Best LLMs for Research Region Identification

Category: Structured Data & Fact Extraction · Typical I/O: 1098→1810 tokens

Run this task on Fronset — request an invitation

Models

Frontier on this task: Moonshot Kimi K3 at 8.84 / 10. Quality bar at 90%: 7.95.

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.

Model	Quality score	CI low	Cost / 1k runs	vs best value
Claude Sonnet 5	8.77 / 10	8.59	$3.82	best value
Thinking Machines Inkling Small	8.50 / 10	8.29	$5.06	1.3x more expensive
Thinking Machines Inkling	8.39 / 10	8.17	$13.09	3.4x more expensive
Moonshot Kimi K3	8.84 / 10	8.63	$17.12	4.5x more expensive
Gemini 3.5 Flash Lite	5.32 / 10	4.97	$0.40	90% cheaper
Gemini 3.5 Flash	7.27 / 10	6.98	$3.53	7% cheaper

Cost breakdown

Model	Quality	Confidence	Cost / 1k runs	Overpay	Mode
Claude Sonnet 5 ★ Anthropic	8.77 / 10 CI [8.59, 8.95]	RANKED	$3.82	best value	batch
Thinking Machines Inkling Small OpenRouter	8.50 / 10 CI [8.29, 8.72]	HIGH	$5.06	1.3x	batch
Thinking Machines Inkling OpenRouter	8.39 / 10 CI [8.17, 8.60]	HIGH	$13.09	3.4x	batch
Moonshot Kimi K3 best Moonshot AI	8.84 / 10 CI [8.63, 9.04]	HIGH	$17.12	4.5x	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: 1098 input tokens → 1810 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 evidence-based regional relevance, ranking, coverage versus focus trade-off, allowed-set compliance, and restraint against unsupported regions. Cardinality and identifier validity are deterministic.

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": "Output model for region identification",
  "properties": {
    "reasoning": {
      "default": "",
      "description": "Explanation for region selection",
      "title": "Reasoning",
      "type": "string"
    },
    "region_codes": {
      "description": "List of relevant region codes",
      "items": {
        "type": "string"
      },
      "title": "Region Codes",
      "type": "array"
    }
  },
  "required": [
    "region_codes"
  ],
  "title": "RegionIdentificationOutput",
  "type": "object"
}

Prompt templates

The published template for this task.

RESEARCH_REGION_IDENTIFIER_SYSTEM + RESEARCH_REGION_IDENTIFIER_USER (286 calls in window)

System prompt

You are an expert market analyst specializing in geographic market identification.

Your task is to identify the most relevant geographic regions for researching a specific subject.

Consider:
1. Where is the subject primarily headquartered or based?
2. What are the subject's primary markets?
3. Where does the subject have significant operations?
4. Where would authoritative information be published?
5. What regulatory jurisdictions are most relevant?

Output a JSON object with:
{{
  "region_codes": ["US", "UK", ...],
  "reasoning": "Brief explanation of why these regions were selected"
}}

Select 1-4 most relevant regions. Prioritize quality over quantity.

User prompt

Identify the most relevant geographic regions for researching: {subject_name}

Subject Code: {subject_code}
Subject Type: {subject_type}
Subject Description: {subject_description}

Available Regions:
{available_regions}

Additional Context:
{additional_context}