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A model for documents and semi-structured data (JSON). Performs well in English and non-English languages; supports the same languages as embed-multilingual-v3.0. This model has a context length of 4096 tokens
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$2.00 / 1K searches
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A model that allows for re-ranking English Language documents and semi-structured data (JSON). This model has a context length of 4096 tokens.
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$2.00 / 1K searches
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$0.80 / 1M Tokens
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$3.20 / 1M Tokens
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$2.50 / 1M Tokens
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$12.50 / 1M Tokens
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$0.06 / 1M Tokens
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$0.24 / 1M Tokens
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音频
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IBM Granite Guardian 是护栏与评审模型家族，适合越狱检测、工具调用幻觉与 RAG groundedness 判定。
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Pricing Integration List chevron-right Language Models chevron-right Trinity-Nano (6B) Trinity-Mini (26B) Trinity-Large-Preview Trinity-Large-Thinking API Reference chevron-right Your First API Call Chat Completion Usage Models Capabilities chevron-right Streaming Messages Multi-Turn Conversations Function Calling Structured Outputs Reasoning Traces Quick Deploys chevron-right Download Models arrow-up-right Hardware Prerequisites Consumer Hardware chevron-right Inference Engines chevron-right Policies chevron-right Deprecation Policy chevron-up chevron-down gitbook Powered by GitBook gitbook xmark block-quote On this page xmark xmark copy Copy chevron-down block-quote On this page block-quote Language Models Trinity-Large-Thinking Overview Trinity-Large-Thinking is a reasoning-optimized variant of Arcee AI's Trinity-Large family — a 398B-parameter sparse Mixture-of-Experts (MoE) model with approximately 13B active parameters per token. Built on Trinity-Large-Base and post-trained with extended chain-of-thought reasoning and agentic RL, Trinity-Large-Thinking delivers state-of-the-art performance on agentic benchmarks while maintaining strong general capabilities. Trinity-Large-Thinking generates explicit reasoning traces wrapped in <think>...</think> blocks before producing its final response. This thinking process is critical to the model's performance — thinking tokens must be kept in context for multi-turn conversations and agentic loops to function correctly.
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