模型广场

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

Open-source general-purpose LLM with representative instruction-following and coding skills within the open-weights ecosystem, suited for chat, code assistance, and enterprise text workflows.

上下文

131K

输入 / 1M tokens

$0.287

输出 / 1M tokens

$1.147

缓存读 / 1M

$0.057

缓存写 / 1M

-

Best value for code and math. Fraction of flagship cost.

上下文

164K

输入 / 1M tokens

$0.287

输出 / 1M tokens

$0.431

缓存读 / 1M

$0.058

缓存写 / 1M

$0.359

Experimental interim release focused on long-context efficiency, with optional reasoning mode and V3.1-level performance overall.

上下文

164K

输入 / 1M tokens

$0.287

输出 / 1M tokens

$0.43

缓存读 / 1M

$0

缓存写 / 1M

$0

Faster, lower-cost variant of DeepSeek V4, optimized for coding assistants, high-throughput chat, and latency-sensitive Agent workflows.

上下文

1M

输入 / 1M tokens

$0.22

输出 / 1M tokens

$0.66

缓存读 / 1M

$0.007

缓存写 / 1M

$0

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 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

Cost-effective general model for balanced speed and quality.

上下文

128K

输入 / 1M tokens

$0.31

输出 / 1M tokens

$1.23

缓存读 / 1M

$0.031

缓存写 / 1M

$0.39

Built for conversation and bilingual chat.

上下文

197K

输入 / 1M tokens

$0.304

输出 / 1M tokens

$1.213

缓存读 / 1M

$0.061

缓存写 / 1M

$0

A next-generation autonomous language model that uses multi-agent collaboration to plan, execute, and continuously refine complex tasks. It supports production-grade workflows including live debugging, root cause analysis, financial modeling, and document generation across Word, Excel, and PowerPoint.

上下文

205K

输入 / 1M tokens

$0.3

输出 / 1M tokens

$1.2

缓存读 / 1M

$0.06

缓存写 / 1M

$0

Near GPT-4o performance at lower latency and cost, suited for high-frequency interactions, coding, and vision tasks.

上下文

1M

输入 / 1M tokens

$0.4

输出 / 1M tokens

$1.6

缓存读 / 1M

$0.1

缓存写 / 1M

-

Fast and low-cost; for high-concurrency text and reasoning tasks.

上下文

400K

输入 / 1M tokens

$0.25

输出 / 1M tokens

$2

缓存读 / 1M

$0.025

缓存写 / 1M

$0

Lightweight variant in the gpt-5.4 family, tuned for low-latency, high-volume tasks like classification, extraction, and sub-agent execution.

上下文

400K

输入 / 1M tokens

$0.2

输出 / 1M tokens

$1.25

缓存读 / 1M

$0.02

缓存写 / 1M

$0

Entry point of the GPT-5.6 lineup. A small, low-latency workhorse for chat, tagging, and lightweight agent loops, keeping reasoning solid enough for routine work.

上下文

1.1M

输入 / 1M tokens

$0.2

输出 / 1M tokens

$1.2

缓存读 / 1M

$0.02

缓存写 / 1M

$0.25

Instruction-tuned Qwen3 variant without thinking mode, strong on multilingual reasoning, math, code, and tool use for agent workflows.

上下文

262K

输入 / 1M tokens

$0.287

输出 / 1M tokens

$2.868

缓存读 / 1M

-

缓存写 / 1M

-

Internal fine-tune of Qwen3-32B for text-only Chinese workloads, tuned to in-house instruction style for QA, summarization and rewriting.

上下文

131K

输入 / 1M tokens

$0.287

输出 / 1M tokens

$1.147

缓存读 / 1M

-

缓存写 / 1M

-

Qwen3-generation thinking-mode reasoning model with native tool use, tuned for math, coding, and multi-step agentic workflows.

上下文

262K

输入 / 1M tokens

$0.359

输出 / 1M tokens

$1.434

缓存读 / 1M

$0.072

缓存写 / 1M

$2.438

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

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

Open-weight coding model tuned for agentic terminal tasks and repo-scale reasoning with low inference cost.

上下文

262K

输入 / 1M tokens

$0.248

输出 / 1M tokens

$1.485

缓存读 / 1M

-

缓存写 / 1M

-

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

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

Flagship coding and agent model that sustains thousand-tool-call autonomous workflows for complex software engineering.

上下文

1M

输入 / 1M tokens

$0.435

输出 / 1M tokens

$0.87

缓存读 / 1M

$0.0036

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