模型广场

Fast and affordable. Handles simple tasks well.

上下文

200K

输入 / 1M tokens

$1

输出 / 1M tokens

$5

缓存读 / 1M

$0.1

缓存写 / 1M

$1.25

Previous-gen Sonnet. Still strong for long-form content.

上下文

1M

输入 / 1M tokens

$3

输出 / 1M tokens

$15

缓存读 / 1M

$0.3

缓存写 / 1M

$3.75

Best balance of quality and cost from Anthropic.

上下文

1M

输入 / 1M tokens

$3

输出 / 1M tokens

$15

缓存读 / 1M

$0.3

缓存写 / 1M

$3.75

Coding and agent quality approach the frontier, with native reasoning built in, well-suited for complex planning and long-horizon professional tasks that require cross-step consistency.

上下文

1M

输入 / 1M tokens

$2

输出 / 1M tokens

$10

缓存读 / 1M

$0.2

缓存写 / 1M

$2.5

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

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

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

A multimodal reasoning model for complex coding, knowledge work, and long-running agent tasks. It works across large codebases, calls tools, and debugs, using images, logs, and test output to refine its output.

上下文

1M

输入 / 1M tokens

$3

输出 / 1M tokens

$15

缓存读 / 1M

$0.3

缓存写 / 1M

$0

Stable code generation with predictable output.

上下文

1M

输入 / 1M tokens

$2

输出 / 1M tokens

$8

缓存读 / 1M

$0.5

缓存写 / 1M

-

Balanced general-purpose model covering dialogue, coding, and multilingual tasks at reasonable speed and cost for assistants and routine agent workflows.

上下文

128K

输入 / 1M tokens

$2.5

输出 / 1M tokens

$10

缓存读 / 1M

$1.25

缓存写 / 1M

$0

Reliable all-rounder for everyday tasks.

上下文

400K

输入 / 1M tokens

$1.25

输出 / 1M tokens

$10

缓存读 / 1M

$0.125

缓存写 / 1M

-

ChatGPT product-line snapshot of GPT-5, tuned for natural multi-turn conversation and consistent tone rather than agentic reasoning workloads.

上下文

128K

输入 / 1M tokens

$1.25

输出 / 1M tokens

$10

缓存读 / 1M

$0.13

缓存写 / 1M

$0

Successor to GPT-5 that scales reasoning depth to task difficulty, returning fast answers on simple prompts and slower deliberation on hard ones, fit for general assistant, coding, and tool-use work.

上下文

400K

输入 / 1M tokens

$1.25

输出 / 1M tokens

$10

缓存读 / 1M

$0.13

缓存写 / 1M

$0

Rolling snapshot of the GPT-5.1 build powering ChatGPT, tuned for natural multi-turn conversation rather than heavy reasoning workloads.

上下文

128K

输入 / 1M tokens

$1.25

输出 / 1M tokens

$10

缓存读 / 1M

$0.13

缓存写 / 1M

$0

Previous flagship. Strong reasoning at a lower price.

上下文

400K

输入 / 1M tokens

$1.75

输出 / 1M tokens

$14

缓存读 / 1M

$0.175

缓存写 / 1M

-

Snapshot pointer to the GPT-5.2 Instant model that powers ChatGPT, optimized for everyday chat, writing, and light coding.

上下文

128K

输入 / 1M tokens

$1.75

输出 / 1M tokens

$14

缓存读 / 1M

$0.175

缓存写 / 1M

$0

Chat-tuned everyday model that talks more naturally and refuses less, with better factual accuracy on common questions.

上下文

128K

输入 / 1M tokens

$1.75

输出 / 1M tokens

$14

缓存读 / 1M

$0.175

缓存写 / 1M

$0

High-quality coding with stable refactoring.

上下文

256K

输入 / 1M tokens

$1.75

输出 / 1M tokens

$14

缓存读 / 1M

$0.175

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

1M

输入 / 1M tokens

$2.5

输出 / 1M tokens

$15

缓存读 / 1M

$0.25

缓存写 / 1M

$0

The middle rung of the GPT-5.6 lineup, tuned for daily dev work, general reasoning, and agent orchestration. Delivers reliable performance at a manageable cost for routine business workloads.

上下文

1.1M

输入 / 1M tokens

$2

输出 / 1M tokens

$12

缓存读 / 1M

$0.2

缓存写 / 1M

$2.5

Reasoning model that works through problems step by step before answering, useful for math, science, and code questions that need multi-step derivation.

上下文

200K

输入 / 1M tokens

$2

输出 / 1M tokens

$8

缓存读 / 1M

$0.5

缓存写 / 1M

$0

Cost-efficient reasoning model with adjustable thinking effort, tuned for STEM and coding tasks where deliberation matters.

上下文

200K

输入 / 1M tokens

$1.1

输出 / 1M tokens

$4.4

缓存读 / 1M

$0.55

缓存写 / 1M

-

Lightweight o-series reasoning model with built-in chain-of-thought, delivering steady math, coding, and tool-use quality at low latency and cost.

上下文

2M

输入 / 1M tokens

$1.1

输出 / 1M tokens

$4.4

缓存读 / 1M

$0.275

缓存写 / 1M

$0

Flagship agent-centric reasoning model with a 1M context window, excelling at coding, productivity, and long-horizon autonomous tasks.

上下文

1M

输入 / 1M tokens

$1.65

输出 / 1M tokens

$4.951

缓存读 / 1M

$0.33

缓存写 / 1M

$2.063

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

Reasoning-oriented Grok variant built for multi-step problem solving and agentic tool use, with strict prompt adherence and low hallucination.

上下文

2M

输入 / 1M tokens

$2.5

输出 / 1M tokens

$5

缓存读 / 1M

$0.4

缓存写 / 1M

$0

Multi-agent variant that dispatches sub-agents in parallel for deep research, multi-step reasoning, and tool orchestration.

上下文

2M

输入 / 1M tokens

$4

输出 / 1M tokens

$12

缓存读 / 1M

$0.4

缓存写 / 1M

$0

Reasoning-oriented Grok variant built for multi-step problem solving and agentic tool use, with strict prompt adherence and low hallucination.

上下文

2M

输入 / 1M tokens

$2.5

输出 / 1M tokens

$5

缓存读 / 1M

$0.4

缓存写 / 1M

$0

Grok 4.3 is a reasoning model from xAI that supports text and image inputs with text output. It is suited for agentic workflows, instruction following, long-document analysis, and deep research. The model supports a 1M token context window. Requests above 200K total tokens are billed at a higher rate.

上下文

1M

输入 / 1M tokens

$2.5

输出 / 1M tokens

$5

缓存读 / 1M

$0.4

缓存写 / 1M

$0

Grok's flagship reasoning model, running an internal chain of thought before responding. Strong performance on coding and STEM tasks, with support for text, image, and file inputs. Suited for technical problem-solving and long-document analysis.

上下文

500K

输入 / 1M tokens

$2

输出 / 1M tokens

$6

缓存读 / 1M

$0.5

缓存写 / 1M

$0

Grok's agent-oriented release, built for long-running multi-step work across a codebase. Accepts text, image, and file inputs. Suited for research and interactive or visual output.

上下文

500K

输入 / 1M tokens

$2

输出 / 1M tokens

$6

缓存读 / 1M

$0.5

缓存写 / 1M

$0

Flagship agent model for complex workflows.

上下文

1.1M

输入 / 1M tokens

$2

输出 / 1M tokens

$6

缓存读 / 1M

$0.4

缓存写 / 1M

$0

Designed for agent-based workflows such as OpenClaw.

上下文

128K

输入 / 1M tokens

$1.2

输出 / 1M tokens

$4

缓存读 / 1M

$0.24

缓存写 / 1M

$0

GLM 5.2 is a large-scale reasoning model with a 1M-token context window, suited for long-horizon agents, repo-level coding, and multi-step automation.

上下文

1M

输入 / 1M tokens

$1.4

输出 / 1M tokens

$4.4

缓存读 / 1M

$0.275

缓存写 / 1M

$0

Flagship-tier model for complex software engineering and long-horizon agent tasks, with stable execution across multi-turn tool calls and large-scale codebase refactoring.

上下文

1M

输入 / 1M tokens

$1.4

输出 / 1M tokens

$4.4

缓存读 / 1M

$0.26

缓存写 / 1M

$0