Trinity Large Thinking

Arcee AIReleased Apr 1, 2026

Trinity Large Thinking is a powerful open source reasoning model from the team at Arcee AI. It shows strong performance in PinchBench, agentic workloads, and reasoning tasks.

Try it in the estimate Compare

Input, per 1M tokens: $0.30$0.28 for holders
Output, per 1M tokens: $0.96$0.88 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	$0.30	$0.28	per 1M tokens
Outputper 1M tokens	$0.96	$0.88	per 1M tokens
Cached inputper 1M tokens	$0.072	$0.066	per 1M tokens

Price history

What an agent paid per million tokens, by day.

Unchanged since Sep 29, 2026: $0.30 in and $0.96 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: "arcee-ai/trinity-large-thinking",  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": "arcee-ai/trinity-large-thinking",    "messages": [{ "role": "user", "content": "Hello" }],    "max_tokens": 1024  }'

Questions

Agents

Holders

Inputper 1M tokens

$0.30

$0.28

per 1M tokens

Outputper 1M tokens

$0.96

$0.88

per 1M tokens

Cached inputper 1M tokens

$0.072

$0.066

per 1M tokens

Host	Answered, last day	Context
Arcee AI, answering	100%	262K

Host

Answered, last day

Context

Arcee AI

100%

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: "arcee-ai/trinity-large-thinking", 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": "arcee-ai/trinity-large-thinking", "messages": [{ "role": "user", "content": "Hello" }], "max_tokens": 1024 }'

Trinity Large Thinking

Price

Price history

Usage through bInference

Where it runs

Use it

Questions

Similar models

Trinity Large Thinking

Price

Price history

Usage through bInference

Where it runs

Where it runs

Use it

Questions

Similar models

Where it runs

Trinity Large Thinking

Price

Price history

Usage through bInference

Where it runs

Use it

Questions

How much does Trinity Large Thinking cost?

How does my agent use Trinity Large Thinking?

How much can Trinity Large Thinking read at once?

What can Trinity Large Thinking do?

Are my prompts stored?

Similar models

Trinity Large Thinking

Price

Price history

Usage through bInference

Where it runs

Where it runs

Use it

Questions

How much does Trinity Large Thinking cost?

How does my agent use Trinity Large Thinking?

How much can Trinity Large Thinking read at once?

What can Trinity Large Thinking do?

Are my prompts stored?

Similar models

Where it runs