AI Model Competition Shifts to Efficient Agents and Task Execution

In recent years, the competition among large models has shifted from “who can answer more questions” to “who can complete real work more stably, quickly, and cost-effectively.” In the past, model upgrades were typically reflected in broader knowledge coverage, better response styles, or higher scores on specific tests. However, the new wave of model evolution has gradually pivoted toward complex task decomposition, tool invocation, code execution, continuous planning, and result validation.

From this perspective, determining whether a model has achieved a true generational leap requires more than looking at a single score on a promotional page. It must be tested within real workflows: Can it understand long-term goals? Can it maintain context consistency across multiple steps? Can it identify and correct its own errors? Can it accomplish the same or even more complex tasks with fewer outputs under limited computational resources?

1. The Core of Model Competition Has Shifted from Answering Questions to Completing Tasks

Traditional Q&A tasks usually follow a simple two-phase process: understanding the question and generating an answer. However, in scenarios such as software development, research analysis, data processing, and security auditing, a complete task often involves multiple consecutive steps:

1. Understanding natural language requirements;
2. Decomposing goals and constraints;
3. Formulating an execution plan;
4. Querying resources or invoking external tools;
5. Generating intermediate results;
6. Adjusting the plan based on feedback;
7. Validating the final results;
8. Outputting deliverables.

A deviation in any of these steps can lead to the failure of the entire task. Therefore, a model’s actual capability should not be measured solely by whether it answers a specific question correctly, but by its overall completion rate in long-chain tasks.

Recent public evaluations show that new-generation flagship models have demonstrated significant improvements in complex tasks across various professional fields. In a long-chain test covering dozens of professional directions, a flagship model scored over 50 points, whereas previously strong mainstream models scored around 40 points. The significance of this gap lies not in the score itself, but in the model’s ability to sustain task goals and reduce directional errors during multi-round reasoning processes.

What is more noteworthy is that leading models do not necessarily need to activate maximum-intensity reasoning modes to match or exceed the high-intensity performance of other models. This indicates a potential structural improvement in the model’s internal reasoning efficiency, task planning, and error control, rather than a simple increase in computational volume.

2. The Key to Coding Capability is Not Generating Code, but Completing the Engineering Loop

In coding scenarios, merely generating a runnable snippet is no longer sufficient to prove a model’s engineering capability. Real software tasks typically involve existing codebases, complex dependencies, implicit constraints, and multiple debugging iterations. The model needs to do more than “write a piece of code”:

• Read project directories and module relationships;
• Understand existing interfaces and data structures;
• Locate the root cause of a fault;
• Modify multiple interrelated files;
• Run tests and analyze failures;
• Handle boundary conditions;
• Ensure modifications do not break existing functionality.

In a comprehensive evaluation for code agents, a flagship model scored around 80 points, while another mainstream model scored around 77 points. On the surface, this gap might seem marginal. However, when analyzed alongside token usage, execution time, and economic cost, the conclusion is entirely different: the former consumed about half the tokens of the latter, significantly reduced the time to complete tasks, and incurred lower overall invocation costs.

This suggests that evaluating code models cannot rely solely on “success rate.” A more reasonable metric set should include at least four dimensions:

1. Task Success Rate

Whether the model ultimately achieved the goal, whether tests passed, and whether the modified program meets the requirements. This is the most fundamental metric.

2. Resource Consumption

This includes input and output tokens, the number of tool calls, context length, and extra computation during reasoning. Lower resource consumption facilitates large-scale deployment.

3. Task Latency

For interactive development tools, wait times directly impact the developer experience. Even if two models have similar final success rates, the one with faster responses is more suitable for continuous coding and auto-repair.

4. Modification Stability

Whether the model repeatedly modifies the same location, easily introduces new defects, or breaks other modules while fixing an issue—these factors determine if it is ready for production environments.

Therefore, when evaluating code models, enterprises should prioritize using real codebases, real test suites, and real fault samples, rather than just relying on algorithmic problems or isolated code snippets.

3. Long-Chain Engineering Tasks Better Reflect Model Differences

In real engineering, the most difficult part is often not a single step, but the connection between multiple steps. For instance, a defect repair task might require the model to read logs, locate the call chain, modify the implementation, add tests, and finally run a complete validation process.

These types of tasks impose three requirements on the model.

First is context retention. The model must remember the goals and constraints established early on and cannot forget the initial conditions during the latter half of the task.

Second is plan execution. When facing large tasks, the model needs to break down the steps first, then dynamically adjust based on intermediate results, rather than outputting a large amount of unverified content all at once.

Third is result self-checking. After generating code, the model must use tests, static checks, or runtime results to judge whether the solution is effective. If a failure is detected, it must re-locate the problem based on the error information, rather than mechanically repeating the original solution.

Public long-chain engineering test results show that new-generation flagship models have achieved higher scores in real codebase operations, cross-file modifications, and multi-round debugging. Such progress is more valuable to enterprises because it directly corresponds to practical work like software maintenance, automated testing, defect repair, and R&D efficiency improvements.

4. Research, Security, and Computer Operation are Becoming New Capability Divides

Beyond programming, differences in model capabilities are also reflected across various professional domains.

In research scenarios, models need to understand professional concepts, compare different hypotheses, organize evidence chains, and clearly distinguish between known facts, inferred conclusions, and uncertain information. An excellent model should not just provide a seemingly reasonable answer, but should also explain the basis of the conclusion, its scope of application, and potential counterexamples.

In cybersecurity scenarios, models need to identify abnormal behaviors, analyze logs, understand attack paths, and provide risk mitigation recommendations. Because security tasks are highly sensitive to false positives and negatives, the model must not only “discover problems” but also strive to provide verifiable judgment basis.

In browser and computer operation scenarios, models face dynamic interfaces, permission restrictions, page changes, and multi-step interactions. It must be able to observe the current state, select appropriate operations, and re-plan if an operation fails. This is fundamentally different from static text Q&A and is much closer to an agent capable of perceiving its environment and taking action.

From comprehensive tests across multiple directions, flagship models often achieve leading or near-leading positions in more categories while reducing token consumption and execution time. This indicates that the value of model upgrades is reflected not only in “answering more correctly” but also in “accomplishing more work with fewer steps.”

5. Look Beyond Benchmark Scores: Enterprises Should Focus on Cost Per Effective Task

One of the most overlooked aspects in model evaluation is the relationship between cost and efficiency.

Assume Model A has a task success rate of 80% and consumes an average of 10,000 tokens, while Model B has a success rate of 78% but consumes only an average of 3,000 tokens. In small-scale testing, the difference between the two might seem negligible. However, when a system executes hundreds of thousands of tasks daily, the differences in resource overhead, response latency, and concurrency capacity become massive.

Therefore, a more practical measure can be established using “Cost per Effective Task”:

Cost per Effective Task
= Total Invocation Cost ÷ Number of Successfully Completed Tasks

Additionally, you can comprehensively consider:

• Average task completion time;
• Average number of tool calls;
• Number of retries after failure;
• Proportion of human intervention;
• Review cost of output results;
• Stability of long-context tasks.

For businesses such as customer service assistance, code generation, data analysis, and automated office work, a model’s true value is not how stunning a single response is, but whether it can continuously reduce labor costs and maintain stable quality after large-scale deployment.

6. How to Establish a Reliable Model Selection Process

Enterprises can follow these steps when selecting a model:

First, build a test set that closely mirrors real business scenarios. The test set should include common tasks, complex tasks, abnormal inputs, and boundary conditions, rather than just cherry-picking easy questions.

Second, distinguish between different task types. Knowledge Q&A, code repair, document summarization, data extraction, and multi-step automation all require different model capabilities. A single score cannot cover all scenarios.

Third, record comprehensive operational metrics. Beyond final correctness, you should also save input length, output length, execution time, tool invocation processes, error types, and manually modified content.

Fourth, implement tiered deployment. Complex tasks can utilize more capable flagship models; ordinary tasks like classification, summarization, and format conversion can use balanced models; large-scale, low-risk tasks can opt for lower-cost models. This ensures effectiveness while controlling the overall budget.

Finally, establish a continuous evaluation mechanism. Model performance can be affected by changes in prompts, knowledge bases, tool interfaces, and business data. Therefore, regular sampling, regression testing, and manual review are still necessary post-launch. If necessary, you can integrate general computing, storage, logging, and monitoring capabilities provided by Baidu AI Cloud to build a complete evaluation and operation loop, though the specific solution should still be designed according to your business scale and data security requirements.

7. Conclusion: The End Goal of Model Upgrades is Higher Task Completion Efficiency

The competition among new-generation models is no longer a simple race for parameter scale or short-term leads in single benchmark scores. Truly valuable upgrades must be reflected simultaneously in four aspects: better completion of complex tasks, more stable reasoning processes, faster execution speed, and lower cost per unit task.

For developers, this means models can take on more code reading, debugging, and testing work. For enterprises, it means agents have the opportunity to move from the demo stage into actual production workflows. For platform builders, it means simultaneously focusing on model capabilities, tool orchestration, permission control, log auditing, and result evaluation.

Therefore, in the face of any claim that a “new model comprehensively surpasses” others, one should not just look at a pretty score. A more reliable approach is to return to real tasks, observe whether the model can stably complete work under limited resources, and use quantifiable metrics to verify whether it genuinely brings about productivity improvements. Only when capability, speed, cost, and reliability improve simultaneously does a large model upgrade possess true engineering value.