Cost mode:

Category: Relevance, Classification & Matching · Typical I/O: 3748→1325 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: Thinking Machines Inkling at 8.88 / 10. Quality bar at 90%: 7.99.

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
Xiaomi MiMo V2.6 Flash8.33 / 107.87$0.07best value
Claude Haiku 5.58.52 / 108.19$0.182.7x more expensive
Gemini 3.5 Flash Lite8.29 / 108.07$0.304.6x more expensive
Xiaomi MiMo V2.6 Pro8.72 / 108.36$0.355.3x more expensive
MiniMax M38.65 / 108.53$0.395.9x more expensive
NVIDIA Nemotron-3 Super 120B8.51 / 108.31$0.629.3x more expensive
Tencent Hy38.57 / 108.38$0.7111x more expensive
Qwen 3.8 Flash8.16 / 107.83$1.0816x more expensive
Gemini 3.8 Flash8.51 / 108.35$1.0916x more expensive
GPT-6 Sol8.05 / 107.76$1.4221x more expensive
Thinking Machines Inkling Small8.79 / 108.61$1.6024x more expensive
GLM-5.3 Flash8.46 / 107.96$1.6625x more expensive
Qwen 3.7 Plus8.70 / 108.55$1.6725x more expensive
NVIDIA Nemotron-3 Ultra 550B8.73 / 108.55$2.2935x more expensive
Claude Sonnet 5.58.49 / 108.18$2.4437x more expensive
Claude Opus 5.58.56 / 108.26$6.2494x more expensive
Thinking Machines Inkling8.88 / 108.71$8.24124x more expensive
Qwen 3.8 Max8.47 / 108.30$8.62130x more expensive
Grok 4.78.51 / 108.18$9.10137x more expensive
Moonshot Kimi K38.84 / 108.63$11.03166x more expensive
GLM-5.38.74 / 108.57$16.13243x more expensive
NVIDIA Nemotron-3 Nano 30B-A3B7.49 / 107.00$0.274x more expensive

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
Xiaomi MiMo V2.6 Flash ★ Xiaomi MiMo8.33 / 10 CI [7.87, 8.79]MEDIUM$0.07best valuebatch
Claude Haiku 5.5 Anthropic8.52 / 10 CI [8.19, 8.85]MEDIUM$0.182.7xbatch
Gemini 3.5 Flash Lite Gemini8.29 / 10 CI [8.07, 8.51]HIGH$0.304.6xbatch
Xiaomi MiMo V2.6 Pro Xiaomi MiMo8.72 / 10 CI [8.36, 9.07]MEDIUM$0.355.3xbatch
MiniMax M3 OpenRouter8.65 / 10 CI [8.53, 8.77]RANKED$0.395.9xbatch
NVIDIA Nemotron-3 Super 120B OpenRouter8.51 / 10 CI [8.31, 8.71]RANKED$0.629.3xbatch
Tencent Hy3 OpenRouter8.57 / 10 CI [8.38, 8.75]RANKED$0.7111xbatch
Qwen 3.8 Flash Alibaba Cloud (DashScope)8.16 / 10 CI [7.83, 8.49]MEDIUM$1.0816xbatch
Gemini 3.8 Flash Gemini8.51 / 10 CI [8.35, 8.66]RANKED$1.0916xbatch
GPT-6 Sol OpenAI8.05 / 10 CI [7.76, 8.34]HIGH$1.4221xbatch
Thinking Machines Inkling Small OpenRouter8.79 / 10 CI [8.61, 8.96]RANKED$1.6024xbatch
GLM-5.3 Flash Z.AI8.46 / 10 CI [7.96, 8.96]MEDIUM$1.6625xbatch
Qwen 3.7 Plus Alibaba Cloud (DashScope)8.70 / 10 CI [8.55, 8.86]RANKED$1.6725xbatch
NVIDIA Nemotron-3 Ultra 550B OpenRouter8.73 / 10 CI [8.55, 8.92]RANKED$2.2935xbatch
Claude Sonnet 5.5 Anthropic8.49 / 10 CI [8.18, 8.79]MEDIUM$2.4437xbatch
Claude Opus 5.5 Anthropic8.56 / 10 CI [8.26, 8.86]MEDIUM$6.2494xbatch
Thinking Machines Inkling best OpenRouter8.88 / 10 CI [8.71, 9.05]RANKED$8.24124xbatch
Qwen 3.8 Max Alibaba Cloud (DashScope)8.47 / 10 CI [8.30, 8.64]RANKED$8.62130xbatch
Grok 4.7 xAI8.51 / 10 CI [8.18, 8.83]MEDIUM$9.10137xbatch
Moonshot Kimi K3 Moonshot AI8.84 / 10 CI [8.63, 9.05]HIGH$11.03166xbatch
GLM-5.3 Z.AI8.74 / 10 CI [8.57, 8.92]RANKED$16.13243xbatch

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: 3748 input tokens → 1325 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 decision correctness, calibration of risk, relevance, evidence feasibility of the angle, policy compliance, and clarity of rationale. Penalize engagement recommendations driven only by reach.

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.

{
  "description": "Per-post triage: should we engage, how risky, and the angle.",
  "properties": {
    "angle": {
      "default": "",
      "description": "Short suggested angle for a helpful reply (empty if ignore).",
      "title": "Angle",
      "type": "string"
    },
    "claim_subjects": {
      "description": "Up to 6 task or model slugs, COPIED VERBATIM from the coverage index you were given, that this post is actually about. These select which measured facts the reply is allowed to draw on, so an invented or approximate slug is worse than an empty list. Empty if ignoring, or if the post is not about a listed subject.",
      "items": {
        "type": "string"
      },
      "title": "Claim Subjects",
      "type": "array"
    },
    "decision": {
      "description": "Whether to engage with this post.",
      "enum": [
        "engage",
        "ignore"
      ],
      "title": "Decision",
      "type": "string"
    },
    "reasoning": {
      "default": "",
      "description": "Brief justification for the decision.",
      "title": "Reasoning",
      "type": "string"
    },
    "relevance": {
      "description": "0-1 relevance to the benchmark's themes (cost/eval/model-selection).",
      "maximum": 1,
      "minimum": 0,
      "title": "Relevance",
      "type": "number"
    },
    "risk_tier": {
      "description": "low=factual/supportive answer; medium=opinion/promotional; high=contentious/critical/sensitive.",
      "enum": [
        "low",
        "medium",
        "high"
      ],
      "title": "Risk Tier",
      "type": "string"
    }
  },
  "required": [
    "decision",
    "relevance",
    "risk_tier"
  ],
  "title": "EngagementTriageDecision",
  "type": "object"
}

Prompt templates

The published template for this task.

default · LLMB_ENGAGEMENT_OPPORTUNITY_TRIAGE_SYSTEM + LLMB_ENGAGEMENT_OPPORTUNITY_TRIAGE_USER (564 calls in window)

System prompt

COVERAGE INDEX — the subjects we hold measured evidence about, and the kinds of fact held about each:
{bench_claim_index}

COVERED SUBJECTS — the only models that may be named or evaluated:
{covered_models}

Make an engage-or-ignore decision based on relevance, ability to add value, evidence availability, and risk. Treat controversy or popularity as context, not automatic reasons to engage. Evidence availability means the coverage index above: engage where a specific measured fact we hold bears on the post, and prefer ignoring over engaging on a subject the index does not list. Any proposed angle must be supportable by that evidence together with the approved claims, and must respect forbidden claims and operator instructions. In claim_subjects, name the index entries the post is actually about, copied verbatim and at most six — these decide which measured facts the reply may draw on, so copy them exactly or leave the list empty rather than approximating. Do not name or evaluate anything absent from the covered subjects list; address the underlying tradeoff generally instead. Explain the decisive factors compactly. 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 — operator_instructions: {operator_instructions}; platform: {platform}; author_handle: {author_handle}; post_text: {post_text}; themes: {themes}; approved_claims: {approved_claims}; forbidden_claims: {forbidden_claims}; channel_profile: {channel_profile}; policy_profile: {policy_profile}. Use only these inputs to complete the task defined by the system prompt.