Cost mode:

Category: Structured Data & Fact Extraction · Typical I/O: 3474→890 tokens

Runs on model group: All models. Inputs may reach any model in the group, not only the ranked models below.

Run this task on Fronset — request an invitation

Models

Frontier on this task: GPT-6 Luna at 8.10 / 10. Quality bar at 90%: 7.29.

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.

ModelQuality scoreCI lowCost / 1k runsvs best value
GPT-6 Luna8.10 / 107.75$0.55best value
Tencent Hy37.37 / 106.98$5.7310x more expensive
Thinking Machines Inkling Small7.32 / 106.86$10.2119x more expensive
Qwen 3.8 Flash7.47 / 107.05$15.6629x more expensive
Moonshot Kimi K37.57 / 107.14$58.37106x more expensive
Claude Opus 5.56.57 / 106.07$12.8523x more expensive
Meta Muse Spark 1.36.81 / 106.44$22.2140x more expensive
NVIDIA Nemotron-3 Ultra 550B7.22 / 106.80$8.5416x more expensive
MiniMax M35.92 / 105.58$3.927.1x more expensive
GPT-6 Sol5.93 / 105.43$4.748.6x more expensive

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
GPT-6 Luna ★ best OpenAI8.10 / 10 CI [7.75, 8.46]MEDIUM$0.55best valuebatch
Tencent Hy3 OpenRouter7.37 / 10 CI [6.98, 7.75]MEDIUM$5.7310xbatch
Thinking Machines Inkling Small OpenRouter7.32 / 10 CI [6.86, 7.77]MEDIUM$10.2119xbatch
Qwen 3.8 Flash Alibaba Cloud (DashScope)7.47 / 10 CI [7.05, 7.90]MEDIUM$15.6629xbatch
Moonshot Kimi K3 Moonshot AI7.57 / 10 CI [7.14, 8.00]MEDIUM$58.37106xbatch

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: 3474 input tokens → 890 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 source entailment, atomicity, self-containment, factual checkability, preservation of qualifiers, completeness of material claims, non-duplication, and category fit. Penalize invented context and opinions presented as facts.

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.

{
  "$defs": {
    "ExtractedClaimItem": {
      "description": "A single factual claim extracted from source material.",
      "properties": {
        "claim": {
          "description": "A single factual claim or data point",
          "title": "Claim",
          "type": "string"
        }
      },
      "required": [
        "claim"
      ],
      "title": "ExtractedClaimItem",
      "type": "object"
    }
  },
  "description": "Structured output from claim extraction LLM call.",
  "properties": {
    "claims": {
      "description": "List of extracted factual claims",
      "items": {
        "$ref": "#/$defs/ExtractedClaimItem"
      },
      "title": "Claims",
      "type": "array"
    }
  },
  "required": [
    "claims"
  ],
  "title": "ClaimExtractionResult",
  "type": "object"
}

Prompt templates

The published template for this task.

default · LLMB_ATOMIC_FACT_CLAIM_EXTRACTION_SYSTEM + LLMB_ATOMIC_FACT_CLAIM_EXTRACTION_USER (78341 calls in window)

System prompt

Extract only objectively checkable claims directly stated by the source. Make each claim self-contained by resolving local pronouns and abbreviations only when the source makes the resolution unambiguous. Preserve entities, dates, units, quantities, comparisons, uncertainty, and scope. Exclude opinions, instructions, promotional statements, methodology-only remarks, and duplicate or compound claims. Use only supplied categories; return uncategorized when no category fits. 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 — chapter_descriptions: {chapter_descriptions}; summary_text: {summary_text}. Use only these inputs to complete the task defined by the system prompt.