Haiku 4.5 vs MiniMax M2.1：Agent任务基准测试对比分析

Jesutofunmie's open-source comparison project systematically tested Anthropic Haiku4.5 and MiniMax M2.1 on Agent tasks, revealing their differences in design thinking and execution skills. This provides valuable data references for developers to select suitable Agent models, covering dimensions like task understanding, tool usage, and multi-round dialogue.

Traditional benchmarks (MMLU, HumanEval) fail to assess Agent-specific capabilities (multi-round dialogue, tool calling, error recovery). Thus, the Haiku-4.5-vs-Minimax-2.1 project was launched.

• Haiku4.5: Lightweight Claude model, fast/low-cost, improved reasoning, suitable for latency/cost-sensitive scenarios.
• MiniMax M2.1: Optimized for Agent tasks (Function Calling, multi-round management), strong in Chinese context. Both are widely used for Agent construction.

Systematic methods ensure comparability: Task Design: Info collection, tool usage, planning/decomposition, error recovery, multi-round coordination. Metrics: Task completion rate, efficiency (rounds/tools/tokens), quality (accuracy/completeness), user experience, error handling. Control Variables: Same prompt, tools, token budget, timeout, evaluation standards.

Haiku4.5: Strong design thinking—deep task understanding, strategy planning, structured output, boundary awareness. Limitations: Conservative tool usage, occasional goal forgetting, weaker Chinese adaptation. MiniMax M2.1: Strong execution—proactive tool usage, stable multi-round state, fast response, native Chinese advantage. Limitations: Surface-level task understanding, shallow planning, fluctuating output quality.

Choose Haiku4.5: Demand analysis, content generation (reports), multi-language scenarios, cost-sensitive deployment. Choose MiniMax M2.1: Tool-intensive tasks, Chinese-priority apps, real-time interaction, long-process tasks.

Combining models yields better results:

• Layered: Haiku (planning) + MiniMax (execution)
• Routing: Design tasks → Haiku, execution → MiniMax
• Collaboration: Two Agents complement strengths (increases complexity but boosts capability).

Limitations: Limited vertical domain coverage (medical/legal/finance), subjective metrics (user experience), outdated results as models update, single benchmark limitations. Future: Expand task diversity, add more models (GPT-4/Claude3), automated continuous evaluation, real user feedback validation.

Industry Value: Provides systematic Agent evaluation methodology, highlights 'design vs execution' divide, suggests future Agent systems may need combined specialized models. Conclusion: Haiku excels in design/structure; MiniMax in execution/Chinese. Choose based on scenario needs, not just benchmarks—monitor model updates and validate in real scenarios.