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TSK-1 Benchmark: Which AI Model Builds the Best App?

Use Claude for finish and follow-up edits, GPT when a long brief must land word for word, DeepSeek for design and workspace depth, and Kimi for the fewest missteps. TSK-1 Auto can choose. Every ranked result is a working Taskade Genesis app we opened, filled in, and asked to change. Compare models or build an AI app. What is TSK-1? →

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TSK-1 model benchmark

ModelInterfaceTaskMemoryAdaptTSK-1 Score
  • Leading
  • Strong
  • Emerging
  • Limited
  • Not scored
Updated September 2026
Interfaceready to shareTaskfollows your requestMemorykeeps your dataAdapthandles follow-up edits

The app request is held constant within each comparison. Working apps only. We test the result, saved data, and follow-up edits. Benchmark history Meet TSK-1 →

Start building

Describe the app you need. TSK-1 coordinates the rest.

Benchmark updates

Plain-language takeaways from every published test. Open any update to read what changed.

Real-customer shapes and follow-up edits

Three real-customer app shapes joined the set: a sales CRM, a fleet inspection, and an AI governance office. The first full follow-up-edit ladder passed on every model that ran it.

A broad model sweep and parallel helpers

Seventeen models built the same client sign-up form. A separate test of parallel helpers showed that one file per helper produced the cleanest result.

Same request, four setups

A control run told us how big a difference has to be before it counts. Two identical setups produced noticeably different costs for the same app, so we now only claim a difference when it is larger than that gap, and we grade on what the app does.

Same result after a platform update

We re-ran the automatic model after a large platform update. The apps came out the same, with fewer failed build actions along the way.

Sixteen builds, five models, identical prompts

Five models built and edited the same apps from identical prompts. GPT-5.6 Luna was the value pick on every app and GPT-5.6 Terra the quality pick. Most builds now keep a checklist of the request inside the app.

Cleaner follow-up edits

Follow-up edits now start from a map of the app, and they landed cleaner. GPT-5.6 Luna edited apps other models had built without breaking them, and a missing page now explains itself.

Two new model families, three new app shapes

Qwen and Grok entered the benchmark, and three real-customer app shapes joined the test set: a sales CRM, a field-inspection audit, and an AI governance office. Every model still gets the same words.

A dedicated follow-up-edit test

We added a dedicated follow-up-edit pass and stricter instruction checks. On its first run, none of the 31 edit attempts finished - a strong first build is not enough on its own; an app must also change cleanly and stay within the brief.

Faster builds, verified results

A complete app finished in under five minutes without follow-up. We also rechecked every published result and established a baseline for automatic model selection.

Accuracy and value moved together

The strongest results reproduced a long brief exactly, balanced build quality with efficiency, and explained their design choices clearly.

Lower cost did not mean lower quality

An efficient build completed a long form at the lowest measured cost, while the leading result also saved every submitted field correctly.

Long briefs became a harder test

Several builds followed the brief word for word, and one handled a 60-field form without cutting requirements.

Working software became the baseline

We made a running app the entry requirement. Attractive results that failed to open no longer received a score.

Efficiency without tradeoffs

The cleanest test also delivered the lowest measured cost, showing that careful model selection can improve both quality and value.

Brief fidelity and saved data

The strongest builds matched every requested field, stored submitted data correctly, and remained useful from form to follow-up.

Prompt length changed the outcome

Shorter instructions improved brief matching and edits, but sometimes reduced visual quality. The benchmark now balances all four dimensions.

Nine models, one consistent standard

Nine models built the same app. Several produced polished designs, but only working results qualified for comparison.

Self-checking improved reliability

The most dependable results detected and repaired problems before finishing, while weaker builds reported success too early.

Restraint mattered

The strongest designs followed the brief without adding unwanted gates or steps. Doing only what was asked became part of quality.

Compare two models

Each comparison puts the same app request to both models and carries the TSK-1 result for either side.

Claude vs ChatGPTDeepSeek vs ChatGPTDeepSeek vs ClaudeGemini vs ClaudeGemini vs DeepSeekGemini vs GPTGLM vs ClaudeGLM vs DeepSeekGLM vs GPTGPT vs ClaudeGrok vs ClaudeGrok vs DeepSeekGrok vs GPTKimi vs ClaudeKimi vs DeepSeekKimi vs GeminiKimi vs GLMKimi vs GPTMiniMax vs KimiOpus vs SonnetQwen vs ClaudeQwen vs DeepSeekQwen vs GLMQwen vs Kimi

For how Taskade picks a model for you, the speed-to-depth rungs, and pinning one exact model, read the model documentation.

FAQ

Which AI model is best for building apps?

It depends on what you are building. Some models hold closer to a brief, some finish faster, some look more polished. In Taskade you do not have to choose: TSK-1 Auto handles the default, and you can pick a model yourself when you want more control.

What can Taskade Genesis build with TSK-1?

Taskade Genesis can turn one request into CRMs, dashboards, portals, forms, trackers, and internal tools. TSK-1 coordinates the AI model, workspace memory, agents, and automations so the result can store information, answer questions, run workflows, and keep improving.

What does the TSK-1 Benchmark measure?

Whether a model can turn one prompt into a complete, working app. We grade four qualities of a living system: Interface, Task, Memory, and Adaptation — how finished it feels, how closely it follows your request, whether it keeps your data, and how cleanly it handles follow-up changes.

How is TSK-1 different from Vibe Code Bench, WebDev Arena, and Design Arena?

Those benchmarks grade code, a web page, or crowd votes on a screenshot. TSK-1 grades a running app: we open it, enter data the way a customer would, check that the data was kept, run its automation, and ask for a follow-up change. A build that looks right but loses your data does not score.

Does TSK-1 benchmark app builders or AI models?

AI models. Every model builds inside the same app builder, Taskade Genesis, from the same request, word for word, so the builder is held constant. A control run in August showed that two identical setups can still differ, so we only call out a difference between models when it is larger than that gap.

Do you test the apps or just the code?

We test the apps. Every build gets opened and used. We fill in its form the way a customer would, submit it, then check that the answers landed in the right place. An app that looks beautiful and loses your data fails.

Which models can I use in Taskade?

Taskade gives you 15+ frontier models from OpenAI, Anthropic, and open-weight providers, all on one subscription. TSK-1 Auto handles the default, and you can choose a model yourself when you want more control.

How often is this updated?

We run a new test whenever a notable model ships. Every result cleared for public comparison stays on this page, including the ones that did not go well.

What is TSK-1?

TSK-1 is the intelligence behind Taskade Genesis. It brings AI models, workspace memory, agents, and automations together so one request can become a working app. The TSK-1 Benchmark shows how that process performs on real app builds.

Can I try the same app request?

Yes. Every model in a comparison receives the same request, word for word. Describe the same idea in Taskade Genesis, choose a model or let TSK-1 Auto decide, and see what it builds.

Are the benchmarked apps publicly available?

Not yet one by one. We are listing every finished benchmark build under a single official creator so you can open and clone it. Until then, each model page explains what that model produced, and the App Kits on this page are live systems you can open and clone today.

How many builds are behind each result?

Each result comes from a small set of hands-on app builds. The TSK-1 Score rescales four evidence tiers to 0-100 for easier comparison; it is not 100 separate checks. Models with the same result share a rank.