模型广场

A natively omni-modal model with unified understanding of text, image, audio, and video, providing a flagship foundation for full-modality capabilities.

上下文

128K

输入 / 1M tokens

$0.84

输出 / 1M tokens

$3.28

缓存读 / 1M

$0

缓存写 / 1M

$0

Character roleplay model for virtual companion scenarios, with stable persona adherence and plot progression in multi-turn dialogue, supporting multimodal inputs.

上下文

128K

输入 / 1M tokens

$0.12

输出 / 1M tokens

$0.29

缓存读 / 1M

$0.02

缓存写 / 1M

$0

Lightweight tier of the Seed 2.0 family tuned for low-latency agent, coding, and GUI workloads.

上下文

256K

输入 / 1M tokens

$0.25

输出 / 1M tokens

$2

缓存读 / 1M

$0.05

缓存写 / 1M

$0.008333

Lightweight tier in the Seed 2.0 family, tuned for high-throughput, low-latency tasks with adjustable reasoning levels. Fits batch processing, moderation, and classification.

上下文

256K

输入 / 1M tokens

$0.1

输出 / 1M tokens

$0.4

缓存读 / 1M

$0.02

缓存写 / 1M

$0.008333

A high-performance general-purpose model from Google, designed for advanced reasoning, coding, mathematics, and scientific tasks. Its built-in thinking capabilities improve response accuracy and enable deeper contextual understanding.

上下文

1.1M

输入 / 1M tokens

$0.3

输出 / 1M tokens

$2.5

缓存读 / 1M

$0.03

缓存写 / 1M

$0.08333

A lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency, with faster token generation and higher throughput. Thinking is disabled by default but can be enabled via API.

上下文

1.1M

输入 / 1M tokens

$0.1

输出 / 1M tokens

$0.4

缓存读 / 1M

$0.01

缓存写 / 1M

$0.08333

Massive context window. Great for video understanding.

上下文

1M

输入 / 1M tokens

$2.5

输出 / 1M tokens

$15

缓存读 / 1M

$0.25

缓存写 / 1M

$0

Speed-tier variant in the Gemini 3 line, pairing Pro-grade reasoning with Flash-level latency and cost, built for agent workflows and high-throughput interactive apps.

上下文

1M

输入 / 1M tokens

$0.5

输出 / 1M tokens

$3

缓存读 / 1M

$0.05

缓存写 / 1M

$0

A high-efficiency multimodal lite model for low-latency, high-volume workloads like translation, classification, and data extraction, priced at about half of Gemini 3 Flash.

上下文

1M

输入 / 1M tokens

$0.25

输出 / 1M tokens

$1.5

缓存读 / 1M

$0.025

缓存写 / 1M

$0.08333

Google's latest. Strong reasoning with native image and video.

上下文

1M

输入 / 1M tokens

$2

输出 / 1M tokens

$12

缓存读 / 1M

$0.2

缓存写 / 1M

$0

Designed for efficient multimodal AI tasks, offering strong coding, reasoning, real-time chat, and agent execution at Flash-tier cost and speed.

上下文

1M

输入 / 1M tokens

$1.5

输出 / 1M tokens

$9

缓存读 / 1M

$0.15

缓存写 / 1M

$0.08333

Fast and cost-efficient.

上下文

33K

输入 / 1M tokens

$0.06

输出 / 1M tokens

$0.12

缓存读 / 1M

$0

缓存写 / 1M

$0

GPT-4o Mini Transcribe is OpenAI's smaller, cost-efficient speech-to-text model built on GPT-4o Mini audio capabilities.

上下文

131K

输入 / 1M tokens

-

输出 / 1M tokens

-

缓存读 / 1M

-

缓存写 / 1M

-

GPT-4o Transcribe is OpenAI's high-quality speech-to-text model built on GPT-4o audio capabilities.

上下文

131K

输入 / 1M tokens

-

输出 / 1M tokens

-

缓存读 / 1M

-

缓存写 / 1M

-

GPT-4o Transcribe Diarize is an automatic speech recognition (ASR) model with built-in speaker diarization, meaning it associates audio segments with different speakers in a conversation.

上下文

131K

输入 / 1M tokens

-

输出 / 1M tokens

-

缓存读 / 1M

-

缓存写 / 1M

-

GPT-5.5 natively supports video comprehension, speech emotion recognition, and autonomous tool calling, delivering omni-modal foundational capabilities for agent-based execution of complex tasks.

上下文

1.1M

输入 / 1M tokens

$10

输出 / 1M tokens

$45

缓存读 / 1M

$1

缓存写 / 1M

$0

Whisper is OpenAI's open-source automatic speech recognition model. It supports transcription and translation across 50+ languages from audio files up to 25 MB. Accepts formats including mp3, mp4, wav, and webm.

上下文

待核

输入 / 1M tokens

-

输出 / 1M tokens

-

缓存读 / 1M

-

缓存写 / 1M

-

Mid-tier V2.5 model with native image, audio, and video understanding, built for agent workflows and coding at lower cost than Pro.

上下文

1M

输入 / 1M tokens

$0.14

输出 / 1M tokens

$0.28

缓存读 / 1M

$0.0428

缓存写 / 1M

$0