Cost mode:

Category: Content Summarization & Synthesis · Typical I/O: 7185→13088 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 Sol at 8.52 / 10. Quality bar at 90%: 7.67.

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 Luna7.96 / 107.58$2.71best value
NVIDIA Nemotron-3 Super 120B7.96 / 107.68$3.231.2x more expensive
Gemini 3.5 Flash Lite8.14 / 107.80$5.001.8x more expensive
Xiaomi MiMo V2.6 Pro7.92 / 107.65$5.191.9x more expensive
DeepSeek V4.1 Flash7.85 / 107.54$5.221.9x more expensive
Gemini 3.8 Flash7.94 / 107.71$12.284.5x more expensive
Meta Muse Spark 1.37.98 / 107.72$31.1511x more expensive
GPT-6 Sol8.52 / 108.30$36.6213x more expensive
Claude Sonnet 5.58.20 / 107.95$38.1114x more expensive
Tencent Hy4 Preview7.95 / 107.63$59.4522x more expensive
Claude Opus 5.58.36 / 108.16$77.4929x more expensive
Grok 4.77.74 / 107.39$129.0748x more expensive
Qwen 3.8 Max8.12 / 107.79$176.3565x more expensive
Moonshot Kimi K38.14 / 107.71$190.9070x more expensive
NVIDIA Nemotron 3.5 Lightning6.33 / 105.87$4.271.6x more expensive
Claude Haiku 5.57.59 / 107.30$2.0425% cheaper
Qwen 3.8 Flash7.49 / 107.01$20.737.6x more expensive
Xiaomi MiMo V2.6 Flash7.18 / 106.70$1.3252% cheaper

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
GPT-6 Luna ★ OpenAI7.96 / 10 CI [7.58, 8.35]MEDIUM$2.71best valuebatch
NVIDIA Nemotron-3 Super 120B OpenRouter7.96 / 10 CI [7.68, 8.25]HIGH$3.231.2xbatch
Gemini 3.5 Flash Lite Gemini8.14 / 10 CI [7.80, 8.48]MEDIUM$5.001.8xbatch
Xiaomi MiMo V2.6 Pro Xiaomi MiMo7.92 / 10 CI [7.65, 8.18]HIGH$5.191.9xbatch
DeepSeek V4.1 Flash DeepSeek7.85 / 10 CI [7.54, 8.17]MEDIUM$5.221.9xbatch
Gemini 3.8 Flash Gemini7.94 / 10 CI [7.71, 8.18]HIGH$12.284.5xbatch
Meta Muse Spark 1.3 OpenRouter7.98 / 10 CI [7.72, 8.24]HIGH$31.1511xbatch
GPT-6 Sol best OpenAI8.52 / 10 CI [8.30, 8.74]HIGH$36.6213xbatch
Claude Sonnet 5.5 Anthropic8.20 / 10 CI [7.95, 8.46]HIGH$38.1114xbatch
Tencent Hy4 Preview OpenRouter7.95 / 10 CI [7.63, 8.26]MEDIUM$59.4522xbatch
Claude Opus 5.5 Anthropic8.36 / 10 CI [8.16, 8.57]HIGH$77.4929xbatch
Grok 4.7 xAI7.74 / 10 CI [7.39, 8.08]MEDIUM$129.0748xbatch
Qwen 3.8 Max Alibaba Cloud (DashScope)8.12 / 10 CI [7.79, 8.44]MEDIUM$176.3565xbatch
Moonshot Kimi K3 Moonshot AI8.14 / 10 CI [7.71, 8.56]MEDIUM$190.9070xbatch

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: 7185 input tokens → 13088 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 entailment, atomicity, verifiability, specificity, preservation of qualifiers, source coverage, and non-duplication. Penalize inference beyond the source, compound claims, and claims with no precise evidence anchor.

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": {
    "BenchClaimItem": {
      "description": "One factual claim distilled from a benchmark slice or journal page.",
      "properties": {
        "cited_figures": {
          "description": "Every figure or proper noun the claim depends on (e.g. '3.2x', '$0.42', '31%', 'Claude Haiku 4.5'), copied VERBATIM from the source data provided. Do not paraphrase or round these — they are checked against the source, and any figure not present verbatim invalidates the claim.",
          "items": {
            "type": "string"
          },
          "title": "Cited Figures",
          "type": "array"
        },
        "claim_kind": {
          "default": "",
          "description": "Short archetype tag: one of 'category_leader', 'cost_efficiency', 'cost_delta', 'batch_savings', 'threshold_sensitivity', 'upset', 'best_in_class', 'journal_finding', or '' if none fit.",
          "title": "Claim Kind",
          "type": "string"
        },
        "claim_text": {
          "description": "A single self-contained factual sentence a social-media reply could cite verbatim. Specific, defensible, plain — no marketing language, no hashtags, no call to action.",
          "title": "Claim Text",
          "type": "string"
        },
        "cost_mode": {
          "anyOf": [
            {
              "type": "string"
            },
            {
              "type": "null"
            }
          ],
          "default": null,
          "description": "'batch' or 'sync' if the claim is specific to one cost mode; omit (null) otherwise.",
          "title": "Cost Mode"
        },
        "quality_bar": {
          "anyOf": [
            {
              "type": "integer"
            },
            {
              "type": "null"
            }
          ],
          "default": null,
          "description": "The quality bar (%) this claim is about, e.g. 80 or 95. Omit (null) if the claim spans bars (e.g. 'cheapest at 80% but drops out at 95%').",
          "title": "Quality Bar"
        }
      },
      "required": [
        "claim_text"
      ],
      "title": "BenchClaimItem",
      "type": "object"
    }
  },
  "description": "LLM output: the few most interesting claims for one slice.",
  "properties": {
    "claims": {
      "description": "The most interesting, genuinely quotable claims for this slice (aim for 3). Prefer surprising or counterintuitive facts over obvious ones. Empty list if the slice has nothing worth claiming.",
      "items": {
        "$ref": "#/$defs/BenchClaimItem"
      },
      "title": "Claims",
      "type": "array"
    }
  },
  "title": "BenchClaimBatch",
  "type": "object"
}

Prompt templates

The published template for this task.

default · LLMB_EVIDENCE_GROUNDED_CLAIM_GENERATION_SYSTEM + LLMB_EVIDENCE_GROUNDED_CLAIM_GENERATION_USER (0 calls in window)

System prompt

Generate only claims directly entailed by the supplied source. Each claim must be atomic, self-contained, specific enough to verify, and linked to its supporting evidence. Preserve qualifiers, dates, units, entities, and scope. Exclude opinions, predictions presented as facts, promotional language, and trivial restatements. 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 — slice_kind: {slice_kind}; slice_key: {slice_key}; facts_json: {facts_json}; claims_target: {claims_target}; title: {title}; url: {url}; article_markdown: {article_markdown}; claim_profile: {claim_profile}. Use only these inputs to complete the task defined by the system prompt.