Best LLMs for Engagement Opportunity Triage
Classifies whether an incoming public message merits a response, identifies risk, and proposes a defensible angle using supplied themes, evidence boundaries, channel context, and policy. Illustrative uses include triaging a customer escalation, software security report, partner q
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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.
| Model | Quality score | CI low | Cost / 1k runs | vs best value |
|---|---|---|---|---|
| Xiaomi MiMo V2.6 Flash | 8.33 / 10 | 7.87 | $0.07 | best value |
| Claude Haiku 5.5 | 8.52 / 10 | 8.19 | $0.18 | 2.7x more expensive |
| Gemini 3.5 Flash Lite | 8.29 / 10 | 8.07 | $0.30 | 4.6x more expensive |
| Xiaomi MiMo V2.6 Pro | 8.72 / 10 | 8.36 | $0.35 | 5.3x more expensive |
| MiniMax M3 | 8.65 / 10 | 8.53 | $0.39 | 5.9x more expensive |
| NVIDIA Nemotron-3 Super 120B | 8.51 / 10 | 8.31 | $0.62 | 9.3x more expensive |
| Tencent Hy3 | 8.57 / 10 | 8.38 | $0.71 | 11x more expensive |
| Qwen 3.8 Flash | 8.16 / 10 | 7.83 | $1.08 | 16x more expensive |
| Gemini 3.8 Flash | 8.51 / 10 | 8.35 | $1.09 | 16x more expensive |
| GPT-6 Sol | 8.05 / 10 | 7.76 | $1.42 | 21x more expensive |
| Thinking Machines Inkling Small | 8.79 / 10 | 8.61 | $1.60 | 24x more expensive |
| GLM-5.3 Flash | 8.46 / 10 | 7.96 | $1.66 | 25x more expensive |
| Qwen 3.7 Plus | 8.70 / 10 | 8.55 | $1.67 | 25x more expensive |
| NVIDIA Nemotron-3 Ultra 550B | 8.73 / 10 | 8.55 | $2.29 | 35x more expensive |
| Claude Sonnet 5.5 | 8.49 / 10 | 8.18 | $2.44 | 37x more expensive |
| Claude Opus 5.5 | 8.56 / 10 | 8.26 | $6.24 | 94x more expensive |
| Thinking Machines Inkling | 8.88 / 10 | 8.71 | $8.24 | 124x more expensive |
| Qwen 3.8 Max | 8.47 / 10 | 8.30 | $8.62 | 130x more expensive |
| Grok 4.7 | 8.51 / 10 | 8.18 | $9.10 | 137x more expensive |
| Moonshot Kimi K3 | 8.84 / 10 | 8.63 | $11.03 | 166x more expensive |
| GLM-5.3 | 8.74 / 10 | 8.57 | $16.13 | 243x more expensive |
| NVIDIA Nemotron-3 Nano 30B-A3B | 7.49 / 10 | 7.00 | $0.27 | 4x more expensive |
Cost breakdown
| Model | Quality | Confidence | Cost / 1k runs | Overpay | Mode |
|---|---|---|---|---|---|
| Xiaomi MiMo V2.6 Flash ★ Xiaomi MiMo | 8.33 / 10 CI [7.87, 8.79] | MEDIUM | $0.07 | best value | batch |
| Claude Haiku 5.5 Anthropic | 8.52 / 10 CI [8.19, 8.85] | MEDIUM | $0.18 | 2.7x | batch |
| Gemini 3.5 Flash Lite Gemini | 8.29 / 10 CI [8.07, 8.51] | HIGH | $0.30 | 4.6x | batch |
| Xiaomi MiMo V2.6 Pro Xiaomi MiMo | 8.72 / 10 CI [8.36, 9.07] | MEDIUM | $0.35 | 5.3x | batch |
| MiniMax M3 OpenRouter | 8.65 / 10 CI [8.53, 8.77] | RANKED | $0.39 | 5.9x | batch |
| NVIDIA Nemotron-3 Super 120B OpenRouter | 8.51 / 10 CI [8.31, 8.71] | RANKED | $0.62 | 9.3x | batch |
| Tencent Hy3 OpenRouter | 8.57 / 10 CI [8.38, 8.75] | RANKED | $0.71 | 11x | batch |
| Qwen 3.8 Flash Alibaba Cloud (DashScope) | 8.16 / 10 CI [7.83, 8.49] | MEDIUM | $1.08 | 16x | batch |
| Gemini 3.8 Flash Gemini | 8.51 / 10 CI [8.35, 8.66] | RANKED | $1.09 | 16x | batch |
| GPT-6 Sol OpenAI | 8.05 / 10 CI [7.76, 8.34] | HIGH | $1.42 | 21x | batch |
| Thinking Machines Inkling Small OpenRouter | 8.79 / 10 CI [8.61, 8.96] | RANKED | $1.60 | 24x | batch |
| GLM-5.3 Flash Z.AI | 8.46 / 10 CI [7.96, 8.96] | MEDIUM | $1.66 | 25x | batch |
| Qwen 3.7 Plus Alibaba Cloud (DashScope) | 8.70 / 10 CI [8.55, 8.86] | RANKED | $1.67 | 25x | batch |
| NVIDIA Nemotron-3 Ultra 550B OpenRouter | 8.73 / 10 CI [8.55, 8.92] | RANKED | $2.29 | 35x | batch |
| Claude Sonnet 5.5 Anthropic | 8.49 / 10 CI [8.18, 8.79] | MEDIUM | $2.44 | 37x | batch |
| Claude Opus 5.5 Anthropic | 8.56 / 10 CI [8.26, 8.86] | MEDIUM | $6.24 | 94x | batch |
| Thinking Machines Inkling best OpenRouter | 8.88 / 10 CI [8.71, 9.05] | RANKED | $8.24 | 124x | batch |
| Qwen 3.8 Max Alibaba Cloud (DashScope) | 8.47 / 10 CI [8.30, 8.64] | RANKED | $8.62 | 130x | batch |
| Grok 4.7 xAI | 8.51 / 10 CI [8.18, 8.83] | MEDIUM | $9.10 | 137x | batch |
| Moonshot Kimi K3 Moonshot AI | 8.84 / 10 CI [8.63, 9.05] | HIGH | $11.03 | 166x | batch |
| GLM-5.3 Z.AI | 8.74 / 10 CI [8.57, 8.92] | RANKED | $16.13 | 243x | 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: 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.