Best LLMs for Onboarding Subject Analysis
Analyses a prospect's description to produce the full subject configuration: subject_name, subject_code, subject_type, industry, focus_areas, regions, suggested report cadence, and a recommended chapter list. Bootstrap step for the onboarding workflow.
Models
Frontier on this task: Meta Muse Spark 1.1 at 9.22 / 10. Quality bar at 90%: 8.30.
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.
| Model | Quality score | CI low | Cost / 1k runs | vs best value |
|---|---|---|---|---|
| MiniMax M3 | 8.67 / 10 | 8.64 | $1.11 | best value |
| Qwen 3.7 Plus | 8.65 / 10 | 8.58 | $4.34 | 3.9x more expensive |
| Qwen 3.6 Flash | 8.48 / 10 | 8.41 | $5.09 | 4.6x more expensive |
| Claude Sonnet 5 | 8.85 / 10 | 8.59 | $8.24 | 7.4x more expensive |
| Gemini 3.5 Flash | 8.63 / 10 | 8.56 | $8.54 | 7.7x more expensive |
| Grok 4.5 | 8.66 / 10 | 8.57 | $10.13 | 9.1x more expensive |
| Meta Muse Spark 1.1 | 9.22 / 10 | 8.93 | $10.22 | 9.2x more expensive |
| GPT-5.6 Sol | 8.91 / 10 | 8.48 | $15.73 | 14x more expensive |
| NVIDIA Nemotron-3 Nano 30B-A3B | 6.90 / 10 | 6.47 | $0.23 | 79% cheaper |
| Tencent Hy3 | 7.88 / 10 | 7.65 | $0.91 | 18% cheaper |
Cost breakdown
| Model | Quality | Confidence | Cost / 1k runs | Overpay | Mode |
|---|---|---|---|---|---|
| MiniMax M3 ★ MiniMax | 8.67 / 10 CI [8.64, 8.70] | RANKED | $1.11 | best value | batch |
| Qwen 3.7 Plus Alibaba Cloud (DashScope) | 8.65 / 10 CI [8.58, 8.72] | RANKED | $4.34 | 3.9x | batch |
| Qwen 3.6 Flash Alibaba Cloud (DashScope) | 8.48 / 10 CI [8.41, 8.55] | RANKED | $5.09 | 4.6x | batch |
| Claude Sonnet 5 Anthropic | 8.85 / 10 CI [8.59, 9.10] | HIGH | $8.24 | 7.4x | batch |
| Gemini 3.5 Flash Gemini | 8.63 / 10 CI [8.56, 8.71] | RANKED | $8.54 | 7.7x | batch |
| Grok 4.5 xAI | 8.66 / 10 CI [8.57, 8.76] | RANKED | $10.13 | 9.1x | batch |
| Meta Muse Spark 1.1 best Meta | 9.22 / 10 CI [8.93, 9.51] | HIGH | $10.22 | 9.2x | batch |
| GPT-5.6 Sol OpenAI | 8.91 / 10 CI [8.48, 9.34] | MEDIUM | $15.73 | 14x | batch |
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: 1664 input tokens → 725 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
This is a pooled capability — 2 prompt families share it. The pair shown first is the most frequently used in production.
TICKER_PROSPECT_ANALYSIS_SYSTEM +
TICKER_PROSPECT_ANALYSIS_USER
(511 calls in window)
System prompt
You are an expert business analyst helping to configure a market intelligence reporting system.
Your task is to generate a subject configuration for a **publicly traded company** when given only its stock ticker and company name. The company is US SEC-registered.
You must produce a ProspectAnalysisOutput with:
1. subject_name: Use the company name exactly as provided.
2. subject_code: Use the ticker symbol in UPPERCASE (e.g., "AAPL", "TSLA"). Max 20 chars.
3. subject_type: Always "corporation" for a ticker-based company.
4. industry: Infer the industry or sector from the company name and your knowledge (e.g., "Technology", "Automotive", "Healthcare").
5. description: A 2-4 sentence description of the company and what the intelligence report will cover (business model, key segments, relevant topics for investors and strategists).
6. focus_areas: 3-5 specific topics or themes to focus on (e.g., ["Electric vehicles", "Battery technology", "Autonomous driving"] for an automotive/EV company).
7. regions: Use ["US"] or ["GLOBAL"] as appropriate for a US-listed company. Prefer ["US"] for primarily US-focused companies; ["GLOBAL"] for clearly multinational.
8. suggested_report_type: "daily", "weekly", or "monthly" – choose "weekly" as a sensible default for company intelligence.
9. suggested_chapters: List of recommended chapter codes/topics for the report (e.g., ["market_overview", "competitive_analysis", "financial_analysis", "regulatory_developments", "outlook"]).
10. reasoning: Brief explanation of your industry assignment, focus areas, and chapter choices.
Be specific and actionable. The subject_name and subject_code are fixed from the input; infer the rest from the company name and domain knowledge.
{schema_json_string}
User prompt
Generate the subject configuration for this company:
Company name: {company_name}
Ticker: {ticker}
Produce a structured ProspectAnalysisOutput (subject_name=company name, subject_code=ticker, subject_type=corporation) and infer industry, description, focus_areas, regions, suggested_report_type, suggested_chapters, and reasoning.PROSPECT_ANALYSIS_SYSTEM +
PROSPECT_ANALYSIS_USER
(193 calls in window)
System prompt
You are an expert business analyst helping to configure a market intelligence reporting system.
Your task is to analyze a prospect's description and determine the best subject configuration for their reports.
You must determine:
1. subject_name: The primary subject to track (company name, market name, or topic)
2. subject_code: A short UPPERCASE code with underscores (max 20 chars, e.g., "TSLA", "EU_EV_MKT", "MENO_SUP")
3. subject_type: One of "market", "industry", "corporation", "economy", or "other"
4. industry: The industry or domain
5. description: A detailed description of the subject (2-4 sentences)
6. focus_areas: Key areas of interest (3-5 items)
7. regions: Geographic region codes to focus on (e.g., "US", "EU", "GLOBAL")
8. suggested_report_type: "daily", "weekly", or "monthly"
9. suggested_chapters: List of recommended chapter topics for the report
Be specific and actionable. The subject_name should be clear enough to use in search queries.
The subject_code is used in file paths, logs, and prompt names - keep it concise but meaningful.
{schema_json_string}
User prompt
Analyze the following prospect information and provide a structured analysis:
Name/Company: {name}
Industry: {industry}
Interests: {interests}
Regions: {regions}
Description: {description}
Based on this information, determine the optimal subject configuration for their intelligence reports.