Qwen/Qwen3-Coder-Next
Qwen3-Coder-Next is an open-weight MoE language model designed specifically for coding agents. With only 3B activated parameters out of 79.7B total, it achieves performance comparable to models with 10–20x more active parameters. It features a hybrid Gated Attention + Gated DeltaNet MoE architecture with 512 experts (10 active per token), 262K native context, and achieves 74.2% on SWE-Bench Verified — making it highly cost-effective for production agent deployment.
api_example.sh
Pricing
Pay-per-use, no commitments
Technical Specifications
Model Architecture & Performance
API Reference
Complete parameter documentation
| Parameter | Type | Default | Description |
|---|---|---|---|
| stream | boolean | true | Enable streaming responses for real-time output. |
| temperature | number | 1 | Controls randomness in output. |
| max_tokens | number | 8192 | Maximum tokens to generate. |
| top_p | number | 0.95 | Controls nucleus sampling. |
Performance
Strengths & considerations
| Strengths | Considerations |
|---|---|
| Only 3B active params from 79.7B total — performs like 30–60B models 74.2% on SWE-Bench Verified, 63.7% SWE-Bench Multilingual Native 262K context length (262,144 tokens) Hybrid Gated Attention + Gated DeltaNet MoE, 512 experts / 10 active Advanced tool calling with complex function orchestration 10–20x parameter efficiency advantage for agent workloads | Non-thinking mode only — no chain-of-thought reasoning blocks Not optimized for vision or multimodal tasks Best suited for agentic tasks; overkill for simple completions |
Use cases
Recommended applications for this model
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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
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