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

Turbo tier in Seed 2.1: multimodal model for coding and long-horizon agents, suited to multi-step task execution and visual content understanding.

上下文

262K

输入 / 1M tokens

$0.5

输出 / 1M tokens

$2.5

缓存读 / 1M

$0.1

缓存写 / 1M

$0.008

Agent-oriented foundation model tuned for tool calling and complex instruction following, with use cases spanning GUI agents, search agents, and multi-step task orchestration.

上下文

256K

输入 / 1M tokens

$0.5

输出 / 1M tokens

$4

缓存读 / 1M

$0.05

缓存写 / 1M

$0.008333

Code-focused preview model for agent workflows, with competitive SWE-Bench and LiveCodeBench scores, aimed at autonomous coding agents and multi-language software tasks.

上下文

256K

输入 / 1M tokens

$0.5

输出 / 1M tokens

$3

缓存读 / 1M

$0.1

缓存写 / 1M

$0.008333

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

Flagship of the Seed 2.0 series for complex reasoning and long-horizon agent workflows with reliable tool use.

上下文

256K

输入 / 1M tokens

$0.5

输出 / 1M tokens

$3

缓存读 / 1M

$0.1

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

MiniMax\\'s first multimodal release, accepting image and video input. Long-context inference runs cheaper and faster, and it handles multi-step agent workflows and computer-use scenarios.

上下文

1M

输入 / 1M tokens

$1.2

输出 / 1M tokens

$4.8

缓存读 / 1M

$0.12

缓存写 / 1M

$0

Top Chinese-English bilingual. Strong long context.

上下文

262K

输入 / 1M tokens

$0.6

输出 / 1M tokens

$3.011

缓存读 / 1M

$0.15

缓存写 / 1M

$0.718

Latest multimodal model in the K2 series, built for long-horizon coding, code-driven UI/UX generation, and multi-agent orchestration.

上下文

256K

输入 / 1M tokens

$0.8939

输出 / 1M tokens

$3.7131

缓存读 / 1M

$0.34

缓存写 / 1M

$1.1174

Coding-focused variant in the Kimi K2 family. Accepts text and image inputs, with reasoning on by default. Suited for long-horizon coding and agentic workflows.

上下文

262K

输入 / 1M tokens

$0.95

输出 / 1M tokens

$4

缓存读 / 1M

$0.19

缓存写 / 1M

$1.1174

Lightweight multimodal model with balanced performance.

上下文

128K

输入 / 1M tokens

$0.25

输出 / 1M tokens

$0.75

缓存读 / 1M

$0.12

缓存写 / 1M

$0

Plus-tier vision-language model in the Qwen3-VL line, handling text, image and video inputs with strengths in document parsing, video understanding, spatial grounding and agent tool use.

上下文

262K

输入 / 1M tokens

$0.144

输出 / 1M tokens

$1.434

缓存读 / 1M

$0.029

缓存写 / 1M

$0.539

Open-weight mid-tier MoE text model with long chain-of-thought reasoning, strong on function-calling and multi-step planning benchmarks for self-hosted agentic workflows.

上下文

262K

输入 / 1M tokens

$0.115

输出 / 1M tokens

$0.917

缓存读 / 1M

$0

缓存写 / 1M

$0

Mid-tier Qwen model tuned for multi-step reasoning and agent workflows, compact enough for private and on-prem deployment.

上下文

262K

输入 / 1M tokens

$0.086

输出 / 1M tokens

$0.688

缓存读 / 1M

$0

缓存写 / 1M

$0

Lightweight mid-tier variant tuned for low-latency agent workflows with coding and tool-calling support.

上下文

1M

输入 / 1M tokens

$0.029

输出 / 1M tokens

$0.287

缓存读 / 1M

$0.003

缓存写 / 1M

$0.036

Multimodal model from the Qwen3.5 line with toggleable thinking mode, tuned for image and video understanding, document parsing, and multimodal agents.

上下文

1M

输入 / 1M tokens

$0.115

输出 / 1M tokens

$0.688

缓存读 / 1M

$0.057

缓存写 / 1M

$0.717

Thinking-mode variant of the dense open-weight model that outputs step-by-step reasoning; at 27B it surpasses the prior open-weight 397B-A17B flagship across coding, math and multi-step reasoning benchmarks.

上下文

262K

输入 / 1M tokens

$0.412564

输出 / 1M tokens

$2.475384

缓存读 / 1M

$0

缓存写 / 1M

$0

Qwen 3.6 lightweight tier with text, image, and video input. Low latency and low cost, well-suited for high-volume classification, extraction, summarization, and simple agent workflows.

上下文

1M

输入 / 1M tokens

$0.165

输出 / 1M tokens

$0.99

缓存读 / 1M

$0

缓存写 / 1M

$0

Enhanced Qwen model with strong bilingual (Chinese/English) comprehension, excelling at long-document analysis and structured output.

上下文

1M

输入 / 1M tokens

$0.276

输出 / 1M tokens

$1.651

缓存读 / 1M

$0

缓存写 / 1M

$0

Qwen3.7 Flash is the lightweight tier in the series, a vision-language reasoning model with strengths in object recognition, spatial understanding, and real-world visual perception. Suited for multimodal agents, visual coding, search, and computer interaction.

上下文

1M

输入 / 1M tokens

$0.03

输出 / 1M tokens

$0.13

缓存读 / 1M

$0.006

缓存写 / 1M

$0.038

Qwen 3.7 Plus is a mid-tier multimodal model that reads screens, operates GUIs, and navigates mobile apps end-to-end. Suited for agent workflows and tool calling.

上下文

1M

输入 / 1M tokens

$0.276

输出 / 1M tokens

$1.101

缓存读 / 1M

$0.056

缓存写 / 1M

$0

Flash tier in Qwen3.8: multimodal reasoning for coding and agent workflows, with visual understanding across documents, codebases, and long video.

上下文

1M

输入 / 1M tokens

$0.113

输出 / 1M tokens

$0.382

缓存读 / 1M

$0.014

缓存写 / 1M

$0.177

Qwen3.8 series flagship, the general-availability successor to Max Preview. A multimodal reasoning model built for complex reasoning, visual understanding, coding, and agentic workflows.

上下文

1M

输入 / 1M tokens

$2

输出 / 1M tokens

$6

缓存读 / 1M

$0.25

缓存写 / 1M

$2.5

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

Multimodal model with strong image + text understanding.

上下文

128K

输入 / 1M tokens

$0.3

输出 / 1M tokens

$0.9

缓存读 / 1M

$0.055

缓存写 / 1M

$0

Flash tier of GLM-5.3: native multimodal inputs, suited for efficient coding and long-horizon agents with stable long-context behavior in production.

上下文

1M

输入 / 1M tokens

$0.15

输出 / 1M tokens

$0.5

缓存读 / 1M

$0.03

缓存写 / 1M

$0