Fronset Benchmark

Best LLMs for Profiled Document Section Analysis

Analyzes one document section using supplied document conventions, analytical dimensions, evidence boundaries, and decision context. Illustrative uses include analyzing a contract clause, software specification, vendor requirement, market-risk section, licensing term, research-me

Cost mode:

Category: Financial Analysis & Trading Decisions · Typical I/O: 14299→3571 tokens

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Models

Frontier on this task: GLM-5.3 Flash at 9.12 / 10. Quality bar at 90%: 8.21.

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 does not clear the bar.

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
GPT-5.6 Luna ★ OpenAI8.67 / 10 CI [8.53, 8.81]RANKED$1.07best valuebatch
MiniMax M3 OpenRouter8.87 / 10 CI [8.81, 8.93]RANKED$3.793.5xbatch
GPT-5.4 Nano OpenAI8.23 / 10 CI [8.02, 8.43]HIGH$4.984.7xbatch
Thinking Machines Inkling Small OpenRouter8.73 / 10 CI [8.55, 8.91]RANKED$9.969.3xbatch
Qwen 3.7 Plus Alibaba Cloud (DashScope)8.39 / 10 CI [8.05, 8.72]MEDIUM$11.3111xbatch
GLM-5.3 Flash best Z.AI9.12 / 10 CI [8.94, 9.31]RANKED$13.6013xbatch
Gemini 3.5 Flash Gemini8.59 / 10 CI [8.35, 8.83]HIGH$19.8619xbatch
GPT-5.6 Terra OpenAI8.72 / 10 CI [8.57, 8.87]RANKED$20.7919xbatch
Meta Muse Spark 1.3 OpenRouter8.70 / 10 CI [8.43, 8.97]HIGH$27.8626xbatch
Claude Sonnet 5 Anthropic8.76 / 10 CI [8.43, 9.10]MEDIUM$30.1628xbatch
GPT-5.6 Sol OpenAI8.89 / 10 CI [8.75, 9.04]RANKED$31.1029xbatch
Thinking Machines Inkling OpenRouter8.90 / 10 CI [8.76, 9.03]RANKED$36.5034xbatch
Tencent Hy4 Preview OpenRouter8.90 / 10 CI [8.59, 9.21]MEDIUM$48.5245xbatch
Grok 4.6 xAI8.91 / 10 CI [8.61, 9.22]MEDIUM$50.9848xbatch
Moonshot Kimi K3 Moonshot AI9.03 / 10 CI [8.86, 9.20]RANKED$88.4883xbatch
GLM-5.3 Z.AI8.84 / 10 CI [8.59, 9.09]HIGH$102.8696xbatch

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: 14299 input tokens → 3571 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.

Evaluation rubric

Judge section fidelity, document-profile correctness, quantitative accuracy, materiality under the supplied lens, qualifier preservation, separation of fact and analysis, and recognition of missing context.

Output schema

Every answer on this task is checked against this JSON Schema, whichever model wrote it. An answer that doesn't fit counts as a model failure, and the call is retried on another model.

{
  "description": "Analysis result for a single chunk of a large SEC filing.",
  "properties": {
    "business_insights": {
      "title": "Business Insights",
      "type": "string"
    },
    "competitive_insights": {
      "title": "Competitive Insights",
      "type": "string"
    },
    "financial_insights": {
      "title": "Financial Insights",
      "type": "string"
    },
    "investment_relevance": {
      "title": "Investment Relevance",
      "type": "string"
    },
    "key_information": {
      "title": "Key Information",
      "type": "string"
    },
    "risk_insights": {
      "title": "Risk Insights",
      "type": "string"
    },
    "section_title": {
      "title": "Section Title",
      "type": "string"
    }
  },
  "required": [
    "section_title",
    "key_information",
    "business_insights",
    "financial_insights",
    "risk_insights",
    "competitive_insights",
    "investment_relevance"
  ],
  "title": "SecChunkAnalysis",
  "type": "object"
}

Prompt templates

This task has 3 published templates; the default is shown first.

LLMB_PROFILED_DOCUMENT_SECTION_ANALYSIS_SYSTEM + LLMB_PROFILED_DOCUMENT_SECTION_ANALYSIS_USER (442 calls in window)

System prompt

Analyze only the supplied section. Apply document_profile to interpret terminology, status, and qualifiers and analysis_profile to select material facts, metrics, risks, opportunities, and implications. Preserve whether figures are historical, adjusted, projected, or uncertain. State when a requested topic is outside the section rather than importing it from elsewhere. Treat empty optional values as absent and return only the requested result. Your response must conform exactly to this output schema: {schema_json_string}.

User prompt

Inputs — section_title: {section_title}; section_content: {section_content}; document_profile: {document_profile}; analysis_profile: {analysis_profile}. Use only these inputs to complete the task defined by the system prompt.
SEC_S1_CHUNK_ANALYSIS_SYSTEM_PROMPT + SEC_S1_CHUNK_USER_PROMPT (390 calls in window)

System prompt

You are a senior investment analyst at a long-term focused investment firm. You specialize in analyzing SEC filings, particularly S-1 and S-1/A registration statements for companies going public.

You will be provided with a specific section from an S-1 filing. Your job is to extract the most investment-relevant information from this section and analyze its implications for long-term investors.

Focus on:
- Business model insights and competitive positioning
- Financial performance and metrics
- Risk factors and potential concerns
- Strategic direction and management quality
- Market opportunity and growth prospects

Provide your analysis as a structured JSON object using only information found directly in the section. Do not speculate or add external knowledge.

## 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 section from an S-1 filing: **{section_title}**

**Section Content:**
```
{section_content}
```

**Instructions:**
1. Extract the most important investment-relevant information from this section
2. Focus on insights that would help a long-term investor evaluate this company
3. Identify any business model insights, financial information, risks, or competitive factors
4. Your analysis should be concise but comprehensive
5. Use only information directly stated in the section content

**JSON Output Format:**
The required JSON output schema is provided in the system prompt.
JSON_REPAIR_SYSTEM + JSON_REPAIR_USER (1 calls in window)

System prompt

You are a JSON repair tool. The user gives you malformed or partial model output and a JSON Schema. Return ONLY a single valid JSON object that satisfies the schema, salvaging as much real content from the input as possible. Do not invent data for fields the input doesn't support — use the schema's allowed empty/null values. Output the JSON object only: no prose, no markdown, no code fences.

User prompt

JSON Schema:
{schema_json}

Malformed output to repair:
{raw_text}

Return only the corrected JSON object.