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Model infoModelsSnapshot: 15 Oct 2026

Tavus / Phoenix

Phoenix-4

Human-rendering model with expressive listening and speaking behavior, supporting image and video face training.

availableClosed weightsVideo generationAudio-driven video
Official documentation (opens in a new tab)Reviewed 14 Oct 2026
8 of 18 specification fields collected
Output resolution1080pSee specification notes below
Clip durationNot collectedAwaiting source review
Input billingNot collectedTavus CVI
Output billingNot collectedNo inferred price

Specifications

Provider-documented facts. “Not collected” means we have not verified that field yet, not that the feature is unsupported.

Context / input limit
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Maximum output
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Context caching
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Reasoning
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Tool calling
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Structured output
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Batch processing
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Fine-tuning
SupportedSource for Fine-tuning (opens in a new tab)

Per-identity face refinement; not a public general-purpose weight fine-tuning endpoint.

Parameters
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Architecture
Streaming audio encoder, long-term memory, diffusion motion head and 3D Gaussian renderingSource for Architecture (opens in a new tab)
Knowledge cutoff
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Languages
Not collected

Exact model-version information has not been established; unknown does not mean unsupported.

Input modalities
Image, Video, AudioSource for Input modalities (opens in a new tab)

Image/video establishes identity; supplied audio drives animation.

Output modalities
VideoSource for Output modalities (opens in a new tab)

Speech synthesis is a separate pipeline stage, not proof that Phoenix is a native TTS model.

Frame rate
40Source for Frame rate (opens in a new tab)

Provider-reported 1080p rendering throughput; actual session performance is not guaranteed by this research result.

Access & pricing

Prices belong to an endpoint and billing tier. A provider listing does not guarantee access for every account or region.

API model ID
phoenix-4
Access
POST https://tavusapi.com/v2/faces with model_name=phoenix-4; use the resulting face in the conversational pipeline.
Regions
Privacy policy describes US processing and possible transfers to service providers in other countries. Exact model-serving location is not established.
trainingVideo
30 seconds speaking followed by 30 seconds listening in one continuous recording; minimum 25 fps and 1080p, maximum 750 MB; MP4 or WebM with H.264 video and AAC audio.Source for trainingVideo (opens in a new tab)

Training source requirements, not generated output limits.

trainingAccess
Supply one public/presigned training URL accessible for at least 24 hours after submission. Image training requires a stock voice_name or default_voice_id, not both. Video training derives a voice from the recording.Source for trainingAccess (opens in a new tab)
trainingPaths
Image or video; face becomes usable after training, typically 3–4 hours. Head-to-toe full-body use requires an appropriate video.Source for trainingPaths (opens in a new tab)

Endpoint pricing has not been collected. Check the provider for applicable rates.

Data & enterprise terms

Default privacy terms permit service improvement and AI research/training. Biometric retention is limited by the stated purpose or one year after last interaction, whichever comes first. Enterprise zero-retention arrangements are separate; no universal zero-retention promise is inferred.

Read the service policy (opens in a new tab)

Caching

Caching can reuse earlier input processing. Availability, lifetime and billing depend on the endpoint. A cache hit still generates a new response.

01 / INITIAL INPUTProcess & retain

The system processes eligible input and retains reusable state.

02 / LATER REQUESTCheck available state

Reuse depends on matching input, session state and the cache policy.

03 / CACHE HITReuse & generate

Reusing state can reduce repeated work. Any charges follow the billing rules below.

Tavus CVI

phoenix-4
Not verified

Internal streaming state, saved faces and conversation memories do not establish billable prompt caching, cache lifetime or discounts.

How it works
Not collected

Internal streaming state, saved faces and conversation memories do not establish billable prompt caching, cache lifetime or discounts.

Minimum reusable input
Not collected

Internal streaming state, saved faces and conversation memories do not establish billable prompt caching, cache lifetime or discounts.

Lifetime & refresh
Not collected

Internal streaming state, saved faces and conversation memories do not establish billable prompt caching, cache lifetime or discounts.

How to enable it
Not collected

Internal streaming state, saved faces and conversation memories do not establish billable prompt caching, cache lifetime or discounts.

Usage fields
Not collected

Internal streaming state, saved faces and conversation memories do not establish billable prompt caching, cache lifetime or discounts.

Billing rules
Not collected

Internal streaming state, saved faces and conversation memories do not establish billable prompt caching, cache lifetime or discounts.

Limits & data boundaries
Not collected

Internal streaming state, saved faces and conversation memories do not establish billable prompt caching, cache lifetime or discounts.

No verified cache rate for this endpoint. An empty rate is not a zero charge.

Caching reviewed 14 Oct 2026

What the measurements say

AVSR

Tavus · provider-reported
0.261score

Same paired internal test set; both models fine-tuned. Provider evaluation, not an independent cross-provider ranking. AVSR/CSIM higher is better; FID/FVD lower is better; ΔP describes motion vividness.

Evaluation source (opens in a new tab)

CSIM

Tavus · provider-reported
0.914score

Same paired internal test set; both models fine-tuned. Provider evaluation, not an independent cross-provider ranking. AVSR/CSIM higher is better; FID/FVD lower is better; ΔP describes motion vividness.

Evaluation source (opens in a new tab)

Motion vividness ΔP

Tavus · provider-reported
0.464score

Same paired internal test set; both models fine-tuned. Provider evaluation, not an independent cross-provider ranking. AVSR/CSIM higher is better; FID/FVD lower is better; ΔP describes motion vividness.

Evaluation source (opens in a new tab)

FID

Tavus · provider-reported
28.36score

Same paired internal test set; both models fine-tuned. Provider evaluation, not an independent cross-provider ranking. AVSR/CSIM higher is better; FID/FVD lower is better; ΔP describes motion vividness.

Evaluation source (opens in a new tab)

FVD

Tavus · provider-reported
160.81score

Same paired internal test set; both models fine-tuned. Provider evaluation, not an independent cross-provider ranking. AVSR/CSIM higher is better; FID/FVD lower is better; ΔP describes motion vividness.

Evaluation source (opens in a new tab)

Human preference Elo · fine-tuned

Tavus · provider-reported
929Elo

Provider head-to-head naturalness comparisons; evaluator count and calibration not disclosed in the reviewed article.

Evaluation source (opens in a new tab)

Latency and throughput depend on the host, request size, reasoning settings and load. There is no measured speed or global usage figure in this record.

The record behind the model

Why it is included

Named model developed by Tavus in its proprietary hosted/research stack. Reviewed primary documentation provides no downloadable weights for this model; service terms restrict source derivation and independent avatar use. Open SDKs and third-party models in the surrounding pipeline are separate.

Classification evidence (opens in a new tab)

Lifecycle dates

Named release announced18 Feb 2026Source (opens in a new tab)

Sources reviewed