Cost mode:

Category: Long-form Content Generation · Rail: absolute · Typical I/O: 878→1085 tokens

Models

Frontier on this task: GPT-5.6 Terra at 9.82 / 10. Quality bar at 90%: 8.84.

point-estimate floor (CI low) · upper CI (less certain) · Bars sorted by blended cost; best-value model first. Greyed rows are MEDIUM+ models whose point estimate clears the bar but whose CI low does not.

ModelQuality scoreCI lowCost / 1k runsvs best value
Tencent Hy39.06 / 108.86$1.04best value
MiniMax M39.57 / 109.27$1.811.7x more expensive
Qwen 3.7 Plus9.12 / 109.07$1.861.8x more expensive
GPT-5.6 Luna9.62 / 109.26$2.082x more expensive
GPT-5.6 Terra9.82 / 109.50$3.963.8x more expensive
NVIDIA Nemotron-3 Ultra 550B9.26 / 109.14$4.434.2x more expensive
Qwen 3.6 Flash8.88 / 108.78$6.125.9x more expensive
Meta Muse Spark 1.19.14 / 108.81$9.709.3x more expensive
Grok 4.59.34 / 109.32$10.209.8x more expensive
NVIDIA Nemotron-3 Super 120B8.81 / 108.54$0.6835% cheaper

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
Tencent Hy3 OpenRouter9.06 / 10 CI [8.86, 9.26]HIGH$1.04best valuebatch
MiniMax M3 MiniMax9.57 / 10 CI [9.27, 9.87]MEDIUM$1.811.7xbatch
Qwen 3.7 Plus Alibaba Cloud (DashScope)9.12 / 10 CI [9.07, 9.17]RANKED$1.861.8xbatch
GPT-5.6 Luna OpenAI9.62 / 10 CI [9.26, 9.98]MEDIUM$2.082xbatch
GPT-5.6 Terra best OpenAI9.82 / 10 CI [9.50, 10.00]MEDIUM$3.963.8xbatch
NVIDIA Nemotron-3 Ultra 550B OpenRouter9.26 / 10 CI [9.14, 9.38]RANKED$4.434.2xbatch
Qwen 3.6 Flash Alibaba Cloud (DashScope)8.88 / 10 CI [8.78, 8.98]RANKED$6.125.9xbatch
Meta Muse Spark 1.1 Meta9.14 / 10 CI [8.81, 9.47]MEDIUM$9.709.3xbatch
Grok 4.5 xAI9.34 / 10 CI [9.32, 9.36]RANKED$10.209.8xbatch

Overpay shows how much more you pay than the best-value model that clears the quality bar (marked ★) — the best-value good-enough option. "16x" means you overpay 16× — 16× that reference for no quality benefit above the bar. Typical call shape for this task: 878 input tokens → 1085 output tokens, EMA-tracked from production traffic. Cost is the observed, all-in $ per 1,000 task runs: each model's own measured usage on this task — output verbosity, thinking/reasoning tokens, cache reads and writes, and the spend on its billed failures — priced at current list rates and adjusted by the billing overhead we actually reconcile against provider invoices. Models that answer tersely cost what they actually cost; models that think at length pay for it. Not comparable to providers' advertised $/1M list rates — this is what running the task costs, not a per-token price.

Prompt templates

The system + user template pair used for this task.

SECTION_GENERATION_SYSTEM_PROMPT + SECTION_GENERATION_USER_PROMPT (504 calls in window)

System prompt

You are an expert content strategist who organizes publication content into thematic sections for a newsletter landing page.

Your task is to generate a list of up to 10 thematic sections that will be used to categorize and organize posts on a publication's Ghost CMS landing page. Each section groups related posts under a common theme.

Guidelines:
1. Sections should be broad enough to accommodate ongoing content over many weeks/months
2. Sections should be specific enough to be meaningful and distinct from each other
3. Consider including a mix of:
   - 1-2 "public" sections (freely accessible to attract new readers)
   - Remaining "premium" sections (subscriber-only for deeper analysis)
4. Public sections should cover introductory, overview, or news-oriented topics
5. Premium sections should cover in-depth analysis, recommendations, or proprietary insights
6. Section names should be concise (2-5 words) and professional
7. Each section needs a brief description explaining what content belongs there

Output your response as JSON matching the provided schema.

## Required Output Format
Your response MUST be a single, valid JSON object conforming to this schema:
```json
{schema_json_string}
```

User prompt

I publish intelligence briefings about the following:

"{description}"

The publication is called "{subject_name}".

Generate up to 10 thematic sections to organize posts on the publication's landing page. Each section groups related posts under a common theme.

Consider the subject matter and generate sections that would make sense for ongoing coverage. Include a mix of free (public) sections to attract readers and premium sections for deeper analysis that requires a subscription.

{excluded_section_names_block}
The required JSON output schema is provided in the system prompt.