MinerU2.5 Pro 2604 1.2B

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

Released April 202633K context≈ 24,576 words

A compact 1.2B parameter model from opendatalab that handles both text and image inputs, suggesting multimodal document understanding capabilities. Its naming hints at document parsing or data extraction work, and the relatively modest parameter count means it trades raw reasoning depth for efficiency and deployability. Open weights under Apache 2.0 make it accessible for local or custom deployments.

Capabilities

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

Creative Writing

Moderate

Instruction Following

MinerU2.5 Pro 2604 1.2B

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

Released April 202633K context≈ 24,576 words

A compact 1.2B parameter model from opendatalab that handles both text and image inputs, suggesting multimodal document understanding capabilities. Its naming hints at document parsing or data extraction work, and the relatively modest parameter count means it trades raw reasoning depth for efficiency and deployability. Open weights under Apache 2.0 make it accessible for local or custom deployments.

Capabilities

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

Creative Writing

Moderate

Instruction Following

Glossary

Data ExtractionThe process of automatically identifying and pulling relevant information from unstructured or scattered data sources.Document ParsingThe process of automatically reading and extracting structured information like text, tables, and layout from documents.Document UnderstandingThe ability to read and extract meaningful information from structured documents like receipts, invoices, and forms by recognizing both text and layout.MultimodalA model that can process and understand multiple types of input, such as both text and images.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.Parameter ModelA neural network described by the number of learnable weights it contains; more parameters generally mean greater capacity to learn complex patterns, but also require more computational resources.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning DepthA model's ability to perform complex multi-step logical thinking and problem-solving; typically increases with model size.WeightsThe numerical parameters inside a neural network that determine how it processes input and generates output.

MinerU2.5 Pro 2604 1.2B

Capabilities

MinerU2.5 Pro 2604 1.2B

Capabilities

Use Case Fit

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Glossary