Cost mode:

Category: Structured Data & Fact Extraction · Rail: absolute · Typical I/O: 3904→744 tokens

Models

Frontier on this task: Gemini 3.5 Flash at 9.38 / 10. Quality bar at 90%: 8.45.

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
DeepSeek V4 Flash8.58 / 108.13$0.70best value
Qwen 3.5 Flash8.77 / 108.39$0.751.1x more expensive
Gemini 3.1 Flash Lite8.61 / 108.14$0.831.2x more expensive
NVIDIA Nemotron-3 Super 120B8.81 / 108.43$1.392x more expensive
MiniMax M39.04 / 108.92$1.432x more expensive
DeepSeek V4 Pro8.93 / 108.61$2.012.9x more expensive
Qwen 3.6 Flash8.52 / 108.34$3.665.2x more expensive
GPT-5.6 Terra8.93 / 108.57$4.866.9x more expensive
Claude Sonnet 58.54 / 108.33$6.499.2x more expensive
Qwen 3.6 Plus8.69 / 108.24$7.1010x more expensive
Gemini 3.1 Pro Preview9.06 / 108.77$7.4511x more expensive
GPT-5.6 Sol9.13 / 108.79$8.1412x more expensive
Gemini 3.5 Flash9.38 / 109.10$9.4013x more expensive
Kimi K2.68.61 / 108.11$15.3122x more expensive
GPT-5.58.46 / 107.97$17.4925x more expensive
Claude Opus 4.88.57 / 108.10$18.7827x more expensive
Qwen 3.7 Plus8.19 / 107.89$1.472.1x more expensive
Grok 4.57.52 / 107.40$7.5711x more expensive

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
DeepSeek V4 Flash DeepSeek8.58 / 10 CI [8.13, 9.03]MEDIUM$0.70best valuebatch
Qwen 3.5 Flash Alibaba Cloud (DashScope)8.77 / 10 CI [8.39, 9.16]MEDIUM$0.751.1xbatch
Gemini 3.1 Flash Lite Gemini8.61 / 10 CI [8.14, 9.08]MEDIUM$0.831.2xbatch
NVIDIA Nemotron-3 Super 120B OpenRouter8.81 / 10 CI [8.43, 9.20]MEDIUM$1.392xbatch
MiniMax M3 MiniMax9.04 / 10 CI [8.92, 9.16]RANKED$1.432xbatch
DeepSeek V4 Pro DeepSeek8.93 / 10 CI [8.61, 9.25]MEDIUM$2.012.9xbatch
Qwen 3.6 Flash Alibaba Cloud (DashScope)8.52 / 10 CI [8.34, 8.69]RANKED$3.665.2xbatch
GPT-5.6 Terra OpenAI8.93 / 10 CI [8.57, 9.29]MEDIUM$4.866.9xbatch
Claude Sonnet 5 Anthropic8.54 / 10 CI [8.33, 8.76]HIGH$6.499.2xbatch
Qwen 3.6 Plus Alibaba Cloud (DashScope)8.69 / 10 CI [8.24, 9.15]MEDIUM$7.1010xbatch
Gemini 3.1 Pro Preview Gemini9.06 / 10 CI [8.77, 9.36]HIGH$7.4511xbatch
GPT-5.6 Sol OpenAI9.13 / 10 CI [8.79, 9.47]MEDIUM$8.1412xbatch
Gemini 3.5 Flash best Gemini9.38 / 10 CI [9.10, 9.67]HIGH$9.4013xbatch
Kimi K2.6 Moonshot AI8.61 / 10 CI [8.11, 9.10]MEDIUM$15.3122xbatch
GPT-5.5 OpenAI8.46 / 10 CI [7.97, 8.95]MEDIUM$17.4925xbatch
Claude Opus 4.8 Anthropic8.57 / 10 CI [8.10, 9.05]MEDIUM$18.7827xbatch

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: 3904 input tokens → 744 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.

SEC_S1_TOC_SYSTEM_PROMPT + SEC_S1_TOC_USER_PROMPT (1061 calls in window)

System prompt

You are an expert at analyzing SEC filings, particularly S-1 registration statements. 

Your task is to find and extract the table of contents from the provided S-1 filing text. Look for:

1. A formal "TABLE OF CONTENTS" or "INDEX" section
2. Chapter/section listings with titles and page numbers
3. Standard S-1 sections like:
   - PART I: INFORMATION REQUIRED IN PROSPECTUS
   - Item 1. Business
   - Item 1A. Risk Factors
   - Item 2. Use of Proceeds
   - Management's Discussion and Analysis
   - Financial Statements
   - PART II: INFORMATION NOT REQUIRED IN PROSPECTUS
   - Signatures
   - Exhibit Index

Extract the chapter titles exactly as they appear, along with any page numbers provided. If no formal table of contents exists, look for section headers throughout the text and identify the main sections.

Provide your analysis as a structured JSON object.

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

User prompt

Analyze the following S-1 filing text and extract the table of contents information:

**S-1 Filing Text (First 50,000 characters):**
```
{sample_text}
```

**Instructions:**
1. Look for a table of contents section in the text
2. Extract all chapter/section titles and their page numbers
3. If no formal TOC exists, identify major section headers throughout the text
4. Focus on investment-relevant sections like Business, Risk Factors, Financial Data, etc.
5. Provide the exact titles as they appear in the document

**JSON Output Format:**
The required JSON output schema is provided in the system prompt.