Best AI model for frontend design

Every flagship shipping today writes frontend code that works. Claude Fable 5.2, Claude Opus 5.5, GPT-6 Astra, Gemini 4 Argon and Kimi K3.1 Pro all produce accessible markup, keyboard focus states, container queries and a reduced-motion branch without being asked for any of it.

So the interesting question is not which one codes better. It is which one returns an interface somebody designed, and that answer does not live in the model at all. It lives in whether a token file arrived with the request.

The short call:

• Long build across many files: Claude Opus 5.5, first on Design Arena's Overall Agentic board at the time of writing.
• One component against a spec: Claude Haiku 5.5 or Gemini 3.8 Flash.
• Top of each lineup: Claude Fable 5.2 and GPT-6 Astra.
• The values that make the component look art-directed: written once, in the Thanor library, and pasted into whichever model you use.

Three interfaces whose every value was written down before a model saw them:

The ceiling is your token file#

A component has about fourteen decisions in it, and a typical prompt names none of them. Radius, border color, the ramp the padding comes off, the hover transform, the duration of that transform, the disabled treatment, the focus ring offset, the shadow or the deliberate absence of one.

Leave them out and the model supplies the category median, every time:

• border-radius: 8px, on everything from the button to the modal.
• box-shadow: 0 1px 3px rgba(0,0,0,0.1), the Tailwind default, unchanged.
• transition: all 0.2s ease, which animates properties you did not intend.
• A 1px border in a gray with no relationship to the surface behind it.

None of that is wrong code. All of it is unowned code, and a designer reading the result can tell inside two seconds, because the values agree with the framework rather than with each other.

The clearest way to see it is to open two components from the same session. The button transitions on all 0.2s, the card on transform 0.3s, and nothing in the file says which one was intended. Two durations that close to each other read as a mistake rather than as a decision, because that is what they are.

The fix is boring. Write fourteen values down once, keep them in a file (design tokens in the W3C format are one way), and paste them above every request. The model stops guessing and starts executing, which is the only behavior change worth chasing. That is the same mechanism as the gap between a paid prompt and a free component: one carries decisions, the other carries markup.

What a frontend brief has to name#

Seven groups, and a page of text covers all of them.

The last row is the one that separates a mock from an interface. Six states per interactive element, named as a requirement, and the model builds all six because you asked for six. Ask for "a polished button" and you get hover.

Radius is the cheapest tell to fix. A 2px control next to a 14px card is a decision somebody made; 8px on both is the framework talking.

The spacing ramp is the one people leave out most often and miss most. With a fixed set of six values and a rule that nothing may sit off it, every gap in the interface becomes a choice between six options rather than a free number, and the vertical rhythm holds across screens built weeks apart. Without it you get 18px here and 22px there, both defensible, neither related.

Model by model, on frontend work#

The tiers differ by how much they can hold, not by how well they write a flex container.

Holding a long build together#

Claude Opus 5.5, released September 22, keeps a token file, a component inventory and the existing code in view at once in its 1M token window, and at the time of writing it leads Design Arena's Overall Agentic board, where a coding agent builds across many files. The practical effect is consistency: component nineteen still uses the 240ms response curve from component one, because component one is still in the window.

Volume work on a written spec#

Claude Haiku 5.5, Gemini 3.8 Flash and DeepSeek V4.1 Flash are the volume tiers, and an exact value leaves them nothing to invent. Twenty small components against a token file is the job they are for, and the saved budget buys a second iteration.

Everything in between#

Claude Sonnet 5.5, released September 28, is the default for a page at a time. Claude Fable 5.2, released November 9, is Anthropic's model for demanding reasoning and long-horizon work, and GPT-6 Astra, released September 3, leads OpenAI's lineup with GPT-6.1 Sol, GPT-6.1 Terra and GPT-6 Luna underneath it. Google's flagship is now Gemini 4 Argon, with Gemini 3.8 Flash as its newest Flash.

Pick one and stay there for a project. Switching mid-build puts consistency at risk more than quality: two models given the same token file agree, and two models given a half-remembered conversation do not.

Where the models actually diverge#

Three places, and none of them is design taste.

1. Output length. Most current flagships list about 1M tokens of context, so the live difference is output: 128K tokens per response for the current Claude models and GPT-6 Astra, up to 1M for Gemini 4 Argon. Nothing about the CSS improves; a long single file just stops being cut off before the footer.
2. Instruction density tolerance. Hand a model forty explicit constraints and some tiers are more likely to drop the ones near the end. The flagships hold more of them, which is why a dense brief is worth a dense model.
3. Refactoring an existing file. Editing code you did not write rewards reasoning depth more than generating a greenfield component does.

Notice what is absent from that list. Color sense, type judgment, spacing rhythm, motion feel: models do differ there, but only in their defaults, and Design Arena's blind votes put those differences at a few votes in a hundred near the top. Once the brief names the values, they are copied from the brief, and the defaults stop mattering.

That is worth saying plainly, because the upgrade cycle invites the opposite belief. A new release raises the floor of the code and leaves the design exactly where your prompt left it. The same pattern shows up in every launch this cluster covers.

There is a practical test. Keep one component as a fixture: a card with your radius, your border, your ramp padding, your 240ms response curve and all six states. On the day a new model ships, paste the token file and ask for that card. If the fourteen values come back intact, the release changes nothing about your design work, and you have learned it in four minutes.

The verdict for frontend work#

Use Claude Opus 5.5 for a build you want held in one context, and a cheap tier for anything you can specify tightly enough to hand over in one message. With the same token file in front of both, most of what separates them is speed and cost.

The order of operations matters more than the choice. Write the tokens, build one component, check all six states by keyboard, then batch the rest against that component as the reference. Doing it the other way round, generating twenty components and reconciling them afterwards, costs more time than the tier ever saves.

Ranked by how much each choice actually changes the interface:

• The token file: everything.
• The spacing ramp: whether screens built weeks apart share a rhythm.
• The state list: the difference between a mock and a working control.
• The refusal list, meaning what this design will not do: the reason two projects off one library look unrelated.
• The model: a workflow preference.

Thanor writes those files: exact values per design, from the palette in hex to the easing curves and every interaction, with the media the page uses, for personal and client work. Free accounts open the designs marked free, and the plans open the rest.

Open Vesperion and copy its prompt; it is free with an account. Read it as a token file before you decide whether a model upgrade was the thing you needed: the values in it do not expire when the next model ships, and the rest of the library is written the same way.