Ternary Bonsai 2 27B: context, capabilities and where to run it

Prices from official vendor price lists · October 2026

PrismML does not publish a token price for this model, so cost depends on where it is hosted. Recorded here for vision, reasoning, adjustable thinking.

Capabilities
Text & chatVisionReasoning
Context window
262.1K tokens
Price / mo
API only

Ternary Bonsai 2 27B is an AI model from PrismML. The vendor does not publish a public per-token price, so the cost cells are left empty rather than guessed.

It holds 262.1K tokens in a single conversation. Capabilities recorded in this rating: text & chat, vision, reasoning, adjustable thinking, tool calling. Access is through the API rather than a consumer subscription.

API priceNot publishedper 1M tokens, in / out
Context262.1K tokenstokens per conversation
VendorPrismMLall models from this vendor
TierUnpricedno public rate card

Compared with alternatives

Good and poor fit

Good fit
  • Multi-step analysis, planning and code review, where the first plausible answer is often wrong.
  • Reading screenshots, charts, scanned documents and photos.
Poor fit
  • Work where the vendor does not publish a rate you can budget against.

Related filters

Text & chat234 models for text generation and chat
Vision159 models that read images
Reasoning175 models that think before answering
Adjustable thinking85 models with a reasoning depth dial
Tool calling172 models that call your functions
PrismML1 model from this vendor
All 245 modelsThe full rating with filters and prices

Frequently asked questions

How much does Ternary Bonsai 2 27B cost?

PrismML does not publish a public per-token rate for this model, so the price cells here are empty rather than estimated.

What is the context window of Ternary Bonsai 2 27B?

262.1K tokens in a single conversation.

What can Ternary Bonsai 2 27B do?

In this rating it is recorded with: text & chat, vision, reasoning, adjustable thinking, tool calling, multilingual, open weights.

Is Ternary Bonsai 2 27B a good choice?

It depends on the job. See the good-fit and poor-fit lists above, and compare against the neighbouring models before committing.

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