ERNIE-4.5-21B-A3B-Thinking

Name: ERNIE-4.5-21B-A3B-Thinking
Author: Baidu

by BaiduERNIE

Open WeightModel weights are publicly available — can be downloaded and self-hosted

context N/A21B params

ERNIE-4.5-21B-A3B-Thinking is a reasoning-focused model that thinks before it responds, working through problems step by step before committing to an answer. Its sparse 21B-parameter architecture activates only 3B parameters per forward pass, making it computationally lean without sacrificing depth. It handles Chinese and English fluently, with particular strength in structured reasoning tasks.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Tool Use

Moderate

Multilingual

Moderate

Reasoning & Logic

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

ERNIE-4.5-21B-A3B-Thinking

by BaiduERNIE

Open WeightModel weights are publicly available — can be downloaded and self-hosted

context N/A21B params

ERNIE-4.5-21B-A3B-Thinking is a reasoning-focused model that thinks before it responds, working through problems step by step before committing to an answer. Its sparse 21B-parameter architecture activates only 3B parameters per forward pass, making it computationally lean without sacrificing depth. It handles Chinese and English fluently, with particular strength in structured reasoning tasks.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Tool Use

Moderate

Multilingual

Moderate

Reasoning & Logic

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning TasksProblems that require a model to think through multiple steps logically to arrive at an answer, rather than just pattern-matching.Reasoning-FocusedA model specifically trained to work through multi-step logical problems methodically rather than generating quick responses.Structured ReasoningThe ability to follow logical steps and rules systematically to solve problems, often involving breaking down complex tasks into smaller, ordered components.