Comparing LLMs for Chinese Creative Writing: What Script Generation Reveals About Planning and Expression

1. Defining Chinese Writing Ability Through Practical Tasks

Comparisons of large language models’ Chinese writing ability often remain too abstract. Asking which model “writes better Chinese” can easily become a matter of preference unless the task and output requirements are clearly defined. A polished response in a single conversation turn tells us less than a model’s ability to produce consistently usable text for a long-form creative assignment with constraints, narrative pacing, and coherent characters.

Consider the workflow for writing short-form screenplays. A complete script typically moves through several stages: developing the premise, defining characters, outlining episodes, expanding individual episodes, and writing dialogue. Throughout this process, a model must do more than understand instructions. It must retain earlier information and generate new material within established constraints.

At a minimum, Chinese creative writing ability should therefore be assessed across four dimensions:

1. Structured planning: Can the model break a broad premise into a coherent episode outline and maintain progression between episodes?
2. Context adherence: Do dialogue and scene descriptions remain consistent with established character traits and emotional tone?
3. Detail and redundancy control: Within an episode of roughly 800 Chinese characters, can the model create vivid scenes without relying on stock phrases or empty transitions?
4. Revision requirements: How much work separates the generated draft from usable copy? This gap directly affects the human editing workload.

2. How Models Differ in Practical Screenwriting Work

Feedback from screenwriters suggests that not every model is equally suitable as a primary scriptwriting tool. Although general conversational capabilities continue to improve, models differ noticeably in the specific task of turning an outline into a script.

For some short-form dramas, writers need a clear scene rhythm: establish conflict within the first three lines, build emotion through dialogue in the middle, and end with a hook. This requires the model to manage three interconnected elements at once: plot causality, character relationships, and the audience’s emotional trajectory.

One unnamed platform model, referred to in the source feedback only as “G,” is described by some users as a leading option for this task. Its reported strength is maintaining control over long contexts. Given a storyboard-style brief of 200–300 Chinese characters, it can separate the requirements into scene actions and dialogue instructions while maintaining a consistent perspective. Several users report that no more than about 20% of an episode draft typically requires rewriting. These are practitioner reports rather than results from a controlled comparison.

Another model, known for conversational interaction, is described as strong in logical reasoning and information synthesis. It handles complex settings and overlapping character motivations well. However, its atmospheric descriptions and conversational dialogue can sometimes feel overly polished, lacking the texture of everyday speech. This does not mean it cannot write effective scripts. Rather, longer tasks that extend an existing outline may require more human guidance to produce consistent results.

Model versions also matter. Earlier versions often relied on predictable completions: when a plot point resembled a familiar pattern in the training material, the model tended to choose a safe continuation, making the story feel conventional. More recent versions are reported to follow individualized creative requirements more closely. Instructions that specify an emotional objective or an explicit prohibition—such as “do not make the male lead appear angry here”—can help the model execute the writer’s intent more accurately.

3. Practical Methods for Evaluating Chinese Creative Writing

A useful evaluation framework should go beyond general impressions. Three approaches can make comparisons more concrete.

3.1 Instruction-Following Tests

Create writing tasks with multiple constraints. For example:

Write a confrontation between two people on a rainy night. Character A must keep their emotions under control, while Character B gradually breaks down. Do not use direct emotion words such as “sad” or “angry.”

Checking whether the output satisfies every requirement provides a practical measure of how accurately the model interprets and applies instructions.

3.2 Structural Consistency Stress Tests

Ask the model to generate multiple episodes sequentially from the same outline. Then examine whether later episodes preserve important details established earlier.

This test helps reveal deterioration in retained context and the model’s ability to retrieve relevant information from earlier material. Both are central to reliable long-form writing.

3.3 Human Revision Cost

Have experienced screenwriters review the drafts and record the proportion of characters that need editing per 1,000 Chinese characters.

Although this approach requires more time and expertise, it closely reflects production conditions: a draft’s value depends heavily on how much work is needed to make it usable.

Models will not necessarily rank in the same order across all three tests. A model may produce an impressive standalone scene but forget important details in later episodes. Another may be less reliable at interpreting a very long brief, yet produce highly usable text once it has understood the requirements.

4. Choosing an LLM for Creative Writing

Creators should choose tools according to their actual workflow rather than general claims about model quality. Three considerations are especially important.

• Prioritize context capacity and retention. Chinese scriptwriting depends on continuity. If a model has forgotten a line of foreshadowing from episode one by the time it writes episode ten, elegant prose alone will not make it a dependable production tool.

• Test negative constraints. In creative work, what a character must not do can be as important as what they should do. Models that reliably follow instructions such as “do not,” “avoid,” and “never” offer greater control over the result.

• Evaluate targeted revision. Good writing rarely emerges in one pass. Test whether the model can rewrite a specified passage in response to local feedback without disrupting the surrounding structure. This capability has a substantial effect on the day-to-day editing experience.

5. Where Chinese Creative Writing Models May Develop Next

Further progress is unlikely to come simply from a broader vocabulary. Three areas stand out as potential directions for development.

Deliberate Control of Narrative Pacing

Models may become better at adjusting dialogue rhythm and information density to factors such as the target audience’s age and viewing context.

Multimodal Creative Collaboration

Alongside script text, models may generate storyboard sketches or visual mood boards, helping writers translate verbal descriptions into a visual language.

Interactive Character Consistency

Persistent, creator-defined character profiles could be loaded across writing tasks to help maintain a consistent voice and characterization.

Over the longer term, the most valuable role for LLMs may be as parallel brainstorming assistants rather than replacements for screenwriters. Their contribution is to reduce repetitive formatting and drafting work so that creators can devote more time to narrative decisions and emotional expression.

6. Conclusion

Differences between models’ Chinese writing abilities become most visible in the practical demands of complex creative tasks, not simply in grammatical fluency. When screenwriters find one model more useful than another, they are often responding to differences in long-form planning, context retention, and adherence to constraints.

For teams exploring AI-assisted writing, a more productive approach than debating which model is “smarter” is to define the required output, build a task-specific evaluation set, and test models within the real production workflow. The key question is not whether a model can produce an impressive passage, but whether it can repeatedly deliver material that moves a project closer to completion.