语言模型聊天助手 AI模型大全国内外大模型价格对比主流模型全收录 - 第 12 页

Solar Pro 3 is Upstage's powerful Mixture-of-Experts (MoE) language model. With 102B total parameters and 12B active parameters per forward pass, it delivers exceptional performance while maintaining computational efficiency. Optimized for Korean with English and Japanese support. Solar Pro 3: Better reasoning at production scale

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$0.25 / 1M Tokens

输出价格

$0.25 / 1M Tokens

复杂推理企业工作流韩英日多语言

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语言模型Upstage稳定版

Solar Pro 2

Advanced reasoning, tool use, and multilingual support. Solar Pro 2: Fluent. Reasoning. Frontier.

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$0.25 / 1M Tokens

输出价格

$0.25 / 1M Tokens

复杂推理Tool Use多语言助手

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语言模型Liquid AI稳定版

LFM2.5-1.2B-Instruct

Instruction-tuned 1.2B parameter model optimized for chat and general-purpose tasks

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聊天助手指令跟随Tool Use

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语言模型Liquid AI稳定版

LFM2-1.2B-Extract

1.2B parameter model for structured information extraction from documents

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结构化抽取信息提取端侧工作流

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语言模型Liquid AI即将下线

LFM2-1.2B-Tool

1.2B parameter model for tool calling (deprecated)

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Tool Use工作流编排函数调用

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语言模型Arcee AI预览版

Trinity Nano 6B

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-Nano (6B) Overview Trinity Nano is a 6B-parameter (1B active) sparse mixture-of-experts language model, optimized for high-efficiency inference in real-time, on-device, and embedded AI applications.

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端侧部署低延迟轻量 Agent

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语言模型Arcee AI稳定版

Trinity Mini

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-Mini (26B) Overview Trinity Mini is a 26B-parameter (3B active) sparse mixture-of-experts language model, engineered for efficient inference over long contexts with robust function calling and multi-step agent workflows.

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函数调用Agent 工作流长上下文

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模型详情

语言模型Arcee AI预览版

Trinity Large Preview

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-Preview Overview Trinity Large (Preview) is a 400B-parameter (13B active) sparse mixture-of-experts language model, engineered to scale model capacity while maintaining inference efficiency over long contexts, with strong performance in reasoning-heavy workloads including math, coding-related tasks, and multi-step agent workflows.

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复杂任务编码推理高级 Agent

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模型详情

语言模型Arcee AI稳定版

Trinity Large Thinking

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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多步推理Reasoning TraceAgent 工作流

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