Language Models
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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.