Best LLMs for Factual Claim Refinement
Reviews extracted factual claims and either minimally refines them into self-contained, verifiable statements or drops them when they are non-factual, unsupported, duplicate, or not useful. Illustrative uses include refining audit observations, customer-interview findings, softwa
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Models
Frontier on this task: GLM-5.3 at 8.63 / 10. Quality bar at 90%: 7.77.
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 | 8.62 / 10 | 8.34 | $0.48 | best value |
| Qwen 3.7 Plus | 8.32 / 10 | 8.12 | $3.40 | 7.1x more expensive |
| Tencent Hy3 | 8.27 / 10 | 7.99 | $4.61 | 9.6x more expensive |
| Thinking Machines Inkling Small | 8.31 / 10 | 8.02 | $6.34 | 13x more expensive |
| GPT-6 Sol | 8.25 / 10 | 7.86 | $8.19 | 17x more expensive |
| NVIDIA Nemotron-3 Ultra 550B | 7.88 / 10 | 7.45 | $8.46 | 18x more expensive |
| GLM-5.3 Flash | 8.32 / 10 | 7.94 | $11.42 | 24x more expensive |
| Meta Muse Spark 1.3 | 8.42 / 10 | 8.09 | $22.58 | 47x more expensive |
| Thinking Machines Inkling | 8.05 / 10 | 7.61 | $24.88 | 52x more expensive |
| Tencent Hy4 Preview | 8.10 / 10 | 7.62 | $44.12 | 92x more expensive |
| GLM-5.3 | 8.63 / 10 | 8.22 | $158.39 | 329x more expensive |
| Claude Sonnet 5.5 | 7.73 / 10 | 7.24 | $7.18 | 15x more expensive |
| Gemini 3.5 Flash Lite | 7.46 / 10 | 7.12 | $1.13 | 2.3x more expensive |
| NVIDIA Nemotron-3 Super 120B | 7.26 / 10 | 6.80 | $1.82 | 3.8x more expensive |
| NVIDIA Nemotron-3 Nano 30B-A3B | 7.67 / 10 | 7.41 | $1.47 | 3x more expensive |
| MiniMax M3 | 7.45 / 10 | 7.12 | $4.31 | 8.9x more expensive |
Cost breakdown
| Model | Quality | Confidence | Cost / 1k runs | Overpay | Mode |
|---|---|---|---|---|---|
| GPT-6 Luna ★ OpenAI | 8.62 / 10 CI [8.34, 8.90] | HIGH | $0.48 | best value | batch |
| Qwen 3.7 Plus Alibaba Cloud (DashScope) | 8.32 / 10 CI [8.12, 8.53] | HIGH | $3.40 | 7.1x | batch |
| Tencent Hy3 OpenRouter | 8.27 / 10 CI [7.99, 8.55] | HIGH | $4.61 | 9.6x | batch |
| Thinking Machines Inkling Small OpenRouter | 8.31 / 10 CI [8.02, 8.60] | HIGH | $6.34 | 13x | batch |
| GPT-6 Sol OpenAI | 8.25 / 10 CI [7.86, 8.64] | MEDIUM | $8.19 | 17x | batch |
| NVIDIA Nemotron-3 Ultra 550B OpenRouter | 7.88 / 10 CI [7.45, 8.30] | MEDIUM | $8.46 | 18x | batch |
| GLM-5.3 Flash Z.AI | 8.32 / 10 CI [7.94, 8.69] | MEDIUM | $11.42 | 24x | batch |
| Meta Muse Spark 1.3 OpenRouter | 8.42 / 10 CI [8.09, 8.75] | MEDIUM | $22.58 | 47x | batch |
| Thinking Machines Inkling OpenRouter | 8.05 / 10 CI [7.61, 8.49] | MEDIUM | $24.88 | 52x | batch |
| Tencent Hy4 Preview OpenRouter | 8.10 / 10 CI [7.62, 8.58] | MEDIUM | $44.12 | 92x | batch |
| GLM-5.3 best Z.AI | 8.63 / 10 CI [8.22, 9.05] | MEDIUM | $158.39 | 329x | 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: 3324 input tokens → 1050 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 $/1M list rates — this is what running the task costs, not a per-token price.
Evaluation rubric
Judge semantic preservation, improved self-containment, minimality of edits, correct drop decisions, source support, qualifier preservation, and reason accuracy. Penalize stylistic rewrites that alter meaning.
Output schema
Every answer on this task is checked against this JSON Schema, whichever model wrote it. An answer that doesn't fit counts as a model failure, and the call is retried on another model.
{
"$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 refinement LLM call.",
"properties": {
"claims": {
"description": "Refined claims that passed quality review",
"items": {
"$ref": "#/$defs/ExtractedClaimItem"
},
"title": "Claims",
"type": "array"
}
},
"required": [
"claims"
],
"title": "ClaimRefinementResult",
"type": "object"
}Prompt templates
The published template for this task.
default ·
LLMB_FACTUAL_CLAIM_REFINEMENT_SYSTEM +
LLMB_FACTUAL_CLAIM_REFINEMENT_USER
(76113 calls in window)
System prompt
Preserve the original meaning and evidence boundary. Refine only to add an unambiguous subject, expand a source-defined abbreviation, restore a necessary qualifier, or resolve a local reference. Never add a fact from general knowledge. Drop claims that cannot be made atomic and self-contained without inference, and provide the schema-defined reason. 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 — summary_text: {summary_text}; chapter_descriptions: {chapter_descriptions}; claims_list: {claims_list}. Use only these inputs to complete the task defined by the system prompt.