LLaDA2.1 flash

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

Released February 202633K context≈ 24,576 words

LLaDA2.1 flash is a diffusion-based language model that generates text through iterative denoising rather than the typical left-to-right token prediction — a fundamentally different working style that can feel unfamiliar at first. It handles reasoning and instruction-following tasks with a compact footprint, trading some raw capability for speed and accessibility.

Capabilities

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

Reasoning & Logic

Moderate

Creative Writing

Moderate

Factual Knowledge

Use Case Fit

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

LLaDA2.1 flash

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

Released February 202633K context≈ 24,576 words

LLaDA2.1 flash is a diffusion-based language model that generates text through iterative denoising rather than the typical left-to-right token prediction — a fundamentally different working style that can feel unfamiliar at first. It handles reasoning and instruction-following tasks with a compact footprint, trading some raw capability for speed and accessibility.

Capabilities

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

Reasoning & Logic

Moderate

Creative Writing

Moderate

Factual Knowledge

Use Case Fit

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

Glossary

AccessibilityDesigning technology so people with disabilities can use it effectively.DenoisingA training approach where the model learns to reconstruct clean audio from corrupted or noisy versions, improving its ability to extract meaningful features.Diffusion-Based Language ModelA language model that generates text by iteratively predicting and refining masked (hidden) tokens across the entire output, rather than predicting one token at a time from left to right.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Iterative DenoisingThe process of gradually removing noise from a noisy input through multiple refinement steps to generate clean outputs.Language ModelAn AI model trained to predict and generate text by learning patterns from large amounts of written data.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.