Ternary Bonsai 2 27B

New

PrismMLReleased Sep 18, 2026

Bonsai 2 27B is a 27B-parameter reasoning model from PrismML derived from Qwen3.8-27B. It supports coding, mathematics, tool calling, and image understanding with a 262K-token context window.

Try it in the estimate Compare

Input, per 1M tokens: $0.090$0.083 for holders
Output, per 1M tokens: $0.60$0.55 for holders
Context: 262K tokens
Tokens this week: None yet

Price

Agents pay these prices from their AI budget. $BINF holders pay up to 8% less for the credit they buy.

Charge	Agents	Holders	Per
Inputper 1M tokens	$0.090	$0.083	per 1M tokens
Outputper 1M tokens	$0.60	$0.55	per 1M tokens
Cached inputper 1M tokens	$0.045	$0.041	per 1M tokens

Price history

What an agent paid per million tokens, by day.

Unchanged since Sep 29, 2026: $0.090 in and $0.60 out per million tokens.

Usage through bInference

Every call our agents make to this model, by day. Counts only: no prompt or answer is stored.

Last 7 days: 0
Last 30 days: 0
All time: 0

InputOutputReasoning

Where it runs

The hosts serving this model now, and how often each answered over the last day.

Use it

Two settings: our address and your agent’s key. The model is set in each call.

import OpenAI from "openai";const client = new OpenAI({  baseURL: "https://binference.io/api/v1",Address  apiKey: process.env.BINF_API_KEY,Key});const reply = await client.chat.completions.create({  model: "prism-ml/ternary-bonsai-2-27b",  messages: [{ role: "user", content: "Summarize today's trades" }],  max_tokens: 1024,});

curl https://binference.io/api/v1/chat/completions \Address  -H "Authorization: Bearer $BINF_API_KEY" \Key  -H "Content-Type: application/json" \  -d '{    "model": "prism-ml/ternary-bonsai-2-27b",    "messages": [{ "role": "user", "content": "Hello" }],    "max_tokens": 1024  }'

Questions

Agents

Holders

Inputper 1M tokens

$0.090

$0.083

per 1M tokens

Outputper 1M tokens

$0.60

$0.55

per 1M tokens

Cached inputper 1M tokens

$0.045

$0.041

per 1M tokens

Host	Answered, last day	Context
DarkbloomInt4int4, answering	99.9%	262K

Host

Answered, last day

Context

DarkbloomInt4int4

99.9%

262K

import OpenAI from "openai";const client = new OpenAI({ baseURL: "https://binference.io/api/v1",Address apiKey: process.env.BINF_API_KEY,Key});const reply = await client.chat.completions.create({ model: "prism-ml/ternary-bonsai-2-27b", messages: [{ role: "user", content: "Summarize today's trades" }], max_tokens: 1024,});

curl https://binference.io/api/v1/chat/completions \Address -H "Authorization: Bearer $BINF_API_KEY" \Key -H "Content-Type: application/json" \ -d '{ "model": "prism-ml/ternary-bonsai-2-27b", "messages": [{ "role": "user", "content": "Hello" }], "max_tokens": 1024 }'

Ternary Bonsai 2 27B

Price

Price history

Usage through bInference

Where it runs

Use it

Questions

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Ternary Bonsai 2 27B

Price

Price history

Usage through bInference

Where it runs

Where it runs

Use it

Questions

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Where it runs

Ternary Bonsai 2 27B

Price

Price history

Usage through bInference

Where it runs

Use it

Questions

How much does Ternary Bonsai 2 27B cost?

How does my agent use Ternary Bonsai 2 27B?

How much can Ternary Bonsai 2 27B read at once?

What can Ternary Bonsai 2 27B do?

Are my prompts stored?

Similar models

Ternary Bonsai 2 27B

Price

Price history

Usage through bInference

Where it runs

Where it runs

Use it

Questions

How much does Ternary Bonsai 2 27B cost?

How does my agent use Ternary Bonsai 2 27B?

How much can Ternary Bonsai 2 27B read at once?

What can Ternary Bonsai 2 27B do?

Are my prompts stored?

Similar models

Where it runs