SOMA X v0.3.0 vs Ternary Bonsai 27B

At a Glance

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Pricing and Limits
Context windowMaximum documented tokensNot reported262K
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 →

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

FieldSOMA-X v0.3.0Ternary Bonsai 27B
DeveloperNVIDIAPrismML
FamilySoma XBonsai 27b
ModelSOMA-X v0.3.0Ternary Bonsai 27B
VersionSOMA-X v0.3.0Ternary Bonsai 27B
Lifecycleactiveactive
Released2026-09-022026-07-04
Knowledge cutoffUnknownUnknown
Input modalitiesModel-specific inputText, Image
Output modalities3DText
Context windowUnknown262K
Total parametersUnknown27B
Active parametersUnknownUnknown
Licenseapache-2.0apache-2.0
Open weightsYesYes
API availableNoYes
Self-hostableYesYes
Provider accessUnknownTogether Ai (Standard)
Capabilitiesanimation, hand-modeling, human-body-modeling, motion-retargeting, pose-inversion, simulationchat, generation, reasoning, tools, vision
Base modelUnknownQwen3.6 27B
Effective bit widthUnknown1.58 bits per weight
Language model sizeUnknown6.66 GiB
Weight formatUnknownTernary Q2_0

SOMA X v0.3.0 Capabilities

animationhand-modelinghuman-body-modelingmotion-retargetingpose-inversionsimulation
Serving providers0
Canonical IDnvidia/SOMA-X-v0.3.0

Ternary Bonsai 27B Capabilities

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

Primary Evidence

Sources and Freshness

Questions

SOMA X v0.3.0 vs Ternary Bonsai 27B FAQs

Is SOMA X v0.3.0 or Ternary Bonsai 27B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both SOMA X v0.3.0 and Ternary Bonsai 27B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, SOMA X v0.3.0 or Ternary Bonsai 27B?+

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, SOMA X v0.3.0 or Ternary Bonsai 27B?+

Neither model has a larger sourced context window in this comparison. SOMA X v0.3.0 is — and Ternary Bonsai 27B is 262K.

Which performs better in benchmarks, SOMA X v0.3.0 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 SOMA X v0.3.0 or Ternary Bonsai 27B be self-hosted?+

Both models have the same recorded self-hosting status: supported. SOMA X v0.3.0 is open weight; Ternary Bonsai 27B is open weight.

Can SOMA X v0.3.0 and Ternary Bonsai 27B understand images?+

SOMA X v0.3.0 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, SOMA X v0.3.0 or Ternary Bonsai 27B?+

Neither has a larger sourced maximum output. SOMA X v0.3.0 is — and Ternary Bonsai 27B is —.

Do SOMA X v0.3.0 and Ternary Bonsai 27B support reasoning and tool use?+

SOMA X v0.3.0: none of these features are definitively sourced. Ternary Bonsai 27B: reasoning, tool calling, and image input. Feature support does not establish relative quality.

Which is available from more inference providers, SOMA X v0.3.0 or Ternary Bonsai 27B?+

SOMA X v0.3.0 has 0 sourced provider routes; Ternary Bonsai 27B has 1, so Ternary Bonsai 27B has broader tracked availability.

Which offers better value, SOMA X v0.3.0 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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