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LLM Service TokenHub

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Model List

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Última atualização: 2026-04-22 10:53:53

Language Models

Model Name
Model (API Parameter)
Supported Capabilities
Context Window
(Tokens)
Maximum Input
(Tokens)
Maximum Output
(Tokens)
Deepseek-v3.2
deepseek-v3.2
Deep Reasoning
Structured Output
Function Calling
128k
96k
32k
GLM-5
glm-5
Deep Reasoning
Function Calling
Caching
200k
200k
128k
GLM-5-Turbo
glm-5-turbo
Deep Reasoning
Structured Output
Function Calling
Caching
200k
200k
128k
GLM-5V-Turbo
glm-5v-turbo
Deep Reasoning
Structured Output
Function Calling
Caching
200k
200k
128k
GLM-5.1
glm-5.1
Deep Reasoning
Structured Output
Function Calling
Caching
200k
200k
128k
kimi-k2.5
kimi-k2.5
Deep Reasoning
Structured Output
Function Calling
Caching
256k
224k
16k
MiniMax-M2.5
minimax-m2.5
Deep Reasoning
Function Calling
Caching
200k
200k
128k
MiniMax-M2.7
minimax-m2.7
Deep Reasoning
Function Calling
Caching
200k
200k
128k

Capability Description

Deep Reasoning

The model, before generating the final response, first performs internal (Chain-of-Thought) reasoning by step-by-step analyzing and decomposing problems, thereby improving the accuracy of responses to complex tasks (such as mathematics, logical reasoning, code generation, and so on).

Structured Output

The model supports outputting structured data in specified formats (such as JSON Schema), facilitating direct parsing and utilization by downstream programs. This capability is suitable for scenarios like information extraction, data population, and API response construction.

Function Calling

The model supports function calling capabilities, which can automatically identify and trigger predefined external tools or APIs during the inference process based on user intent, enabling extended operations such as querying databases and invoking third-party services.

Caching

The model's caching capability can reuse context computation results from historical requests, reducing the overhead of redundant computations, thereby improving response speed and reducing invocation costs.

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