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TSK-1 is a product-focused benchmark from Taskade that asks an AI model to turn one fixed request into a usable application inside Taskade Genesis. Instead of grading a chatbot’s isolated answer, it grades the app a customer receives: its pages and typed fields, saved and calculated data, automations, built-in assistant, visual states, and response to a follow-up change. The method runs each model one to three times per test, keeps failed builds, and scores four qualities—Interface, Task, Memory, and Adapt—on a five-tier scale.
That design makes TSK-1 useful for judging AI app-building reliability, but it is not a universal ranking of model intelligence. Most results rest on very small samples, identical requests can produce different apps, and both the models and Genesis change over time. Read every score with its date, model version, request, and build count.
What is the TSK-1 methodology?
TSK-1 measures whether an AI model can deliver a complete, customer-usable app from one natural-language brief. The benchmark’s unit of grading is the product a customer could use on the day it was tested, not the model’s explanation or the number of generated lines of code.
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Each model receives the same request in Taskade Genesis, an AI app-building environment. The model builds the app one to three times for a test, and evaluators inspect the resulting product. A result therefore includes the interface, database structure, formulas, automations, assistant behavior, and later edits.
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The method has two anchor request types. One asks for a Dota 2 match tracker with hero, result, KDA, duration, and notes fields, plus win-rate and streak dashboards and a premium esports HUD style. The other represents a real customer’s 32-question client sign-up form, including a scoring formula, automation, and eligibility dashboard. The customer’s complete form text is not publicly reproduced.
Requests are frozen and fingerprinted before scored builds. Two outcomes are directly comparable only when the request bytes match. Genesis version, instructions, and other conditions are held constant within a comparison; comparisons made in different weeks are directional because the builder and models may have changed.
How does a TSK-1 test work?
The sequence follows a customer using the finished app rather than an evaluator merely checking whether generation completed.
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- Use a known persona. Evaluators enter a fixed set of inputs whose expected outcome is already known. This makes formulas and eligibility decisions checkable.
- Compare every saved field. A successful submit is not enough. Each stored value is compared with the value entered, including text, selections, dates, and numbers.
- Recalculate derived values. Metrics such as KDA, win rate, streaks, or an eligibility score are independently recalculated and compared with what the app displays.
- Check automation. The evaluator confirms that an automation ran on the correct record and produced the intended result, rather than merely existing in the configuration.
- Question the built-in assistant. One grounded question uses the newly saved record. The answer must reflect that record instead of generic or invented information.
- Request one change. A real edit request is submitted. The evaluator checks that existing data survives and that a genuinely new page or capability appears when requested.
Every build that runs is retained, including incomplete and failed attempts. That prevents a benchmark from showing only a polished success while hiding reliability problems.
What do Interface, Task, Memory, and Adapt measure?
TSK-1 assigns one of five tiers to each quality. The four qualities cover different failure modes, so a visually attractive app cannot compensate for incorrect data or a broken edit.
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Interface
Interface covers completeness and polish across light mode, dark mode, and phone width. It asks whether the pages are understandable and usable, not simply whether a screen exists. Missing navigation, unusable mobile layouts, or unfinished styling lower this dimension.
Task
Task measures fidelity to the frozen brief. Word-for-word requirements matter, as do requested fields, calculations, dashboards, and visual direction. Unrequested extras can hurt when they obscure or replace what the request actually asked for.
Memory
Memory checks whether submitted and derived data remains correct and usable by automations and the built-in assistant. It is the data-integrity dimension: a form can submit successfully yet fail Memory if a field is changed, a formula is wrong, or the assistant cannot retrieve the saved record.
Adapt
Adapt measures a follow-up edit. A strong result adds the requested capability cleanly while preserving existing records and behavior. Data loss, a page that was not really added, or a change that breaks the original workflow indicates weak adaptation.
How is the TSK-1 score calculated?
Each quality receives one of five tiers: Leading, Strong, Emerging, Limited, or Not scored. Those tiers map to four through zero points. The four values are added, then rescaled from a maximum of 16 points to a 0–100 TSK-1 Score.
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| Tier | Points per quality | Meaning |
|---|---|---|
| Leading | 4 | Evidence meets the highest standard for that quality. |
| Strong | 3 | Most requirements work with relatively minor shortcomings. |
| Emerging | 2 | Useful capability is present but important gaps remain. |
| Limited | 1 | Only a small or unreliable portion of the requirement works. |
| Not scored | 0 | The quality cannot receive credit, including an app that fails the opening gate. |
The 0–100 number is a convenience scale, not 100 independent checks. Equal evidence can produce tied scores, and a one-point change in one quality can look more precise than the underlying five-tier judgment warrants.
What were the August 2026 model results?
Taskade’s August 2026 table reported the following scores. They describe the builds and versions assessed at that time, not permanent standings.
| Model | TSK-1 Score |
|---|---|
| Claude | 81 |
| GPT | 81 |
| DeepSeek | 81 |
| Kimi | 75 |
| GLM | 69 |
| Grok | 69 |
| Qwen | 50 |
| Gemini | 44 |
These figures should be read with the test date and build count. A newer model, a Genesis update, a changed prompt, or another run can alter the result. The current benchmark hub lists updates through October 23, 2026, including newer models, additional app shapes, and repeated builds ranging from complete products to builds with gaps.
Is the TSK-1 score reliable?
TSK-1 is informative for the specific question it asks—how reliably a model can build and maintain a real app under controlled conditions—but its small samples limit statistical certainty. The methodology describes one to three builds per model per test; the later hub says most results rely on one build, although some experiments used five.
Identical requests on identical setups have shown noticeable run-to-run variation. A difference between two models should therefore be larger than that observed variation before being treated as meaningful. Close scores are best described as directional evidence, not a definitive ranking.
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The benchmark also has scope limits. It evaluates Taskade Genesis, so results include the behavior of that builder, its data model, and its assistant. They do not establish how the same models perform in another framework, with custom code, or on a different class of application.
What does TSK-1 publish, and what remains private?
Public reporting includes relative cost language, dated result descriptions, public model and version names, and the shape of the customer-style form. Taskade does not publish absolute per-model cost figures across incompatible billing periods, internal test identifiers, provider or infrastructure details, or the customer’s complete 32-question form.
That distinction matters when reproducing a result. A reader can match the public request and procedure, but cannot independently verify private infrastructure settings or recover an unpublished form verbatim.
How should you compare models with TSK-1?
Use the benchmark as a checklist of product risks rather than as a single leaderboard.
- Opening reliability: Did the app load in all required modes and widths?
- Brief fidelity: Were every requested field, page, formula, and design instruction implemented without displacing the core task?
- Data correctness: Did saved values and derived calculations match known inputs?
- Automation and grounding: Did the correct record trigger the automation, and did the assistant answer from that record?
- Responsive quality: Was the interface usable in light, dark, and phone layouts?
- Change safety: Did a follow-up request add the requested capability without damaging existing data?
- Evidence context: What were the model and Genesis versions, test date, request fingerprint, and number of builds?
When two models have similar scores, inspect these axes to find the practical difference. A model with a slightly lower total may be preferable if it preserves records more reliably, while another may produce a better visual interface but fail the assistant check.
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Can you reproduce a TSK-1 benchmark?
You can reproduce the public shape of the method in Taskade Genesis, provided you use the same request text and record the conditions. Exact replication is impossible when private form text or infrastructure details are unavailable, so label your run as a reproduction rather than an official result.
- Copy the request without rewording, and preserve its exact bytes. Record a fingerprint or hash so later readers can confirm it.
- Write down the Genesis version, model name and version, instructions, date, and any other fixed conditions.
- Run the requested number of builds and keep every output, including failures and incomplete apps.
- Open each app in light mode, dark mode, and at phone width. Record whether it loads.
- Enter the known persona data, compare every saved field, and independently recalculate every derived value.
- Trigger the automation and inspect the record it acted on. Ask the assistant one question that can be answered from the newly saved data.
- Submit one concrete change request, then verify that existing data remains intact and the requested page or capability is actually present.
- Publish dated evidence for successes and failures, along with the build count. Do not present a single run as a stable model ranking.
The benchmark hub also describes a user path in which the same request is submitted in Genesis and a model is selected manually or left to TSK-1 Auto. That path can help readers explore the method, but changing the request or conditions breaks strict comparability.
What are the main pitfalls when interpreting TSK-1?
- Confusing a score with precision: A score of 81 is not evidence of 81 separate capabilities.
- Ignoring failed launches: The opening gate means a non-running app cannot be rescued by attractive screens or good prose.
- Checking submission only: A green confirmation does not prove that every field, formula, automation, or assistant answer is correct.
- Comparing different dates as if they were one event: Builder and model updates can change outcomes independently.
- Overreading one build: Random variation can exceed a small gap between models.
- Generalizing beyond Genesis: TSK-1 evaluates an integrated app-builder workflow, not every coding or deployment environment.
The most defensible conclusion is conditional: TSK-1 identifies how models performed on a dated, fixed app-building task under Genesis conditions. It is valuable evidence for choosing a model for similar workflows, but it should be combined with your own requirements, repeated runs, and checks on data integrity.
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How many builds does TSK-1 run for each model?
The methodology specifies one to three builds per model per test. The benchmark hub notes that most published results use one build, while selected experiments used five, so the build count must be read beside every result.
Why does TSK-1 test a phone-width layout?
A customer may access an app on a narrow screen even when the request was written on a desktop. Checking phone width exposes navigation, overflow, and form-layout failures that a desktop-only review can miss.
Does TSK-1 measure the price of using each AI model?
No absolute per-model cost comparison is published because billing periods and provider conditions are not directly compatible. Reports use relative cost language rather than a standardized dollar total.
What happens if a generated app never opens?
Since the August 5, 2026 rule change, that build receives no quality score. The gate was introduced after three of nine builds in an August 1 test failed to run.
Can a TSK-1 result predict performance in another app builder?
Not directly. The score reflects the interaction between a model and Taskade Genesis, including Genesis’s data, automation, and assistant features; another builder can produce different strengths and failures.
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