phi-4 vs Bonsai Image Ternary 4B

At a Glance

Compare
phi-4Microsoft
Pricing and Limits
Input priceFrom · USD / 1M tokens$0.070Deepinfra · Sep 22, 2026Not reported
Output priceFrom · USD / 1M tokens$0.14Deepinfra · Sep 22, 2026Not reported
Context windowMaximum documented tokens16KNot reported
Model facts checkedAug 28, 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 →

Different Model RolesThese models do not share a sourced market category. Their primary-source facts remain comparable below, while performance claims require matched evidence.

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

Fieldphi-4Bonsai Image Ternary 4B
DeveloperMicrosoftPrismML
FamilyPhi 4Bonsai Image 4b
Modelphi-4Bonsai Image Ternary 4B
Versionphi-4Bonsai Image Ternary 4B
Lifecycleactiveactive
Released2024-12-122026-05-21
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextImage
Context window16KUnknown
Total parameters14.7B4B
Active parametersUnknownUnknown
Licensemitapache-2.0
Open weightsYesYes
API availableYesNo
Self-hostableYesYes
Provider accessDeepinfra (Standard), Hugging Face (Standard), Openrouter (Standard)Unknown
Capabilitieschat, generationgeneration
Base modelUnknownFLUX.2 Klein 4B
Default resolutionUnknown512 × 512
Transformer sizeUnknown1.21 GB
Weight formatUnknownTernary weights with FP16 group scales

phi-4 Capabilities

chatgeneration
Serving providers3
Canonical IDmicrosoft/phi-4

Bonsai Image Ternary 4B Capabilities

generation
Serving providers0
Canonical IDprism-ml/Bonsai-Image-Ternary-4B

Primary Evidence

Sources and Freshness

Questions

phi-4 vs Bonsai Image Ternary 4B FAQs

Is phi-4 or Bonsai Image Ternary 4B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both phi-4 and Bonsai Image Ternary 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, phi-4 or Bonsai Image Ternary 4B?+

Only phi-4 has a directly sourced input price: $0.070 per million tokens. Only phi-4 has a directly sourced output price: $0.14 per million tokens.

Which has a larger context window, phi-4 or Bonsai Image Ternary 4B?+

Neither model has a larger sourced context window in this comparison. phi-4 is 16K and Bonsai Image Ternary 4B is —.

Which performs better in benchmarks, phi-4 or Bonsai Image Ternary 4B?+

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

Can phi-4 or Bonsai Image Ternary 4B be self-hosted?+

Both models have the same recorded self-hosting status: supported. phi-4 is open weight; Bonsai Image Ternary 4B is open weight.

Can phi-4 and Bonsai Image Ternary 4B understand images?+

phi-4 is not documented with image input; Bonsai Image Ternary 4B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, phi-4 or Bonsai Image Ternary 4B?+

Neither has a larger sourced maximum output. phi-4 is — and Bonsai Image Ternary 4B is —.

Do phi-4 and Bonsai Image Ternary 4B support reasoning and tool use?+

phi-4: none of these features are definitively sourced. Bonsai Image Ternary 4B: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, phi-4 or Bonsai Image Ternary 4B?+

phi-4 has 3 sourced provider routes; Bonsai Image Ternary 4B has 0, so phi-4 has broader tracked availability.

Which offers better value, phi-4 or Bonsai Image Ternary 4B?+

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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