout after a specified time.

Example

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wss://api.deepgram.com/v2/listen?model=flux-general-en&sample_rate=16000&encoding=linear16&eager_eot_threshold=0.6&eot_threshold=0.8&eot_timeout_ms=7000

Nova 3 Migration Checklist

• Update WebSocket endpoint to /v2/listen
• Set model=flux-general-en and encoding=linear16
• Adjust client to parse TurnInfo messages
• Implement turn event handling (start, eager end of turn, turn resumed, end)
• Tune eager_eot_threshold and eot_threshold for your use case
• Remove custom VAD/barge-in logic (Flux handles this natively!)