InternVL3 5 GPT OSS 20B A4B Preview HF

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

Released August 2025131K context≈ 98,304 words20B params

InternVL3 5 GPT OSS 20B A4B is a vision-language model that punches above its active parameter count — it uses a mixture-of-experts architecture activating only 4B parameters per forward pass despite having 20B total. It handles image-text reasoning tasks with solid multilingual coverage and tends to be practical for document understanding, chart reading, and visual question answering.

Capabilities

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

Instruction Following

Strong

Multimodal

InternVL3 5 GPT OSS 20B A4B Preview HF

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

Released August 2025131K context≈ 98,304 words20B params

InternVL3 5 GPT OSS 20B A4B is a vision-language model that punches above its active parameter count — it uses a mixture-of-experts architecture activating only 4B parameters per forward pass despite having 20B total. It handles image-text reasoning tasks with solid multilingual coverage and tends to be practical for document understanding, chart reading, and visual question answering.

Capabilities

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

Instruction Following

Strong

Multimodal

Glossary

Active Parameter CountThe number of model parameters that are actually used during inference for a given input, as opposed to the total parameters available.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.CoverageThe extent to which training data represents all relevant aspects or regions of a document or domain.Document UnderstandingThe ability to read and extract meaningful information from structured documents like receipts, invoices, and forms by recognizing both text and layout.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.Image-Text ReasoningThe ability to understand and answer questions that require analyzing both visual content and textual information together.Language ModelAn AI model trained to predict and generate text by learning patterns from large amounts of written data.MultilingualA model trained to understand and generate text in multiple languages, not just English.Multilingual CoverageThe ability of a model to understand and generate text in multiple languages, typically because it was trained on data from many different languages.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.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.Text ReasoningA model's capability to analyze, interpret, and draw logical conclusions from textual information.Vision-LanguageA model designed to understand and reason about both visual content (images) and natural language text together.Vision-Language ModelAn AI model that understands both images and text, allowing it to answer questions about images or describe what it sees.Visual Question AnsweringA task where an AI model reads a question and an image, then generates an answer based on what it understands from the image.

Capabilities

Capabilities

Use Case Fit

Glossary