Granite 4.2 30B vs Ternary Bonsai 27B

At a Glance

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Pricing and Limits
Input priceFrom · USD / 1M tokens$0.16Deepinfra · Sep 22, 2026Not reported
Output priceFrom · USD / 1M tokens$0.65Deepinfra · Sep 22, 2026Not reported
Context windowMaximum documented tokens131K262K
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

FieldGranite 4.2 30BTernary Bonsai 27B
DeveloperIBMPrismML
FamilyGranite 4 2Bonsai 27b
ModelGranite 4.2 30BTernary Bonsai 27B
VersionGranite 4.2 30BTernary Bonsai 27B
Lifecycleactiveactive
Released2026-08-252026-07-04
Knowledge cutoffUnknownUnknown
Input modalitiesTextText, Image
Output modalitiesTextText
Context window131K262K
Total parameters29.3B27B
Active parametersUnknownUnknown
Licenseapache-2.0apache-2.0
Open weightsYesYes
API availableYesYes
Self-hostableYesYes
Provider accessDeepinfra (Standard)Together Ai (Standard)
Capabilitieschat, generation, reasoning, structured_outputs, toolschat, generation, reasoning, tools, vision
Base modelUnknownQwen3.6 27B
Effective bit widthUnknown1.58 bits per weight
Language model sizeUnknown6.66 GiB
Weight formatUnknownTernary Q2_0

Granite 4.2 30B Capabilities

chatgenerationreasoningstructured outputstools
Serving providers1
Canonical IDibm-granite/granite-4.2-30b

Ternary Bonsai 27B Capabilities

chatgenerationreasoningtoolsvision
Serving providers1
Canonical IDprism-ml/Ternary-Bonsai-27B

Primary Evidence

Sources and Freshness

Questions

Granite 4.2 30B vs Ternary Bonsai 27B FAQs

Is Granite 4.2 30B or Ternary Bonsai 27B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both Granite 4.2 30B and Ternary Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, Granite 4.2 30B or Ternary Bonsai 27B?+

Only Granite 4.2 30B has a directly sourced input price: $0.16 per million tokens. Only Granite 4.2 30B has a directly sourced output price: $0.65 per million tokens.

Which has a larger context window, Granite 4.2 30B or Ternary Bonsai 27B?+

Ternary Bonsai 27B has the larger sourced context window. Granite 4.2 30B supports 131K and Ternary Bonsai 27B supports 262K.

Which performs better in benchmarks, Granite 4.2 30B or Ternary Bonsai 27B?+

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

Can Granite 4.2 30B or Ternary Bonsai 27B be self-hosted?+

Both models have the same recorded self-hosting status: supported. Granite 4.2 30B is open weight; Ternary Bonsai 27B is open weight.

Can Granite 4.2 30B and Ternary Bonsai 27B understand images?+

Granite 4.2 30B is not documented with image input; Ternary Bonsai 27B is documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, Granite 4.2 30B or Ternary Bonsai 27B?+

Neither has a larger sourced maximum output. Granite 4.2 30B is — and Ternary Bonsai 27B is —.

Do Granite 4.2 30B and Ternary Bonsai 27B support reasoning and tool use?+

Granite 4.2 30B: reasoning and tool calling. Ternary Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, Granite 4.2 30B or Ternary Bonsai 27B?+

Granite 4.2 30B has 1 sourced provider route; Ternary Bonsai 27B has 1, a tie.

Which offers better value, Granite 4.2 30B or Ternary Bonsai 27B?+

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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