Comparative Analysis of Multi-Model Performance: Key Factors in Agent Task Efficiency

Experimental Design and Environment Configuration

1.1 Unified Task Framework
The experiment utilized ATELIER, a creative collaboration system simulating real-world business scenarios. Models were required to complete five core states (project overview, moodboard creation, asset positioning/commenting, version approval, and preview publishing) without pre-existing page references. The system supported three roles (creative director, designer, client) with interactive operations like asset dragging, comment pinning, and version switching while ensuring state persistence after refreshes.

1.2 Standardized Testing Environment

• Hardware: All models ran on identical cloud server specifications to ensure consistent computational resources.
• Input Data: Standardized datasets included six open-access Metropolitan Museum images, project member details, asset versions, and comment seeds.
• Acceptance Criteria: Desktop resolution 1440×1000, mobile 390×844. First-version screenshots were saved per group, allowing only one repair round. Submissions included code, screenshots, logs, review records, and performance metrics.

In-Depth Model Performance Analysis

2.1 Task Completion Time

• DeepSeek V4 Flash Vision Exp: Completed all tasks in 17 minutes, demonstrating superior efficiency through lightweight architecture and optimized tool invocation strategies that minimized action rounds and context-switching overhead.
• GLM-5.3-Flash: Failed initial execution but succeeded after 1 hour 12 minutes post-rerun. Failures stemmed from permission matrix initialization issues, resolved by adjusting approval state synchronization.
• Qwen3.8-Flash: Finished in 88 minutes via three internal automatic retries. While its engineering design improved information density, retry mechanisms introduced additional time costs.
• Kimi K3-256K: Used default retry policies with exponential backoff after two consecutive failures, achieving first success in 1 hour 4 minutes. Its museum archive-inspired UI enhanced user experience but increased retry-related delays.

2.2 Retry Mechanisms and Stability

• DeepSeek: No retry mechanism; relied on first-attempt stability through precise context management and low-latency tool calls, reducing service interruption risks.
• GLM: Required manual reruns, exposing initialization fragility. Developers must optimize exception handling and system fault tolerance.
• Qwen: Automatic retries improved success rates but lacked error-type differentiation, causing ineffective attempts. Recommend implementing error classification and dynamic backoff strategies.
• Kimi: Exponential backoff balanced success rates and latency, but default retry limits were insufficient for complex tasks. Parameters should adapt to business scenarios.

2.3 Context Management and State Persistence

• DeepSeek: Adopted a minimalist editorial style with clear state relationships, reducing memory usage through minimized state change logging.
• GLM: Museum-inspired black-gold-and-white UI maintained visual consistency but complicated permission matrix and approval state synchronization, leading to initial failures.
• Qwen: Dark professional workspace design maximized information density but required navigation-canvas interaction optimizations to prevent post-refresh state loss.
• Kimi: Museum archive aesthetics were restrained, but thin-line tables and thumbnail rendering incurred high computational costs, necessitating performance-visual tradeoffs.

Key Capability Evaluation and Optimization Recommendations

3.1 Action Rounds and Tool Invocation Efficiency
DeepSeek’s efficiency stemmed from optimized action round control. Developers can enhance tool invocation through:

• Reducing redundant API calls: Combine operations like asset upload and comment pinning into atomic actions.
• Asynchronous processing: Execute non-critical tasks (e.g., version approval sync) asynchronously to avoid main workflow blocking.
• Caching frequent data: Store project member info and asset metadata to minimize repeated queries.

3.2 Service Stability Enhancement

• Error classification and dynamic retries: Implement exponential backoff for recoverable errors (e.g., network timeouts) and immediate termination for fatal errors.
• Health checks and circuit breakers: Monitor tool service status in real time, switching to backups or degrading services when error rates exceed thresholds.
• Logging and metrics collection: Track tool invocation duration, success rates, and error types to inform model optimization.

3.3 Context Management Best Practices

• State snapshots and incremental updates: Periodically save system states and log only changes to reduce post-refresh reconstruction costs.
• Role-based permission isolation: Assign independent context spaces to roles to prevent permission conflicts.
• Mobile optimization: Use collapsible menus and stepwise loading for small screens to ensure core functionality accessibility.

Conclusion and Future Directions

This experiment demonstrates that low-cost model efficiency depends not solely on parameter count but also on action round control, retry mechanism design, and context management. Developers should prioritize speed, stability, and cost based on business scenarios. Future advancements in model lightweighting and agent frameworks will further enhance collaborative system efficiency and reliability. Continuous focus on tool invocation optimization, automated error handling, and cross-platform adaptation remains critical for building high-performance AI agent systems.