A state-of-the-art sparse language model powered by MoE architecture and retrieval-augmented grounding. No multimodal bloat—just pure, laser-focused text capability.
Purpose-built for speed, efficiency, and accuracy. No compromises.
Sparse mixture of experts routing for efficient inference. Only active experts compute, dramatically reducing latency and energy consumption.
Grounded responses with external knowledge integration. Reduces hallucinations and ensures factually accurate outputs through real-time retrieval.
Pure language excellence. No multimodal overhead—every parameter optimized for text understanding and generation. Built for serious work.
Battle-tested inference optimization, quantization support, and distributed serving. Deploy at scale with confidence.
Simple REST API. Excellent documentation. SDKs for Python, Node.js, and Go. Start integrating in minutes.
Real metrics on real tasks. No cherry-picked results. Full model cards and evaluation methodology published.
Engineered for maximum efficiency and performance.
Kden vs. comparable text-only models on standard evaluation suites.
Integrate Kden into your project in minutes.
import kden
client = kden.Client(api_key="your-key")
response = client.completions.create(
model="kden-64m",
prompt="Explain quantum entanglement in simple terms.",
max_tokens=256,
temperature=0.7
)
print(response.choices[0].text)
curl -X POST https://api.kden.ai/v1/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "kden-64m",
"prompt": "What is quantum mechanics?",
"max_tokens": 256
}'