ERNIE 4.5 0.3B PT vs Bonsai Image Binary 4B

At a Glance

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Pricing and Limits
Context windowMaximum documented tokens131KNot 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

FieldERNIE-4.5-0.3B-PTBonsai Image Binary 4B
DeveloperBaiduPrismML
FamilyErnie 4 5 0 3b PtBonsai Image 4b
ModelERNIE-4.5-0.3B-PTBonsai Image Binary 4B
VersionERNIE-4.5-0.3B-PTBonsai Image Binary 4B
Lifecycleactiveactive
ReleasedUnknown2026-05-18
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextImage
Context window131KUnknown
Total parameters360.7M4B
Active parametersUnknownUnknown
Licenseapache-2.0apache-2.0
Open weightsYesYes
API availableUnknownNo
Self-hostableYesYes
Provider accessUnknownUnknown
Capabilitieschat, generationgeneration
Base modelUnknownFLUX.2 Klein 4B
Default resolutionUnknown512 × 512
Transformer sizeUnknown0.93 GB
Weight formatUnknownBinary weights with FP16 group scales

ERNIE 4.5 0.3B PT Capabilities

chatgeneration
Serving providers0
Canonical IDbaidu/ERNIE-4.5-0.3B-PT

Bonsai Image Binary 4B Capabilities

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

Primary Evidence

Sources and Freshness

Questions

ERNIE 4.5 0.3B PT vs Bonsai Image Binary 4B FAQs

Is ERNIE 4.5 0.3B PT or Bonsai Image Binary 4B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both ERNIE 4.5 0.3B PT and Bonsai Image Binary 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, ERNIE 4.5 0.3B PT or Bonsai Image Binary 4B?+

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, ERNIE 4.5 0.3B PT or Bonsai Image Binary 4B?+

Neither model has a larger sourced context window in this comparison. ERNIE 4.5 0.3B PT is 131K and Bonsai Image Binary 4B is —.

Which performs better in benchmarks, ERNIE 4.5 0.3B PT or Bonsai Image Binary 4B?+

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

Can ERNIE 4.5 0.3B PT or Bonsai Image Binary 4B be self-hosted?+

Both models have the same recorded self-hosting status: supported. ERNIE 4.5 0.3B PT is open weight; Bonsai Image Binary 4B is open weight.

Can ERNIE 4.5 0.3B PT and Bonsai Image Binary 4B understand images?+

ERNIE 4.5 0.3B PT is not documented with image input; Bonsai Image Binary 4B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, ERNIE 4.5 0.3B PT or Bonsai Image Binary 4B?+

Neither has a larger sourced maximum output. ERNIE 4.5 0.3B PT is — and Bonsai Image Binary 4B is —.

Do ERNIE 4.5 0.3B PT and Bonsai Image Binary 4B support reasoning and tool use?+

ERNIE 4.5 0.3B PT: none of these features are definitively sourced. Bonsai Image Binary 4B: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, ERNIE 4.5 0.3B PT or Bonsai Image Binary 4B?+

ERNIE 4.5 0.3B PT has 0 sourced provider routes; Bonsai Image Binary 4B has 0, a tie.

Which offers better value, ERNIE 4.5 0.3B PT or Bonsai Image Binary 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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