Cost mode:

Category: Infrastructure & Utility · Typical I/O: 2800→3588 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: 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.

ModelQuality scoreCI lowCost / 1k runsvs best value
GPT-6 Luna8.43 / 108.18$0.80best value
Xiaomi MiMo V2.6 Pro7.98 / 107.54$1.121.4x more expensive
Tencent Hy38.27 / 107.99$4.395.5x more expensive
Qwen 3.7 Plus8.30 / 108.10$5.677.1x more expensive
Thinking Machines Inkling Small8.31 / 108.02$6.177.7x more expensive
NVIDIA Nemotron-3 Ultra 550B7.83 / 107.41$7.138.9x more expensive
GPT-6 Sol8.20 / 107.83$8.7611x more expensive
GLM-5.3 Flash8.31 / 107.93$11.3914x more expensive
Meta Muse Spark 1.38.43 / 108.10$19.1524x more expensive
Thinking Machines Inkling8.05 / 107.61$20.4726x more expensive
Tencent Hy4 Preview8.06 / 107.60$45.3557x more expensive
GLM-5.38.63 / 108.22$158.12198x more expensive
Gemini 3.5 Flash Lite7.46 / 107.12$1.141.4x more expensive
NVIDIA Nemotron-3 Super 120B7.26 / 106.80$1.812.3x more expensive
NVIDIA Nemotron-3 Nano 30B-A3B7.67 / 107.42$1.852.3x more expensive
MiniMax M37.46 / 107.14$3.854.8x more expensive

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
GPT-6 Luna ★ OpenAI8.43 / 10 CI [8.18, 8.67]HIGH$0.80best valuebatch
Xiaomi MiMo V2.6 Pro Xiaomi MiMo7.98 / 10 CI [7.54, 8.43]MEDIUM$1.121.4xbatch
Tencent Hy3 OpenRouter8.27 / 10 CI [7.99, 8.55]HIGH$4.395.5xbatch
Qwen 3.7 Plus Alibaba Cloud (DashScope)8.30 / 10 CI [8.10, 8.51]HIGH$5.677.1xbatch
Thinking Machines Inkling Small OpenRouter8.31 / 10 CI [8.02, 8.59]HIGH$6.177.7xbatch
NVIDIA Nemotron-3 Ultra 550B OpenRouter7.83 / 10 CI [7.41, 8.25]MEDIUM$7.138.9xbatch
GPT-6 Sol OpenAI8.20 / 10 CI [7.83, 8.56]MEDIUM$8.7611xbatch
GLM-5.3 Flash Z.AI8.31 / 10 CI [7.93, 8.69]MEDIUM$11.3914xbatch
Meta Muse Spark 1.3 OpenRouter8.43 / 10 CI [8.10, 8.76]MEDIUM$19.1524xbatch
Thinking Machines Inkling OpenRouter8.05 / 10 CI [7.61, 8.49]MEDIUM$20.4726xbatch
Tencent Hy4 Preview OpenRouter8.06 / 10 CI [7.60, 8.53]MEDIUM$45.3557xbatch
GLM-5.3 best Z.AI8.63 / 10 CI [8.22, 9.04]MEDIUM$158.12198xbatch

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: 2800 input tokens → 3588 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 (80421 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.