模型广场

Text-only model in the GLM-4 family with improved coding benchmarks and tool calls during reasoning, suited for software development and agent workflows.

上下文

203K

输入 / 1M tokens

$0.574

输出 / 1M tokens

$2.294

缓存读 / 1M

$0.115

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

Balanced model with strong Chinese understanding.

上下文

128K

输入 / 1M tokens

$0.59

输出 / 1M tokens

$2.35

缓存读 / 1M

$0.55

缓存写 / 1M

$0

Ultra-fast for Chinese. Very cheap.

上下文

203K

输入 / 1M tokens

$0.1

输出 / 1M tokens

$0.43

缓存读 / 1M

$0.01

缓存写 / 1M

$0

Competitive general-purpose Chinese model.

上下文

203K

输入 / 1M tokens

$0.88

输出 / 1M tokens

$3.23

缓存读 / 1M

$0.172

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

Offers significantly improved coding and long-horizon task capabilities, autonomously planning, executing, and refining a single task for over eight hours to deliver complete, engineering-grade results.

上下文

128K

输入 / 1M tokens

$0.826

输出 / 1M tokens

$3.303

缓存读 / 1M

$1.035

缓存写 / 1M

$1.376

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

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