Qwen 3.8 Max Preview Hands-On: 2.4 Trillion Parameter Flagship Approaches Closed-Source Ceiling, Still Lags in Long-Task Stability

On July 19, Alibaba's Tongyi Qianwen team released the Qwen 3.8 series flagship model, with a total parameter count of 2.4 trillion (MoE architecture). Officially positioned as a top-tier general-purpose large model, its overall capability is second only to Fable 5. The preview version, Qwen3.8-Max-Preview, is now available on Token Plan, Qoder, and QoderWork platforms. Weights will be open-sourced later, but the specific release date, license, and minimum deployment requirements have not yet been announced.

Core Specifications and Upgrade Highlights

• Parameter Scale: 2.4 trillion total parameters, doubling from the previous generation Qwen3.7 (1.2 trillion), with expanded expert modules and pre-training corpus.
• Open Strategy: Flagship model weights are directly open-sourced, supporting enterprise private deployment and fine-tuning, breaking the limitations of high-end closed-source models.
• Capability Improvements: Optimized hybrid attention architecture improves cross-chapter association accuracy for million-level long texts by over 15%; code capabilities are adapted for large-scale engineering refactoring and multilingual debugging through Qoder tools.
• Competitive Benchmarking: Directly competes with Kimi K3 (2.8 trillion parameters), but Qwen focuses on balanced general-purpose scenarios, while K3 emphasizes multimodal and long-cycle Agent tasks.

Hands-On Performance: Strong on Complex Tasks, Still Lacking in Details

Third-party evaluations used the same test set as K3, covering tasks such as visual code refactoring, business logic visualization, building evacuation simulation, and 3D Rubik's Cube:

• Visual Code Refactoring: Successfully converted a NASA page screenshot into a single-file HTML, accurately identifying the four-layer structure, using SVG and Canvas for offline display, but not achieving pixel-perfect reproduction and lacking narrow-screen adaptation.
• Business Dashboard: Delivered a 1203-line single-file HTML with native SVG charts and interactive logic; all five core metrics matched the standard answer exactly, approaching an internal tool prototype.
• Building Evacuation Simulation: Implemented complex rules including a 24×16 grid, 12 personnel, and 4 fire doors; A* pathfinding matched the benchmark exactly, with stable state maintenance.
• 3D Rubik's Cube: Built an interactive 3D Rubik's Cube from scratch, but details were not fully disclosed.

Comparison with K3 and Conclusion

• Parameters: Qwen 2.4 trillion vs K3 2.8 trillion, but parameter scale is not the sole determining factor.
• Capabilities: Qwen is more mature in Chinese semantics, official document translation, and enterprise knowledge bases; K3 performs better in native multimodal and long-cycle Agent tasks.
• Hands-On Gap: Qwen slightly lags behind K3 in complex UI visual reproduction and long-task stability, with an initial delivery completion rate of about 80%, but already demonstrates solid engineering delivery capabilities.

Overall, Qwen 3.8 Max Preview shows strength close to top closed-source models in complex software engineering and tool invocation tasks, but still has room for improvement in fine design restoration and long-cycle constraint maintenance.