Best Model for Documentation: Accuracy Is the Only Metric

Documentation is the one coding-adjacent task where a model's greatest strength is also its greatest liability. Producing fluent, confident, well-structured prose is exactly what a language model does best, and it is exactly what makes bad documentation hard to detect.

A wrong function implementation fails a test. A wrong sentence in your API reference sits there for two years, misleading every reader, because nothing executes prose.

The asymmetry that governs everything else

Code has a verifier. Documentation does not. That single asymmetry should drive every decision you make about which model to use and how to constrain it.

The reader of your documentation is, by definition, someone who does not already know the answer. They cannot spot that the described default value is wrong, or that the parameter is optional rather than required, or that the example uses a signature from two versions ago. They will follow it, it will fail, and they will conclude your library is broken.

This means model selection for docs should optimise for one thing above all: how faithfully the model stays within what it was given. Fluency is already sufficient across every model worth considering. Groundedness is not.

Extraction, not generation

The framing that produces good results is to treat documentation as an extraction task. The model is not writing about your API — it is reading your API and reporting what is there.

Practically that means giving it the actual source: the function body, the type signatures, the tests that exercise it, the surrounding module. Then instruct it to document only what is present and to explicitly mark anything it cannot determine rather than filling the gap.

The instruction that does the most work is a permission to say nothing. Something like: if the source does not establish a default value, write that the default is unspecified rather than guessing. Models fill gaps by default because that is what fluent writing looks like; you have to explicitly authorise leaving a hole.

Test each candidate model on this specifically. Hand it a function with a genuinely ambiguous parameter and see whether it invents a plausible description or flags the ambiguity. Models differ substantially here and no benchmark reports it. Why LLMs hallucinate explains the underlying pressure.

Different documentation, different requirements

Lumping all docs together is why teams get inconsistent results. There are at least three distinct jobs.

Reference documentation — parameters, return values, error conditions — is nearly mechanical. The information is all in the source. This is the highest-value and lowest-risk use, and a cheap model does it well. DeepSeek V4 Flash at roughly $0.14 in and $0.28 out per million handles reference generation across a large API surface for very little.

Guides and tutorials require deciding what a reader needs to know and in what order, which is genuinely harder. The model has to model an audience, choose an example that illustrates the point, and sequence concepts. This is worth a stronger model.

Architectural and decision documentation — why the system is shaped this way — is the one you should be most sceptical about. The reasoning lives in people's heads and in old discussions, not in the code. A model reading the code will produce a confident, coherent, invented rationale. Use it to draft structure and have a human supply the substance.

Output tokens dominate the cost

Documentation is one of the few tasks with an inverted token profile. Most coding work reads a lot and writes a little. Docs read a function and produce several paragraphs, so output tokens carry the bill.

That matters because output is priced several times higher than input across every provider. Kimi K3 at $15 per million output against $3 input is a five-to-one ratio; GLM-5.2 at $4.40 against $1.40 is roughly three-to-one. On a task that is mostly output, the headline input price you compared is largely irrelevant.

Run the arithmetic for your actual API surface before choosing. Documenting three hundred functions at eight hundred output tokens each is 240K output tokens — a few dollars on Flash, well over thirty on K3. Input versus output token pricing covers why the two sides diverge so much.

Docs rot faster than code, so plan for regeneration

The reason documentation is a good AI target is not the first generation. It is the regeneration.

Hand-written docs drift because updating them is nobody's priority. If your docs are generated from source, you can regenerate on every release and drift stops being a category of problem. That structural change is worth more than any quality difference between models.

To make it work, keep the generated and hand-written parts in separate files. Regenerate the reference material freely; never regenerate the hand-authored guides, because you will overwrite someone's careful explanation with a mediocre restatement.

Wire it into CI so the check is that generated docs match the current source. Running LLMs in CI pipelines covers making that cheap and non-blocking, and pinning model versions matters here because an unpinned model produces gratuitous diff churn on every run.

What to pick

For reference documentation generated from source at volume, DeepSeek V4 Flash. The task is extraction, the volume is high, and the output-heavy profile makes the cheap tier obviously correct.

For guides, tutorials and anything requiring audience judgement, GLM-5.2. Stronger at structure and sequencing, still reasonable on output price, with reasoning effort levels to spend more only where it helps.

For architectural documentation, use whichever model you like and treat its output as an outline. The facts must come from a human.

Whatever you choose, the process matters more than the model. Ground every request in real source, explicitly authorise the model to say a thing is unspecified, separate generated from hand-written files, and regenerate on release. A cheap model with those constraints produces more trustworthy docs than an expensive one without them.