Best LLMs for Social Post Portfolio Selection
Selects the best fixed-size portfolio of candidate social posts using quality, expected audience value, topical diversity, timeliness, and caller-supplied constraints. Illustrative uses include selecting a balanced portfolio of customer updates, product messages, developer-commun
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Models
Frontier on this task: Meta Muse Spark 1.3 at 8.73 / 10. Quality bar at 90%: 7.86.
point-estimate floor (CI low) · upper CI (less certain) · Bars sorted by blended cost; best-value model first.
| Model | Quality score | CI low | Cost / 1k runs | vs best value |
|---|---|---|---|---|
| GPT-6 Luna | 8.55 / 10 | 8.29 | $0.16 | best value |
| Tencent Hy3 | 7.98 / 10 | 7.78 | $0.21 | 1.3x more expensive |
| Xiaomi MiMo V2.6 Flash | 8.71 / 10 | 8.53 | $0.21 | 1.4x more expensive |
| Claude Haiku 5.5 | 8.64 / 10 | 8.46 | $0.21 | 1.4x more expensive |
| Gemini 3.5 Flash Lite | 8.57 / 10 | 8.33 | $0.30 | 1.9x more expensive |
| NVIDIA Nemotron-3 Nano 30B-A3B | 8.32 / 10 | 7.92 | $0.32 | 2.1x more expensive |
| Xiaomi MiMo V2.6 Pro | 8.43 / 10 | 8.22 | $0.55 | 3.5x more expensive |
| MiniMax M3 | 8.40 / 10 | 8.33 | $0.63 | 4.1x more expensive |
| NVIDIA Nemotron-3 Super 120B | 8.08 / 10 | 7.63 | $1.47 | 9.4x more expensive |
| NVIDIA Nemotron 3.5 Lightning | 8.05 / 10 | 7.75 | $1.48 | 9.5x more expensive |
| NVIDIA Nemotron-3 Ultra 550B | 8.30 / 10 | 7.97 | $1.99 | 13x more expensive |
| GPT-6 Sol | 8.70 / 10 | 8.50 | $2.63 | 17x more expensive |
| Claude Sonnet 5.5 | 8.59 / 10 | 8.36 | $2.90 | 19x more expensive |
| DeepSeek V4.1 Flash | 8.48 / 10 | 8.31 | $2.96 | 19x more expensive |
| Qwen 3.7 Plus | 8.34 / 10 | 8.19 | $3.54 | 23x more expensive |
| Gemini 3.8 Flash | 8.73 / 10 | 8.55 | $3.60 | 23x more expensive |
| Thinking Machines Inkling Small | 8.70 / 10 | 8.48 | $3.86 | 25x more expensive |
| GLM-5.3 Flash | 8.67 / 10 | 8.54 | $4.18 | 27x more expensive |
| Claude Opus 5.5 | 8.60 / 10 | 8.39 | $6.56 | 42x more expensive |
| Qwen 3.8 Flash | 8.22 / 10 | 8.01 | $7.99 | 51x more expensive |
| Meta Muse Spark 1.3 | 8.73 / 10 | 8.56 | $14.65 | 94x more expensive |
| Grok 4.7 | 8.49 / 10 | 8.27 | $20.53 | 131x more expensive |
| Thinking Machines Inkling | 8.53 / 10 | 8.26 | $26.68 | 171x more expensive |
| Qwen 3.8 Max | 8.15 / 10 | 8.00 | $28.06 | 180x more expensive |
| Moonshot Kimi K3 | 8.62 / 10 | 8.39 | $28.77 | 184x more expensive |
| Tencent Hy4 Preview | 8.65 / 10 | 8.49 | $32.12 | 206x more expensive |
| GLM-5.3 | 8.60 / 10 | 8.43 | $33.31 | 213x more expensive |
Cost breakdown
| Model | Quality | Confidence | Cost / 1k runs | Overpay | Mode |
|---|---|---|---|---|---|
| GPT-6 Luna ★ OpenAI | 8.55 / 10 CI [8.29, 8.81] | HIGH | $0.16 | best value | batch |
| Tencent Hy3 OpenRouter | 7.98 / 10 CI [7.78, 8.19] | HIGH | $0.21 | 1.3x | batch |
| Xiaomi MiMo V2.6 Flash Xiaomi MiMo | 8.71 / 10 CI [8.53, 8.89] | RANKED | $0.21 | 1.4x | batch |
| Claude Haiku 5.5 Anthropic | 8.64 / 10 CI [8.46, 8.83] | RANKED | $0.21 | 1.4x | batch |
| Gemini 3.5 Flash Lite Gemini | 8.57 / 10 CI [8.33, 8.81] | HIGH | $0.30 | 1.9x | batch |
| NVIDIA Nemotron-3 Nano 30B-A3B OpenRouter | 8.32 / 10 CI [7.92, 8.72] | MEDIUM | $0.32 | 2.1x | batch |
| Xiaomi MiMo V2.6 Pro Xiaomi MiMo | 8.43 / 10 CI [8.22, 8.64] | HIGH | $0.55 | 3.5x | batch |
| MiniMax M3 OpenRouter | 8.40 / 10 CI [8.33, 8.48] | RANKED | $0.63 | 4.1x | batch |
| NVIDIA Nemotron-3 Super 120B OpenRouter | 8.08 / 10 CI [7.63, 8.54] | MEDIUM | $1.47 | 9.4x | batch |
| NVIDIA Nemotron 3.5 Lightning OpenRouter | 8.05 / 10 CI [7.75, 8.34] | HIGH | $1.48 | 9.5x | batch |
| NVIDIA Nemotron-3 Ultra 550B OpenRouter | 8.30 / 10 CI [7.97, 8.63] | MEDIUM | $1.99 | 13x | batch |
| GPT-6 Sol OpenAI | 8.70 / 10 CI [8.50, 8.90] | RANKED | $2.63 | 17x | batch |
| Claude Sonnet 5.5 Anthropic | 8.59 / 10 CI [8.36, 8.82] | HIGH | $2.90 | 19x | batch |
| DeepSeek V4.1 Flash DeepSeek | 8.48 / 10 CI [8.31, 8.65] | RANKED | $2.96 | 19x | batch |
| Qwen 3.7 Plus Alibaba Cloud (DashScope) | 8.34 / 10 CI [8.19, 8.50] | RANKED | $3.54 | 23x | batch |
| Gemini 3.8 Flash Gemini | 8.73 / 10 CI [8.55, 8.91] | RANKED | $3.60 | 23x | batch |
| Thinking Machines Inkling Small OpenRouter | 8.70 / 10 CI [8.48, 8.91] | HIGH | $3.86 | 25x | batch |
| GLM-5.3 Flash Z.AI | 8.67 / 10 CI [8.54, 8.80] | RANKED | $4.18 | 27x | batch |
| Claude Opus 5.5 Anthropic | 8.60 / 10 CI [8.39, 8.81] | HIGH | $6.56 | 42x | batch |
| Qwen 3.8 Flash Alibaba Cloud (DashScope) | 8.22 / 10 CI [8.01, 8.44] | HIGH | $7.99 | 51x | batch |
| Meta Muse Spark 1.3 best OpenRouter | 8.73 / 10 CI [8.56, 8.91] | RANKED | $14.65 | 94x | batch |
| Grok 4.7 xAI | 8.49 / 10 CI [8.27, 8.70] | HIGH | $20.53 | 131x | batch |
| Thinking Machines Inkling OpenRouter | 8.53 / 10 CI [8.26, 8.79] | HIGH | $26.68 | 171x | batch |
| Qwen 3.8 Max Alibaba Cloud (DashScope) | 8.15 / 10 CI [8.00, 8.31] | RANKED | $28.06 | 180x | batch |
| Moonshot Kimi K3 Moonshot AI | 8.62 / 10 CI [8.39, 8.85] | HIGH | $28.77 | 184x | batch |
| Tencent Hy4 Preview OpenRouter | 8.65 / 10 CI [8.49, 8.80] | RANKED | $32.12 | 206x | batch |
| GLM-5.3 Z.AI | 8.60 / 10 CI [8.43, 8.77] | RANKED | $33.31 | 213x | 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: 1924 input tokens → 546 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 selected-item quality, portfolio diversity, timeliness, relevance, avoidance of redundancy, identifier fidelity, and consistency with the requested count and constraints.
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": "LLM output for X post selection within daily budget.",
"properties": {
"selected_pspp_ids": {
"description": "IDs of the selected PSPPs to publish",
"items": {
"type": "string"
},
"title": "Selected Pspp Ids",
"type": "array"
}
},
"required": [
"selected_pspp_ids"
],
"title": "PostSelectionOutput",
"type": "object"
}Prompt templates
The published template for this task.
default ·
LLMB_SOCIAL_POST_PORTFOLIO_SELECTION_SYSTEM +
LLMB_SOCIAL_POST_PORTFOLIO_SELECTION_USER
(0 calls in window)
System prompt
Evaluate candidates individually, then select the strongest portfolio rather than merely the top independent scores. Balance quality and timeliness with topic and framing diversity; avoid near-duplicates and preserve candidate identifiers exactly. Do not infer engagement from author identity or unsupported metrics, and return exactly the requested number when enough eligible candidates exist. 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 — select_count: {select_count}; total_count: {total_count}; candidates_text: {candidates_text}. Use only these inputs to complete the task defined by the system prompt.