granite 4.1 3b

Name: granite 4.1 3b
Author: IBM

by IBM

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

Released April 2026131K context≈ 98,304 words3b params

A compact, open-weight model from IBM's Granite family that punches above its weight class for a 3B parameter model, handling long-context tasks with a 128K token window. It processes text-to-text tasks efficiently, making it practical for deployment in resource-constrained environments. Being Apache 2.0 licensed means there are no commercial restrictions, which suits teams wanting full control over their stack.

Capabilities

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

Long Context

Strong

Reasoning & Logic

granite 4.1 3b

by IBM

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

Released April 2026131K context≈ 98,304 words3b params

A compact, open-weight model from IBM's Granite family that punches above its weight class for a 3B parameter model, handling long-context tasks with a 128K token window. It processes text-to-text tasks efficiently, making it practical for deployment in resource-constrained environments. Being Apache 2.0 licensed means there are no commercial restrictions, which suits teams wanting full control over their stack.

Capabilities

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

Long Context

Strong

Reasoning & Logic

Glossary

Long-ContextThe ability of a model to process and understand very long sequences of text while maintaining coherence across distant parts of the input.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.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.Resource-ConstrainedHardware with limited memory, processing power, or battery life, requiring models to be optimized for efficiency.Text-to-TextA framework where all NLP tasks are treated as converting input text into output text, so translation, summarization, and classification use the same model structure.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.

Capabilities

Capabilities

Use Case Fit

Glossary