Olmo 3.1 32B Instruct vs Bonsai Image Ternary 4B

At a Glance

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Pricing and Limits
Context windowMaximum documented tokens66KNot 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

FieldOlmo-3.1-32B-InstructBonsai Image Ternary 4B
DeveloperAi2PrismML
FamilyOlmo 3 1 32b InstructBonsai Image 4b
ModelOlmo-3.1-32B-InstructBonsai Image Ternary 4B
VersionOlmo-3.1-32B-InstructBonsai Image Ternary 4B
Lifecycleactiveactive
ReleasedUnknown2026-05-21
Knowledge cutoffUnknownUnknown
Input modalitiesTextText
Output modalitiesTextImage
Context window66KUnknown
Total parameters32.2B4B
Active parametersUnknownUnknown
Licenseapache-2.0apache-2.0
Open weightsYesYes
API availableUnknownNo
Self-hostableYesYes
Provider accessUnknownUnknown
Capabilitieschat, generation, toolsgeneration
Base modelUnknownFLUX.2 Klein 4B
Default resolutionUnknown512 × 512
Transformer sizeUnknown1.21 GB
Weight formatUnknownTernary weights with FP16 group scales

Olmo 3.1 32B Instruct Capabilities

chatgenerationtools
Serving providers0
Canonical IDallenai/Olmo-3.1-32B-Instruct

Bonsai Image Ternary 4B Capabilities

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

Primary Evidence

Sources and Freshness

Questions

Olmo 3.1 32B Instruct vs Bonsai Image Ternary 4B FAQs

Is Olmo 3.1 32B Instruct or Bonsai Image Ternary 4B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Olmo 3.1 32B Instruct and Bonsai Image Ternary 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Olmo 3.1 32B Instruct or Bonsai Image Ternary 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, Olmo 3.1 32B Instruct or Bonsai Image Ternary 4B?+

Neither model has a larger sourced context window in this comparison. Olmo 3.1 32B Instruct is 66K and Bonsai Image Ternary 4B is —.

Which performs better in benchmarks, Olmo 3.1 32B Instruct 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 Olmo 3.1 32B Instruct or Bonsai Image Ternary 4B be self-hosted?+

Both models have the same recorded self-hosting status: supported. Olmo 3.1 32B Instruct is open weight; Bonsai Image Ternary 4B is open weight.

Can Olmo 3.1 32B Instruct and Bonsai Image Ternary 4B understand images?+

Olmo 3.1 32B Instruct 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, Olmo 3.1 32B Instruct or Bonsai Image Ternary 4B?+

Neither has a larger sourced maximum output. Olmo 3.1 32B Instruct is 33K and Bonsai Image Ternary 4B is —.

Do Olmo 3.1 32B Instruct and Bonsai Image Ternary 4B support reasoning and tool use?+

Olmo 3.1 32B Instruct: tool calling. 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, Olmo 3.1 32B Instruct or Bonsai Image Ternary 4B?+

Olmo 3.1 32B Instruct has 0 sourced provider routes; Bonsai Image Ternary 4B has 0, a tie.

Which offers better value, Olmo 3.1 32B Instruct 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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