Cost mode:

Category: Relevance, Classification & Matching · Typical I/O: 1587→722 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: Moonshot Kimi K3 at 9.20 / 10. Quality bar at 90%: 8.28.

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
Claude Haiku 5.58.67 / 108.39$0.30best value
Xiaomi MiMo V2.6 Pro8.76 / 108.28$0.421.4x more expensive
DeepSeek V4.1 Flash8.95 / 108.73$0.732.4x more expensive
Gemini 3.8 Flash8.65 / 108.44$2.578.6x more expensive
GLM-5.3 Flash9.13 / 108.74$4.4315x more expensive
Claude Opus 5.59.17 / 109.03$10.6235x more expensive
Meta Muse Spark 1.38.97 / 108.71$10.6536x more expensive
Thinking Machines Inkling8.50 / 108.06$12.8843x more expensive
Moonshot Kimi K39.20 / 108.93$17.4658x more expensive
Grok 4.78.46 / 108.00$21.5272x more expensive
Tencent Hy4 Preview8.97 / 108.57$24.4982x more expensive
GLM-5.39.18 / 108.88$52.44175x more expensive
GPT-6 Sol7.80 / 107.38$3.0510x more expensive
GPT-6 Luna8.09 / 107.74$0.1455% cheaper
NVIDIA Nemotron-3 Super 120B8.19 / 107.87$2.418x more expensive

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
Claude Haiku 5.5 ★ Anthropic8.67 / 10 CI [8.39, 8.94]HIGH$0.30best valuebatch
Xiaomi MiMo V2.6 Pro Xiaomi MiMo8.76 / 10 CI [8.28, 9.24]MEDIUM$0.421.4xbatch
DeepSeek V4.1 Flash DeepSeek8.95 / 10 CI [8.73, 9.18]HIGH$0.732.4xbatch
Gemini 3.8 Flash Gemini8.65 / 10 CI [8.44, 8.86]HIGH$2.578.6xbatch
GLM-5.3 Flash Z.AI9.13 / 10 CI [8.74, 9.52]MEDIUM$4.4315xbatch
Claude Opus 5.5 Anthropic9.17 / 10 CI [9.03, 9.31]RANKED$10.6235xbatch
Meta Muse Spark 1.3 OpenRouter8.97 / 10 CI [8.71, 9.23]HIGH$10.6536xbatch
Thinking Machines Inkling OpenRouter8.50 / 10 CI [8.06, 8.93]MEDIUM$12.8843xbatch
Moonshot Kimi K3 best Moonshot AI9.20 / 10 CI [8.93, 9.47]HIGH$17.4658xbatch
Grok 4.7 xAI8.46 / 10 CI [8.00, 8.93]MEDIUM$21.5272xbatch
Tencent Hy4 Preview OpenRouter8.97 / 10 CI [8.57, 9.37]MEDIUM$24.4982xbatch
GLM-5.3 Z.AI9.18 / 10 CI [8.88, 9.49]MEDIUM$52.44175xbatch

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: 1587 input tokens → 722 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 identity resolution, evidence quality, date arithmetic, faithful application of the supplied threshold, uncertainty handling, and explanation traceability. Do not judge the policy itself. Schema validity is out of scope.

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": {
    "ReplacementAuthorData": {
      "description": "Data for a deceased replacement author with similar domain expertise.",
      "properties": {
        "biography": {
          "description": "Short bio under 200 chars starting with 'AI research assistant specializing in...'",
          "maxLength": 200,
          "title": "Biography",
          "type": "string"
        },
        "content_domains": {
          "anyOf": [
            {
              "items": {
                "type": "string"
              },
              "type": "array"
            },
            {
              "type": "null"
            }
          ],
          "description": "Content domain names",
          "title": "Content Domains"
        },
        "content_themes": {
          "description": "Keywords for matching (5-10 keywords)",
          "items": {
            "type": "string"
          },
          "title": "Content Themes",
          "type": "array"
        },
        "expertise_areas": {
          "description": "List of expertise areas (3-5 areas)",
          "items": {
            "type": "string"
          },
          "title": "Expertise Areas",
          "type": "array"
        },
        "gender": {
          "anyOf": [
            {
              "type": "string"
            },
            {
              "type": "null"
            }
          ],
          "default": null,
          "description": "Portrait gender: 'male', 'female', or 'neutral'",
          "title": "Gender"
        },
        "inspiration_note": {
          "anyOf": [
            {
              "type": "string"
            },
            {
              "type": "null"
            }
          ],
          "default": null,
          "description": "Internal note about this persona",
          "title": "Inspiration Note"
        },
        "interests": {
          "description": "List of interests",
          "items": {
            "type": "string"
          },
          "title": "Interests",
          "type": "array"
        },
        "name": {
          "description": "Full name of the deceased historical figure with '(AI)' suffix",
          "title": "Name",
          "type": "string"
        },
        "writing_style": {
          "description": "Writing style: 'academic', 'conversational', 'investigative', 'analytical', or 'accessible'",
          "title": "Writing Style",
          "type": "string"
        }
      },
      "required": [
        "name",
        "biography",
        "expertise_areas",
        "content_themes",
        "writing_style"
      ],
      "title": "ReplacementAuthorData",
      "type": "object"
    }
  },
  "description": "Output model for checking if an author persona is SAFE TO USE under\npostmortem-publicity-rights laws (Topic 9 — 100-year threshold).\n\nThe source-of-truth field is `is_safe`. `is_deceased` and `years_since_death`\nare kept for visibility/debugging.",
  "properties": {
    "confidence": {
      "description": "Confidence level: 'high', 'medium', or 'low'",
      "title": "Confidence",
      "type": "string"
    },
    "is_deceased": {
      "description": "True if the person is confirmed deceased",
      "title": "Is Deceased",
      "type": "boolean"
    },
    "is_safe": {
      "description": "True iff is_deceased=True AND years_since_death \u003e= 100. Source of truth for whether to use the persona.",
      "title": "Is Safe",
      "type": "boolean"
    },
    "reasoning": {
      "description": "Brief explanation of the determination",
      "title": "Reasoning",
      "type": "string"
    },
    "replacement": {
      "anyOf": [
        {
          "$ref": "#/$defs/ReplacementAuthorData"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "If is_safe=False, a historical figure deceased ≥100 years with similar domain expertise as replacement"
    },
    "years_since_death": {
      "anyOf": [
        {
          "type": "integer"
        },
        {
          "type": "null"
        }
      ],
      "default": null,
      "description": "Best estimate of years since death (approximate integer); None for living or fictional/unknown",
      "title": "Years Since Death"
    }
  },
  "required": [
    "is_deceased",
    "is_safe",
    "confidence",
    "reasoning"
  ],
  "title": "AuthorLivingCheckResult",
  "type": "object"
}

Prompt templates

The published template for this task.

default · LLMB_PERSONA_POLICY_ELIGIBILITY_CHECK_SYSTEM + LLMB_PERSONA_POLICY_ELIGIBILITY_CHECK_USER (0 calls in window)

System prompt

Given the candidate identity, intended use, evidence, and policy_profile, apply the supplied eligibility rules exactly. Resolve ambiguous identity matches, distinguish established facts from assumptions, and return unknown or ineligible when the required evidence is absent. Use only the rules, jurisdictions, evidence requirements, and time thresholds supplied in policy_profile; leave unspecified policy dimensions unset. 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 — author_name: {author_name}; expertise_areas: {expertise_areas}; content_themes: {content_themes}; content_domains: {content_domains}; policy_profile: {policy_profile}. Use only these inputs to complete the task defined by the system prompt.