Karnak 40B v1.0

Qwen3

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

Released February 2026262K context≈ 196,608 words40B params

Karnak 40B v1.0 is a large open-weight text model built on Qwen3's architecture, offering a substantial 262K token context window that allows it to work across very long documents or extended conversations. At 40 billion parameters, it sits in a range where reasoning depth and instruction-following tend to be reasonably strong. Published under Apache 2.0, it can be run and modified freely, which suits teams wanting local deployment or fine-tuning.

Capabilities

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

Long Context

Moderate

Coding

Karnak 40B v1.0

Qwen3

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

Released February 2026262K context≈ 196,608 words40B params

Karnak 40B v1.0 is a large open-weight text model built on Qwen3's architecture, offering a substantial 262K token context window that allows it to work across very long documents or extended conversations. At 40 billion parameters, it sits in a range where reasoning depth and instruction-following tend to be reasonably strong. Published under Apache 2.0, it can be run and modified freely, which suits teams wanting local deployment or fine-tuning.

Capabilities

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

Long Context

Moderate

Coding

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Fine-TuningThe process of further training a pre-trained model on new data to adapt it for specific tasks or domains.Instruction-FollowingThe ability of a model to understand and execute specific tasks or commands given in natural language prompts.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.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 DepthA model's ability to perform complex multi-step logical thinking and problem-solving; typically increases with model size.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.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