VibeVoice ASR Streaming 1.5B vs Bonsai 1.7B

At a Glance

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Pricing and Limits
Context windowMaximum documented tokensNot reported33K
Model facts checkedSep 2, 2026View model evidence →Sep 18, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldVibeVoice-ASR-Streaming-1.5BBonsai 1.7B
DeveloperMicrosoftPrismML
FamilyVibevoice ASR StreamingBonsai 1 7b
ModelVibeVoice-ASR-Streaming-1.5BBonsai 1.7B
VersionVibeVoice-ASR-Streaming-1.5BBonsai 1.7B
Lifecycleactiveactive
Released2026-09-032026-03-29
Knowledge cutoffUnknownUnknown
Input modalitiesAudioText
Output modalitiesTextText
Context windowUnknown33K
Total parameters2.8B1.7B
Active parametersUnknownUnknown
Licensemitapache-2.0
Open weightsYesYes
API availableNoNo
Self-hostableYesYes
Provider accessUnknownUnknown
Capabilitiesdiarization, hotwords, multilingual, streaming, transcriptionchat, generation
Streaming chunk22 framesUnknown
Effective bit widthUnknown1 bit per weight
Streaming lookahead4 framesUnknown
Supported languagesChinese, English, French, German, Italian, Japanese, Korean, Portuguese, Russian, SpanishUnknown
Target sample rate24000 HzUnknown
Weight sizeUnknown0.25 GB
Weight formatUnknownBinary Q1_0

VibeVoice ASR Streaming 1.5B Capabilities

diarizationhotwordsmultilingualstreamingtranscription
Serving providers0
Canonical IDmicrosoft/VibeVoice-ASR-Streaming-1.5B

Bonsai 1.7B Capabilities

chatgeneration
Serving providers0
Canonical IDprism-ml/Bonsai-1.7B

Primary Evidence

Sources and Freshness

Questions

VibeVoice ASR Streaming 1.5B vs Bonsai 1.7B FAQs

Is VibeVoice ASR Streaming 1.5B or Bonsai 1.7B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both VibeVoice ASR Streaming 1.5B and Bonsai 1.7B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, VibeVoice ASR Streaming 1.5B or Bonsai 1.7B?+

Neither model has a directly sourced input price in this comparison. Neither model has a directly sourced output price in this comparison.

Which has a larger context window, VibeVoice ASR Streaming 1.5B or Bonsai 1.7B?+

Neither model has a larger sourced context window in this comparison. VibeVoice ASR Streaming 1.5B is — and Bonsai 1.7B is 33K.

Which performs better in benchmarks, VibeVoice ASR Streaming 1.5B or Bonsai 1.7B?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can VibeVoice ASR Streaming 1.5B or Bonsai 1.7B be self-hosted?+

Both models have the same recorded self-hosting status: supported. VibeVoice ASR Streaming 1.5B is open weight; Bonsai 1.7B is open weight.

Can VibeVoice ASR Streaming 1.5B and Bonsai 1.7B understand images?+

VibeVoice ASR Streaming 1.5B is not documented with image input; Bonsai 1.7B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, VibeVoice ASR Streaming 1.5B or Bonsai 1.7B?+

Neither has a larger sourced maximum output. VibeVoice ASR Streaming 1.5B is — and Bonsai 1.7B is —.

Do VibeVoice ASR Streaming 1.5B and Bonsai 1.7B support reasoning and tool use?+

VibeVoice ASR Streaming 1.5B: none of these features are definitively sourced. Bonsai 1.7B: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, VibeVoice ASR Streaming 1.5B or Bonsai 1.7B?+

VibeVoice ASR Streaming 1.5B has 0 sourced provider routes; Bonsai 1.7B has 0, a tie.

Which offers better value, VibeVoice ASR Streaming 1.5B or Bonsai 1.7B?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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