app.nzapp
AppsProjectsReposPullsChatIntegrationsGatewayModelsEvalsToolsDatasetsMCPDeploysPricingBlogDocsAssistantsCharactersArtMusic
Sign inStart building
Agent stack
Cloud coding agentAgents SDKIntegrationsBrowser agentMonitors & auto-agentsSchedulersAgent skillsMCP serversDeep research
Models & API
AI GatewayModel catalogModel evalsModel spacesPlaygroundText to imageImage to 3DText to 3DMusic & SFXAudio editorMedia optimizerAI art & libraryChatAPI referenceSchemaBecome a provider
Compute & hosting
DeploysAddonsPostgres hostinggobed vector searchSite hostingAnalyticsCog GPU hostingRL trainingBuilds & CIWorkersTask queuesDomainsGit hosting
Tools
AI toolsDrawDiffusion canvasLive DrawWriteSheetsArtifactsVideo studioNotebooksDatasets
Learn
DocsBlogEval guidesPrompt libraryCLIAlternativesPapersAI charactersArt gallerySecurityConsulting
Company
PricingEnterpriseSettingsBillingStatusInvestorsCreate accountTerms of ServicePrivacy Policy
app.nzapp.nz

An agent that ships images, video, and live sites on app.nz.

Built in New Zealand by App AI NZ.

Social
X / TwitterGitHubYouTube
The app.nz network
GpuBrainPapersReading TimemojojojoNetwrckText-Generator.ioCodex InfinityOpenPathsCuteDSLAI Art GeneratorAIArt-Generator.artSiteSimSimplexGenDictatorFlowWebFiddleRing.nzChatGibidyBitBankExperimentFlowEvangelerHires.nzHow.nzV5 GamesAddicting Word GamesBig Multiplayer ChessWord SmashingreWord GameMultiplication Master
© 2026 App AI NZ Ltd. All rights reserved.All systems normalTermsPrivacy
Blog
August 26, 2026·8 min read·app.nz

MiniMax Music: BF16 vs FP8, then Opus vs AAC vs MP3

Six fixed-seed Music3 renders, ten controlled codec encodes, open Cog source, and the audio players to judge them yourself.

Listen to this article

On-device voice

Uses the voice built into your browser; no article text leaves this page.

Audio narration is not supported by this browser.

MiniMax Music 3 BF16 and FP8 listening study with codec ladder
MiniMax Music 3 BF16 and FP8 listening study with codec ladder

We open-sourced the production wrapper at github.com/lee101/minimax-music. It is a typed Cog with fixed seeds, 10–300 second bounds, scale-to-zero GPU deployment, and WAV, FLAC, Opus, or MP3 delivery. The MIT repository contains the wrapper and experiment tools—not the MiniMax weights, which keep their own model terms.

Deploy MiniMax Music on app.nz, or inspect the full machine-readable study manifest.

First, a correction: this is BF16 vs FP8

The serving stack supports a BF16 autoregressive backbone or an FP8-quantized backbone. Its acoustic DiT and DAV decoder remain BF16 in both profiles. There is no true INT8 Music3 path in the pinned backend, so calling the FP8 output “INT8” would be wrong.

We deployed two immutable RunPod templates on NVIDIA H200, fixed the model, backend commit, prompt, structured lyrics, duration cap, and seed, then rendered three cases through both profiles. A seed controls sampling within one profile; quantization changes the sampling path, so BF16 and FP8 are different takes—not sample-aligned waveforms.

Across these three runs, median generation time per output second fell from 0.380 with BF16 to 0.345 with FP8, about 9.2%. Wall time is deliberately not the headline: cold queue and model-load time dominated several runs.

Listen: Neon Tide

Dreamy 108 BPM synth-pop with an intimate lead and wide chorus. Seed 2026082601; both outputs reached 30.023 seconds.

Neon Tide · BF1611.454s generation · 128k Opus listening copy
Neon Tide · FP810.402s generation · 128k Opus listening copy

BF16 WAV master · FP8 WAV master

Listen: Dust and Wire

Cinematic desert blues at 92 BPM with tremolo guitar, cello, brushes, and a weathered lead. Seed 2026082602; both outputs reached 30.023 seconds.

Dust and Wire · BF1611.123s generation · 128k Opus listening copy
Dust and Wire · FP810.261s generation · 128k Opus listening copy

BF16 WAV master · FP8 WAV master

Listen: After Rain

Instrumental modern jazz trio at 124 BPM. Seed 2026082603. The model ended the BF16 take at 23.499 seconds and the FP8 take at 29.188 seconds, despite the shared 30-second cap. That is a real result worth preserving, not trimming away.

After Rain · BF160.380 realtime factor · 128k Opus listening copy
After Rain · FP80.345 realtime factor · 128k Opus listening copy

BF16 WAV master · FP8 WAV master

Codec listening ladder

Every file below comes from the exact same BF16 Neon Tide WAV master. There is no regeneration and no loudness normalization between encodes, so the only planned variable is the delivery codec and target bitrate. FLAC is the lossless anchor. File size is for this 30.023-second stereo track.

FLAC · lossless anchor2,096,110 bytes

Opus

Opus · 48 kbps VBR207,459 bytes
Opus · 96 kbps VBR392,844 bytes
Opus · 128 kbps VBR515,229 bytes

AAC-LC

AAC-LC · 96 kbps365,516 bytes
AAC-LC · 128 kbps485,903 bytes
AAC-LC · 192 kbps726,480 bytes

MP3

MP3 · 128 kbps481,580 bytes
MP3 · 192 kbps722,348 bytes
MP3 · 320 kbps1,203,884 bytes

What we can claim

  • FP8 reduced median generation seconds per output second by about 9.2% in this

three-case H200 run.

  • All six masters are 32 kHz stereo PCM16 and reported zero clipped samples.
  • The codec ladder is controlled from one PCM master, with hashes, FFprobe

records, encoder settings, byte sizes, and EBU R128 scans in the manifest.

This is not an ABX study and three prompts do not establish universal audio quality. Listen on the same device at the same volume, hide the labels if you want a blind trial, and treat preference as preference.

The practical default remains FP8 for the production backbone and 128k Opus for browser listening, while archival outputs stay WAV. The open Cog keeps BF16 as a separate deployment profile for teams that want to run their own evaluation.

Build what you just read

Ship agents, models, and apps on one cloud.

Start with free credits, then use the same platform from the web app, CLI, desktop app, or MCP.

Start building freeRead the docs

Keep reading

From ComfyUI to any Cog: open audio workflows that scale to zero

How template:name connects ComfyUI to reusable Cog deployments, when to choose open Pocket TTS versus fal audio, and how the shared serverless/pod router returns idle workers to zero.

Packaging MiniMax H3 for Cog, R2, RTX 5090, and scale to zero

A careful H3 video workflow with text, keyframes, native audio, true loops, verified R2 weights, GPU AV1, serverless pricing, and a fail-closed license gate.

Train three open piano models, then deploy one Cog anywhere

A reproducible CC0 symbolic training pipeline, listening previews, one inference image for Cog and RunPod Serverless, and one-click scale-to-zero deployment on app.nz.