Qwen/Qwen3-Next-80B-A3B-Thinking
Qwen3-Next-80B-A3B-Thinking is a next-generation foundation model from Alibaba's Qwen team featuring a revolutionary Hybrid Attention mechanism (Gated DeltaNet + Gated Attention) with High-Sparsity MoE architecture. With 80B total parameters and only 3.9B active per token, it delivers 10x higher throughput than Qwen3-32B on long contexts while outperforming Gemini-2.5-Flash-Thinking on multiple benchmarks.
api_example.sh
Technical Specifications
Model Architecture & Performance
Pricing
Pay-per-use, no commitments
API Reference
Complete parameter documentation
| Parameter | Type | Default | Description |
|---|---|---|---|
| stream | boolean | true | Enable streaming responses for real-time output. |
| temperature | number | 0.6 | Controls randomness. Lower values recommended for reasoning tasks. |
| max_tokens | number | 8192 | Maximum number of tokens to generate. |
| top_p | number | 0.95 | Nucleus sampling parameter. |
Explore the full request and response schema in our external API documentation
Performance
Strengths & considerations
| Strengths | Considerations |
|---|---|
| Hybrid Attention (Gated DeltaNet + Gated Attention) 10x throughput vs Qwen3-32B on 32K+ contexts Only 3.9B active parameters from 80B total Native 256K context window Thinking-only mode for deep reasoning Outperforms Gemini-2.5-Flash-Thinking | Thinking mode only — no fast non-thinking mode Longer thinking traces increase latency New architecture with limited community tooling |
Use cases
Recommended applications for this model
Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
Don't let your AI control you. Control your AI the Qubrid way!
Have questions? Want to Partner with us? Looking for larger deployments or custom fine-tuning? Let's collaborate on the right setup for your workloads.
"Qubrid's medical OCR and research parsing cut our document extraction time in half. We now have traceable pipelines and reproducible outputs that meet our compliance requirements."
Clinical AI Team
Research & Clinical Intelligence
