MODEL PROFILE

Qwen3-Coder-Next-GGUF

阿里巴巴 · Qwen · Qwen3-Coder-Next-GGUF

Today, we're announcing **Qwen3-Coder-Next**, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements: - **Super Efficient with Significant Performance**: With only 3B activated parameters (80B total parameters), it achieves performance comparable to models with 10–20x more active parameters, making it highly cost-effective for agent deployment. - **Advanced Agentic Capabilities**: Through an elaborate training recipe, it excels at long-horizon reasoning, complex tool usage, and recovery from execution failures, ensuring robust performance in dynamic coding tasks. - **Versatile Integration with Real-World IDE**: Its 256k context length, combined with adaptability to various scaffold templates, enables seamless integration with different CLI/IDE platforms (e.g., Claude Code, Qwen Code, Qoder, Kilo, Trae, Cline, etc.), supporting diverse development environments. **Qwen3-Coder-Next** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 80B in total and 3B activated - Number of Parameters (Non-Embedding): 79B - Hidden Dimension: 2048 - Number of Layers: 48 - Hybrid Layout: 12 \* (3 \* (Gated DeltaNet -> MoE) -> 1 \* (Gated Attention -> MoE)) - Gated Attention: - Number of Attention Heads: 16 for Q and 2 for KV - Head Dimension: 256 - Rotary Position Embedding Dimension: 64 - Gated DeltaNet: - Number of Linear Attention Heads: 32 for V and 16 for QK - Head Dimension: 128 - Mixture of Experts: - Number of Experts: 512 - Number of Activated Experts: 10 - Number of Shared Experts: 1 - Expert Intermediate Dimension: 512 - Context Length: 262,144 natively

Text generationlicense:apache-2.0
Context window262,144 Token
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/10/30 17:37:16
OVERVIEW

Model overview

Today, we're announcing **Qwen3-Coder-Next**, an open-weight language model designed specifically for coding agents and local development. It features the following key enhancements: - **Super Efficient with Significant Performance**: With only 3B activated parameters (80B total parameters), it achieves performance comparable to models with 10–20x more active parameters, making it highly cost-effective for agent deployment. - **Advanced Agentic Capabilities**: Through an elaborate training recipe, it excels at long-horizon reasoning, complex tool usage, and recovery from execution failures, ensuring robust performance in dynamic coding tasks. - **Versatile Integration with Real-World IDE**: Its 256k context length, combined with adaptability to various scaffold templates, enables seamless integration with different CLI/IDE platforms (e.g., Claude Code, Qwen Code, Qoder, Kilo, Trae, Cline, etc.), supporting diverse development environments. **Qwen3-Coder-Next** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 80B in total and 3B activated - Number of Parameters (Non-Embedding): 79B - Hidden Dimension: 2048 - Number of Layers: 48 - Hybrid Layout: 12 \* (3 \* (Gated DeltaNet -> MoE) -> 1 \* (Gated Attention -> MoE)) - Gated Attention: - Number of Attention Heads: 16 for Q and 2 for KV - Head Dimension: 256 - Rotary Position Embedding Dimension: 64 - Gated DeltaNet: - Number of Linear Attention Heads: 32 for V and 16 for QK - Head Dimension: 128 - Mixture of Experts: - Number of Experts: 512 - Number of Activated Experts: 10 - Number of Shared Experts: 1 - Expert Intermediate Dimension: 512 - Context Length: 262,144 natively

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Source / channelInputOutputBilling unitCollected at
No verified prices yet. Check the source channel.

Estimate monthly base token costs

—

Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.

INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

View full indexed rankings →

Frequently asked questions

Do open weights permit free commercial use?

Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.

Are these real-time billing prices?

These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.

Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。