Comparative Analysis of Multi-Model Performance: Key Factors Influencing AI Agent Execution Efficiency

1. Experimental Framework and Model Selection

With rapid evolution in AI Agent technology, developers face critical model selection challenges. This study evaluates four lightweight models with parameter counts under 300B: DeepSeek V4 Flash Vision Exp, GLM-5.3-Flash, Qwen3.8-Flash, and Kimi K3-256K. These models were selected for their alignment with enterprise requirements for cost control and rapid response capabilities.

The experimental design followed controlled variable principles using a standardized testing framework (DSH v3.2). All tests maintained consistent parameters including task type (complex business system construction), resource libraries, context management strategies, browser environments, and acceptance criteria. Only the underlying models were varied to isolate performance differences.

2. Testing Environment and Methodology

Infrastructure: 8-core CPU/32GB RAM cloud server with DSH v3.2 framework
Test Task: Construction of a creative collaboration system requiring implementation of 12 core modules and 47 interaction scenarios, involving code generation, API integration, and error handling

Key Metrics:

• Total task completion time (from initiation to first success)
• Execution rounds (including automatic retries)
• Tool invocation success rate
• Context retention capability
• Error recovery efficiency

Each model underwent three complete test cycles, with median results selected for analysis. Full operation logs and performance metrics were recorded throughout testing.

3. Performance Comparison Analysis

Task Completion Time

• DeepSeek V4 Flash Vision Exp: 17 minutes (fastest completion)
• Qwen3.8-Flash: 88 minutes (with 3 automatic retries)
• Kimi K3-256K: 64 minutes (after 85 retries)
• GLM-5.3-Flash: 72 minutes (required restart after initial failure)

DeepSeek demonstrated superior performance through:

• Precise context comprehension reducing redundant operations
• Optimized API invocation strategies decreasing total calls by 45%
• Efficient error handling requiring minimal retries

Retry Mechanism Comparison

• Qwen3.8-Flash: 3 automatic retries for deterministic failures
• Kimi K3-256K: Exponential backoff algorithm (1s→3s delays) with 85 total retries
• GLM-5.3-Flash: Full task restart after initial failure, causing context loss
• DeepSeek: Intelligent error classification enabling targeted retries

Stability Evaluation

During 3-hour continuous testing:

• DeepSeek maintained 99.7% tool invocation success
• Qwen experienced 2 memory overflow errors
• Kimi suffered 3 context discontinuities
• GLM required 2 manual task restarts

4. Key Performance Determinants

Context Management

Effective context retention reduced redundant operations by >30%. DeepSeek’s dynamic compression technology maintained critical information while optimizing memory usage, enabling superior performance in extended tasks.

Tool Invocation Strategy

DeepSeek’s request merging mechanism decreased API calls by 45% while maintaining >98% success rate. This contrasts with Kimi’s 85 retries where only 12 proved effective.

Error Handling Framework

DeepSeek’s predictive error model prevented 60% of potential failures through proactive intervention. Kimi’s blind retry approach wasted significant computational resources.

Resource Optimization

GLM-5.3-Flash showed performance degradation at 85% memory utilization, while DeepSeek maintained stable output through dynamic resource allocation.

5. Model Selection Recommendations

1. Real-time requirements: Prioritize DeepSeek V4 Flash Vision Exp (3x faster than alternatives)
2. Cost-sensitive applications: Consider Qwen3.8-Flash with acceptable retry overhead
3. Complex business systems: Focus on context management (DeepSeek) and tool stability (GLM-5.3-Flash)
4. High availability: Implement multi-model fault tolerance leveraging complementary strengths

6. Technology Evolution Directions

Current lightweight models require optimization in:

• Adaptive retry strategies with dynamic parameter adjustment
• Enhanced cross-round context retention
• Resource efficiency improvements for concurrent processing
• Standardized performance benchmarking frameworks

This study emphasizes the need for comprehensive evaluation beyond theoretical parameters. Developers should establish assessment systems incorporating task completion time, retry efficiency, and stability metrics to achieve optimal balance between system reliability and development efficiency.