Comparing Multi-Model Code Generation Efficiency: Engineering Observations and Practical Recommendations from a Video Generation Task

1. Background: Why a Unified Task Is Needed for Side-by-Side Observation

In everyday development, intelligent coding assistants have moved beyond the question of “Can they write code?” to “Can they efficiently complete complex tasks?” What development teams really care about is no longer just whether a model can generate a runnable snippet, but how stable it is across multi-step tasks, how much resource it consumes, how long users wait, and how usable the final output is.

To obtain comparable data, I designed a unified task: multiple mainstream coding assistants were asked to complete the same video generation code. The requirement was explicit: implement a 20-second teardown-style video around the topic “Why claw machines almost never grab anything,” using 3D simulation to illustrate the internal structure. This type of task combines code generation, script organization, parameter tuning, and media output, making it a reasonable reflection of how an assistant performs in a real engineering pipeline.

Each participant had only one execution attempt, with no manual correction or rerun. The goal was to approximate a production-style first-pass success rate rather than an idealized result achieved through repeated tuning.

2. Task Design: Unified Input and Unified Constraints

The task prompt remained identical across all participants. It described only the requirement without specifying an implementation path:

Implement a 20-second video that uses a teardown simulation to explain why claw machines almost never grab anything. The output should be a renderable video file.

This task is not trivial. It requires the model to understand video pacing, script logic, 3D teardown presentation, and final encapsulation into a playable file. Different assistants may choose different technical routes: some use procedural animation, some generate frame sequences, and some call general-purpose media libraries for composition.

Unified constraints included:

• No assistant was allowed to generate multiple versions in advance for selection.
• Default configurations were allowed, but no manual intervention or debugging was permitted.
• Actual completion time, token consumption, and output token volume were treated as hard metrics.
• Video quality was evaluated through manual scoring as a reference metric.

This design cannot fully eliminate capability differences between models, but it makes those differences visible and provides a basis for team-level tool selection.

3. Hard Metric Comparison: Completion Time, Token Consumption, and Cache Efficiency

After the task was completed, I recorded key runtime data for each participant. To remain neutral, the products are identified by letters rather than names, while preserving their technical characteristics.

Participant	Total time (minutes)	Total token processing	Cache hit ratio	Output tokens
A	8	3.50M	97%	28K
B	17	12.15M	96%	130K
C	21	13.94M	96%	81K
D	36	9.50M	98%	59K
E	46	46.44M	99%	281K

3.1 What Completion Time Differences Reveal

Participant A finished in only 8 minutes, while the slowest participant E took 46 minutes, a nearly sixfold difference. This gap cannot simply be attributed to “model speed.” It reflects differences in task decomposition strategy.

Faster assistants tended to take more direct technical paths: quickly determine the video script, generate code, and execute it immediately, with less repeated reasoning and self-checking. Slower assistants tended to perform extra validation at each stage or even regenerate intermediate artifacts, significantly extending total completion time.

In scenarios that require rapid iteration, this difference directly affects developer experience. If every submitted task requires nearly an hour of waiting, even high-quality output becomes less practically usable.

3.2 Cache Hit Ratio and Cost Structure

All five participants achieved cache hit ratios above 96%, which indicates that repeated reads of input-side context dominate the cost of long tasks. The real differentiator was total token processing volume. Participant E processed 46.44M tokens, more than 13 times that of Participant A, but the final video quality did not improve by a proportional factor.

From a cost perspective, total token processing volume is the most direct billing basis. Even if the per-token price is low, a very high total processing volume can produce a higher actual bill. Therefore, when evaluating coding assistants, teams should not compare unit prices alone. They should also consider the total tokens required to complete a task and the usability of the output.

Output token volume also deserves attention. Higher output volume means the model generated more intermediate text, such as extra explanations, self-commentary, or redundant code. Participant E produced 281K output tokens, but the proportion of final effective code may not be high. Development teams should monitor the ratio of effective output to total output and avoid paying for large volumes of ineffective generation.

4. Quality Dimension: Engineering Insights Behind Subjective Evaluation

Video quality is difficult to quantify precisely, but a meaningful ranking can be obtained through consistent scoring dimensions. I evaluated three aspects: content accuracy, visual presentation, and task completeness.

4.1 Content Accuracy

The core purpose of the teardown video was to explain why the claw fails. Some assistants generated smooth animations, but their explanations remained superficial, such as only showing mechanical arm movement without explaining insufficient gripping force or incorrect jaw timing. Better outputs translated the mechanical principle into intuitive visual comparisons, helping viewers quickly understand the key reason.

4.2 Visual Presentation

A 3D teardown simulation requires reasonable camera movement, step-by-step presentation, and pacing control. High-quality videos first showed the overall machine exterior, then progressively disassembled it, and finally focused on key components. Weaker outputs sometimes had chaotic camera movement, illogical teardown order, or subtitles that were out of sync with the visuals.

4.3 Task Completeness

All participants produced a video of about 20 seconds, but there were differences in detail quality. Some videos had visible visual artifacts, while others had language mismatches, such as English narration when the user asked in Chinese. These issues may not affect core functionality, but they reduce the final delivery experience.

The overall quality ranking did not fully align with completion time. The assistant with the highest quality was not the fastest, but its output stability and depth of content were clearly better. This suggests that in engineering tool selection, a single metric is rarely sufficient and should be combined with scenario-specific weighting.

5. Practical Recommendations: Choosing the Right Assistant for Different Tasks

Based on these observations, I distilled four practical recommendations for development teams.

5.1 Stratify by Task Complexity

For short and straightforward code generation tasks, such as script conversion or simple interface implementation, prioritize assistants with short completion time and fast response. These tasks do not require excessive reasoning; quickly returning usable code is enough.

For complex multi-step tasks, such as video generation, full project scaffolding, or cross-module refactoring, choose assistants with more stable output quality and allow sufficient waiting time. More importantly, establish asynchronous execution mechanisms in team workflows so developers are not blocked for long periods.

5.2 Focus on Effective Tokens Rather Than Total Tokens

When evaluating cost, teams should monitor the effective output rate. A practical approach is to record total token consumption, output token volume, and the number of code lines ultimately adopted for each task. After accumulating enough data, teams can calculate the average token cost per effective line of code.

This metric is more practical than comparing unit prices alone. An assistant with a higher unit price but lower total token consumption may be more economical than one with a lower unit price that generates large amounts of redundant content. Teams can also reduce ineffective output through prompt optimization, such as asking the assistant to “provide only the final code without explanatory text.”

5.3 Build a Unified Evaluation Set

A one-off side-by-side comparison is unlikely to produce stable conclusions. Teams should prepare 5 to 10 typical tasks based on their own business characteristics, covering code generation, debugging, refactoring suggestions, and documentation generation. Periodically test candidate assistants against the same evaluation set to observe capability trends.

The evaluation set should include at least one high-complexity task, such as video generation or full functional module implementation. Such tasks expose weaknesses in long-chain reasoning, resource scheduling, and error recovery that simple tasks often fail to reveal.

5.4 Cache Optimization and Context Management

All participants achieved high cache hit ratios, which suggests that most input tokens are used for repeated context reads. Teams can reduce total input volume by streamlining context, avoiding irrelevant file imports, and using more precise prompts. For long tasks, reasonable checkpointing can also help control token growth.

In addition, some assistants support concise modes or different reasoning intensities. In daily use, teams should choose the appropriate mode according to task difficulty to avoid wasting resources on overqualified processing.

6. Next Steps: Establishing a Continuous Observation Mechanism

This single-task comparison provides a useful starting point, but it is not enough to support long-term decisions. I plan to organize a unified task test weekly and gradually expand task type coverage.

In the long run, building an internal benchmark testing framework is a more effective approach. Such a framework can automatically execute test tasks, record key metrics, and generate comparison reports. This allows teams to continuously observe assistant capability evolution without relying on scattered manual testing.

For development teams, coding assistant selection should not be a one-time static decision. It should be continuously evaluated and dynamically adjusted through actual use. Only by integrating evaluation into daily engineering processes can teams turn tools into real productivity instead of staying at the stage of occasional experimentation.