Best LLMs for Atomic Fact Claim Extraction
Extracts atomic, self-contained factual claims from supplied source text and optionally assigns each claim to caller-provided analytical categories. Illustrative uses include extracting factual statements from contracts, customer correspondence, software incident logs, audit work
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.
| Model | Quality score | CI low | Cost / 1k runs | vs best value |
|---|---|---|---|---|
| GPT-6 Luna | 8.10 / 10 | 7.75 | $0.55 | best value |
| Tencent Hy3 | 7.37 / 10 | 6.98 | $5.73 | 10x more expensive |
| Thinking Machines Inkling Small | 7.32 / 10 | 6.86 | $10.21 | 19x more expensive |
| Qwen 3.8 Flash | 7.47 / 10 | 7.05 | $15.66 | 29x more expensive |
| Moonshot Kimi K3 | 7.57 / 10 | 7.14 | $58.37 | 106x more expensive |
| Claude Opus 5.5 | 6.57 / 10 | 6.07 | $12.85 | 23x more expensive |
| Meta Muse Spark 1.3 | 6.81 / 10 | 6.44 | $22.21 | 40x more expensive |
| NVIDIA Nemotron-3 Ultra 550B | 7.22 / 10 | 6.80 | $8.54 | 16x more expensive |
| MiniMax M3 | 5.92 / 10 | 5.58 | $3.92 | 7.1x more expensive |
| GPT-6 Sol | 5.93 / 10 | 5.43 | $4.74 | 8.6x more expensive |
Cost breakdown
| Model | Quality | Confidence | Cost / 1k runs | Overpay | Mode |
|---|---|---|---|---|---|
| GPT-6 Luna ★ best OpenAI | 8.10 / 10 CI [7.75, 8.46] | MEDIUM | $0.55 | best value | batch |
| Tencent Hy3 OpenRouter | 7.37 / 10 CI [6.98, 7.75] | MEDIUM | $5.73 | 10x | batch |
| Thinking Machines Inkling Small OpenRouter | 7.32 / 10 CI [6.86, 7.77] | MEDIUM | $10.21 | 19x | batch |
| Qwen 3.8 Flash Alibaba Cloud (DashScope) | 7.47 / 10 CI [7.05, 7.90] | MEDIUM | $15.66 | 29x | batch |
| Moonshot Kimi K3 Moonshot AI | 7.57 / 10 CI [7.14, 8.00] | MEDIUM | $58.37 | 106x | 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: 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.