Best AI models for writing text: 2026 comparison

Short answer to "which model should I use for writing": for a long structured article take a flagship (Claude Opus 5, ChatGPT-5.6 Sol, Gemini 3.1 Pro), for a stream of short copy take a light model (Claude Haiku 4.5, GLM 4.7, DeepSeek V4 Flash), and when the text needs verifiable facts and links, take a model with web search from the Perplexity Sonar line. The gap between those three groups is bigger than the gap inside any one of them. Below: what actually separates the models, what a working prompt looks like, and where they lie predictably.

What really separates text models from each other

Every large text model writes clean, grammatical English. The difference shows up in four places.

• Holding structure. Flagships keep a plan across 8-10 thousand characters. Light models drift after the third or fourth section.
• Density. Heavier models respect a character limit better and are more willing to swap an opinion ("this is very convenient") for a fact.
• Following prohibitions. "No evaluative adjectives" is an obedience test. Light models forget the constraint halfway through the answer.
• Access to fresh data. A regular model works from training memory; web search exists only in models built for it.

Reasoning mode is a separate axis: models like Kimi K2 Thinking or Qwen 3.8 Max build an internal plan first and write after. On a simple post that is wasted spend; on a hard analytical section the difference is visible.

Which model for which type of text

• Article, long-form, multi-page document. Claude Opus 5, ChatGPT-5.6 Sol, Gemini 3.1 Pro, Kimi K3.
• The working middle: emails, proposals, instructions. Claude Sonnet 5, ChatGPT-5.6 Terra, Gemini 3.7 Flash, GLM 5.2.
• A stream of short copy: posts, product descriptions, headlines. Claude Haiku 4.5, GLM 4.7, DeepSeek V4 Flash, ChatGPT-5.6 Luna, Gemma 4.
• Text with facts, numbers and links. Perplexity Sonar, Sonar PRO, Sonar Deep Research.
• Reading a file or an image before writing. Gemini 3.1 Pro, ChatGPT-5.5, DeepSeek V4 Pro, Qwen 3.8 Flash.

If you would rather not keep this in your head, describe the task in plain words to the Molecule model and it picks the tool itself. The wider map of models by task type is on the Moleculs.ai overview page.

Text models and what a run costs

Run cost is in subscription credits as of February 2026; the current number is always shown next to the model.

[[МОДЕЛИ: Perplexity | Choosing a model before you send the prompt]]

Long text: articles, long-form, website sections

The main mistake is asking for everything at once. "Write a 10,000-character article" almost always returns evenly spread mush: one idea per section and three paragraphs of filler around it.

A different order works. First ask for the outline only: 6-8 sections with one line about each. Edit the outline by hand, then ask for two or three sections per prompt, pasting the approved outline into every message. The structure stays yours, the model does not lose context, and edits stay local. Fiction and scripts follow the same logic, except the outline is a scene-by-scene beat sheet instead of a section list.

The tell that a model is out of its depth on length: paragraphs like "thus, the choice depends on many factors" start appearing near the end. That means the outline is gone and the model is filling space. Move to a heavier model or cut the chunks smaller.

Short text: posts, emails, descriptions, headlines

Here a flagship is unnecessary and often harmful: it expands into paragraphs what should be three lines. What matters:

• A hard length limit. Not "keep it short" but "up to 600 characters", "headline under 60 characters".
• A number of variants. Ask for 5-7 versions at once and pick, instead of editing one.
• A sample of your own style. Paste two of your old posts: "match this rhythm and vocabulary". That does more than describing tone in words.

For emails, add the thread context and the goal: "I need a one-week deadline extension without admitting the delay is mine". With no goal the model writes polite nothing.

Text with facts and links: when you need web search

A regular text model invents links. It generates a plausible URL because its job is to continue text, not to check an address. Real domain, sensible slug, no page there.

The rule is simple: anything verifiable (a number, a date, a rule, someone else's study) is written by a model with search. Perplexity Sonar for single facts, Sonar PRO for real-time search across several sources, Sonar Deep Research for a topic review with citations. The order: collect facts with a search model, open the links and read them, then hand the verified points to a regular model for writing. Mixing research and writing in one prompt goes badly - some of the facts will still come from the model's memory.

Where the cost of a mistake is high, a model will give you structure and phrasing, but check rules and requirements against the primary source. That applies to legal, medical and financial text without exception.

How to write a prompt you will not have to rewrite

A working prompt is five lines, and each one closes its own class of errors:

ROLE: you are an editor of a corporate blog writing for practitioners.
TASK: an article about choosing a CRM for a 10-30 person company.
    Reader: a business owner with no IT department, picking a first CRM.
FORMAT: 4000 characters, 6 sections with subheads, paragraphs of 2-4
    sentences, one concrete criterion or example per section.
CONSTRAINTS: no intros about technology or the market; no evaluative
    adjectives (convenient, modern, powerful); do not name prices or
    systems I did not give you; do not invent statistics.
ORDER: first show the 6-section outline and wait for my "ok",
    then write two sections per answer.
Copy

• Role sets the vocabulary: without it the model writes in an averaged marketing voice.
• Task with an audience removes half the filler.
• Format in characters and sections is the only way to get predictable length.
• Constraints should ban specific constructions: "no fluff" means nothing, "no evaluative adjectives" is checkable.
• Order with a pause for "ok" stops the model from writing 4000 characters off a bad outline.

[[СХЕМА: five prompt lines - role, task, format, constraints, order - and what each one fixes in the output | What a prompt looks like when the result needs no rewriting]]

Paste this skeleton into the input field and swap in your own topic.

The workflow: from brief to proofread in five steps

1. A five-line brief on the skeleton above. Fifteen minutes here saves an hour of editing.
2. The outline as a separate prompt on a light model: outlines are cheap, so ask for two or three and assemble one.
3. Facts, if you need them, from a search model. You open the links yourself and write the numbers into a separate list.
4. The text in parts on a heavy model: two or three sections per answer, with the outline and what is already written in context.
5. Proofreading as a second prompt: "cut by 20 percent, remove evaluative adjectives, add nothing". That works better than asking for short text upfront.

[[СКРИНШОТ: /dashboard?type=text&q=Rewrite this text shorter and without clichés | Editing a finished draft with a second prompt]]

The sign you can stop: on the next editing pass you are swapping phrasings for equivalent ones instead of removing anything.

Where models fail reliably and what to check by hand

• Numbers and dates. If you did not supply the number, verify it or delete it.
• Links and sources. Open every one: dead pages turn up even with search models.
• Quotes and attribution. Models cheerfully assign a famous line to the wrong person.
• Your industry's terminology. A synonym often gets substituted that means something else in your field.
• The logic of lists. Items that overlap or split one thing along different axes.
• Clichés. "Plays an important role", "it is worth noting". Search the document for them.
• Legal and medical phrasing. Correct in form, inaccurate in substance.

The same model gives different answers to the same prompt. So save a prompt that worked in your notes instead of hoping to reproduce the result from memory.

How many credits text burns and how to keep the quota

A subscription spends credits, and each model has its own run cost. The gap between a light and a heavy model is tens of times, and that is what decides how many texts your quota covers.

• Draft and utility work on 1-2 credit models: outlines, headline variants, cuts.
• Final writing on a heavy model, but only once the outline is approved.
• Do not re-ask the same thing. Collect your edits into one list instead.
• Do not upload files you do not need. Not every model reads attachments, and parsing a large file for one quote rarely pays off.

You can start on the free plan: the quota is 5 credits, and a single text prompt works even without an account. That is not enough for steady work, but it is enough to run your own brief through a couple of models and see whose style is closer. Credit quotas per plan are on the pricing page.