A Six-Minute Landing Page with DeepSeek V4.1 Flash in DSH PTC Mode: Cache Costs and a Two-Turn Caveat

One-sentence takeaway

nehuenpereyra says they used DeepSeek V4.1 Flash with maximum reasoning in DeepSeek Harness's PTC mode to complete a Nura Health landing page in about six minutes with a single task, correctly generating the mobile version as well; the total was 3,422,844 tokens, and the post reports a cost of about $0.072. This was a single field experience, while the comments explain that quota exhaustion made the interface show two turns, so it cannot be treated as a controlled experiment or a stable success rate.

Use cases

• Tasks this can help assess: Generating front-end landing pages with visual design, animation, and responsive layouts in the DSH PTC Agent; observing the scale of long outputs, cache hits, and per-task cost.

• Tasks this should not be generalized to: The success rate, code quality, production readiness, speed ranking, or fair cost comparison with other models across all front-end projects.

• Applicable model version: The original post explicitly calls it DeepSeek V4.1 Flash. The current official API ID is deepseek-flash; the note that the old ID maps to the newer version does not change this post's attribution as a historical V4.1 Flash experience.

• Test environment or client: DeepSeek Harness, official DeepSeek API, PTC mode; target: a Nura Health landing page. The Harness version, machine, network, and tool permissions were not specified.

• Reasoning level and parameters: Maximum reasoning (max reasoning); temperature, top_p, maximum output, timeout, and other parameters were not specified.

Evaluation method

This is a single community field report, not a benchmark. The author did not publish a task set, number of repetitions, preregistered success criteria, independent code review, or comparison model. The visible prompt was not a generic one-line instruction, but a fairly complete design and implementation brief: it specified a Moss, Clay, Cream, and Charcoal color palette, fonts and noise texture, a fixed structure covering navigation, Hero, feature cards, manifesto, protocol, membership, and footer, and required GSAP animations, Lucide icons, real Unsplash assets, responsive implementation, and zero placeholder content. In the comments, the author explicitly said they used no Skills and only the Agent provided by DSH.

The post says the task was completed in a “single attempt”; a later comment explains that two turns in the screenshot resulted from quota exhaustion and should not be interpreted as two independent experiments or two complete reruns.

Key results

The author felt the visual capabilities produced “surprisingly good feedback,” especially because the mobile/responsive version was implemented correctly; the only explicit complaint was the fast token consumption. The page's stack was HTML, native JavaScript, Tailwind CSS, GSAP 3, and Lucide. Comments were mixed: some people thought the page looked like typical AI slop or Claude-generated code, while others recognized its quality and low price; these opinions had no consistent acceptance criteria and can only be treated as community reactions.

The cost figures need to be separated into input and output. The cache-hit rate uses input tokens as its denominator:

3,291,136 ÷ (3,291,136 + 36,963) = 98.89% ≈ 99%

If total tokens are incorrectly used as the denominator, the cached portion is only 3,291,136 ÷ 3,422,844 = 96.15%; it cannot be described as a 99% cache rate for the total volume. Using the project's recorded official pricing basis (cached input $0.006/M, uncached input $0.30/M, output $1.20/M) and applying a 50% off-peak discount, the recalculated cost is approximately $0.07225, close to the $0.072 reported in the post; the post provides no independent bill or itemized pricing window, so this is only a recalculation under that pricing basis.

Raw data

Conclusions and limitations

1. The post supports one clear but narrow signal: in a DSH PTC workflow with a sufficiently specific design brief, a very high cache-hit rate, and maximum reasoning, V4.1 Flash may produce a responsive landing page recognized by the author as successful in about six minutes.

2. The high cached-input share and the off-peak discount pricing assumption can explain the low cost; the original post does not provide the pricing window, so it cannot confirm that the discount actually applied. The output was still 94,745 tokens. The $0.072 figure cannot be carried over directly to a different project, cache prefix, time period, or pricing rule.

3. “A single attempt succeeded” and “two turns” can both be true: the former describes the author's account of task completion, while the latter is the interface record after quota exhaustion. Neither implies a success rate, retry cost, or average duration.

4. The post offers no independent acceptance evidence for whether the page is truly pixel-perfect, whether the animations are accessible, or whether the code is maintainable. The disagreement in comments over “AI slop” also shows that visual-quality judgments are shaped by aesthetics and cannot replace functional testing.

Reproduction notes

To review this field report, fix the model identifier DeepSeek V4.1 Flash, DSH PTC mode, maximum reasoning, and the same design brief, while specifying whether the official API, pricing window, and cache prefix are used. Repeat the task several times at minimum, recording time to first token, total duration, cached input, uncached input, output, actual bill, turn count, and quota interruptions; then define success through mobile breakpoints, interactions, animation cleanup, asset loading, and code tests. The original post does not provide these controls, so this article does not claim to have reproduced it independently.