Model Cost Profile

Arcee AI: Trinity Mini (free)

Developer: arcee-ai

Pricing updated Mar 11, 2026

Input rank: #2Output rank: #2

Live Pricing

Input: $0.0000

Output: $0.0000

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

Arcee AI: Trinity Mini offers an extensive context window of 131072 tokens, making it suitable for applications requiring deep contextual understanding, such as long-form content generation and complex data analysis. This free API model is particularly beneficial for teams looking to leverage advanced AI capabilities without incurring costs, allowing for experimentation and development without financial constraints. The zero-cost structure for both input and output tokens enables organizations to scale their usage efficiently, making it an ideal choice for startups and small teams focused on innovation.

๐Ÿ”ง Tool Calling๐Ÿ“‹ Structured Output๐Ÿง  Reasoning

Context Window

131,072

Tokens

Input Price / 1M

$0.0000

Prompt tokens

Output Price / 1M

$0.0000

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

Arcee AI: Trinity Mini (free) Pricing Trend

Input / 1M tokensOutput / 1M tokens
Mar 7 โ€” Mar 11
$0.000000$0.000000$0.000000Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.000000

Current Output / 1M

$0.000000

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FAQ

Common pricing and benchmark questions for Arcee AI: Trinity Mini (free).

How much does Arcee AI: Trinity Mini (free) cost per 1M input tokens?

Arcee AI: Trinity Mini (free) input pricing is $0.0000 per 1M tokens based on the latest synced provider data.

How much does Arcee AI: Trinity Mini (free) cost per 1M output tokens?

Arcee AI: Trinity Mini (free) output pricing is $0.0000 per 1M tokens based on the latest synced provider data.

What context window does Arcee AI: Trinity Mini (free) support?

Arcee AI: Trinity Mini (free) supports a context window of 131,072 tokens.

How can I compare Arcee AI: Trinity Mini (free) with cheaper alternatives?

Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.