Qwen3-Coder-Next
A coding-focused sparse model for long-running agents and local development.
- Published size
- 80B
- Active parameters
- 3B
- Context window
- 256K
- Architecture
- Hybrid MoE
- License
- Apache 2.0
Deployment considerations
The checkpoint contains 80B parameters despite 3B activation. Test with the actual coding scaffold and tool-call parser you will deploy.
The official card lists 80B total, 3B active parameters and a native context of 262,144 tokens. It supports non-thinking mode only.
GPU memory estimates and minimum / recommended configurations are pending. This profile does not contain measured deployment results.
GPU requirements
- Minimum
- —GPU / VRAM · pending
- Recommended
- —GPU / VRAM · pending
Memory estimates and tested configurations will appear here after checkpoint review and deployment testing.
No invented leaderboards.
Latency, throughput, and cost per token will appear here after a reproducible run. Until then, this page helps you understand the model—not predict its performance.
Qwen3-Coder-Next deployment FAQ
How much GPU memory does Qwen3-Coder-Next need?
The full checkpoint weight footprint is pending review. Minimum and recommended GPU configurations will be added after testing; the model name or active parameter count alone is not a memory requirement.
Has BenchGrid benchmarked Qwen3-Coder-Next?
Not yet. This profile contains publisher specifications and calculated weight-memory estimates. We do not currently publish measured latency, throughput, or cost per token for this model.
Where do these specifications come from?
The specifications are based on the official Alibaba model card linked on this page. Memory estimates use the stated total parameter count, including inactive experts for MoE models. Nominal model sizes are labeled with ~.
Explore your compute options.
Check available hardware, quotas, and current pricing with the provider. These links are not verified deployments or performance recommendations.
