Best LLMs for Promotional Message Generation
Generates one grounded promotional message for a content item using caller-supplied channel, audience, voice, length, link, and formatting rules. Illustrative uses include promoting a software release, product update, webinar, customer report, industry event, documentary, scienti
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Models
Frontier on this task: Moonshot Kimi K3 at 8.57 / 10. Quality bar at 90%: 7.71.
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 | 7.88 / 10 | 7.49 | $0.17 | best value |
| Claude Haiku 5.5 | 7.76 / 10 | 7.50 | $0.33 | 2x more expensive |
| Xiaomi MiMo V2.6 Pro | 7.82 / 10 | 7.56 | $0.50 | 2.9x more expensive |
| Tencent Hy3 | 7.78 / 10 | 7.53 | $0.99 | 5.8x more expensive |
| NVIDIA Nemotron-3 Super 120B | 7.88 / 10 | 7.64 | $1.34 | 7.8x more expensive |
| Gemini 3.8 Flash | 7.93 / 10 | 7.72 | $3.25 | 19x more expensive |
| Claude Sonnet 5.5 | 8.42 / 10 | 8.18 | $4.12 | 24x more expensive |
| GPT-6 Sol | 7.97 / 10 | 7.63 | $4.14 | 24x more expensive |
| Thinking Machines Inkling Small | 8.18 / 10 | 7.86 | $4.49 | 26x more expensive |
| GLM-5.3 Flash | 8.37 / 10 | 8.05 | $5.59 | 33x more expensive |
| Claude Opus 5.5 | 8.45 / 10 | 8.21 | $8.12 | 48x more expensive |
| Meta Muse Spark 1.3 | 8.07 / 10 | 7.71 | $13.46 | 79x more expensive |
| Thinking Machines Inkling | 8.45 / 10 | 8.29 | $18.97 | 111x more expensive |
| Moonshot Kimi K3 | 8.57 / 10 | 8.39 | $24.05 | 141x more expensive |
| Qwen 3.8 Max | 7.77 / 10 | 7.45 | $35.06 | 205x more expensive |
| Tencent Hy4 Preview | 8.23 / 10 | 7.95 | $37.39 | 219x more expensive |
| Grok 4.7 | 7.90 / 10 | 7.49 | $42.42 | 248x more expensive |
| GLM-5.3 | 8.46 / 10 | 8.24 | $107.34 | 628x more expensive |
| Gemini 3.5 Flash Lite | 7.40 / 10 | 7.11 | $0.47 | 2.8x more expensive |
| NVIDIA Nemotron-3 Nano 30B-A3B | 7.35 / 10 | 7.12 | $0.82 | 4.8x more expensive |
| NVIDIA Nemotron-3 Ultra 550B | 7.57 / 10 | 7.25 | $3.02 | 18x more expensive |
| Qwen 3.8 Flash | 7.52 / 10 | 7.06 | $6.26 | 37x more expensive |
| Qwen 3.7 Plus | 7.40 / 10 | 7.00 | $4.40 | 26x more expensive |
| DeepSeek V4.1 Flash | 7.58 / 10 | 7.14 | $0.84 | 4.9x more expensive |
| GPT-6 Luna | 7.55 / 10 | 7.19 | $0.16 | 4% cheaper |
Cost breakdown
| Model | Quality | Confidence | Cost / 1k runs | Overpay | Mode |
|---|---|---|---|---|---|
| Xiaomi MiMo V2.6 Flash ★ Xiaomi MiMo | 7.88 / 10 CI [7.49, 8.26] | MEDIUM | $0.17 | best value | batch |
| Claude Haiku 5.5 Anthropic | 7.76 / 10 CI [7.50, 8.02] | HIGH | $0.33 | 2x | batch |
| Xiaomi MiMo V2.6 Pro Xiaomi MiMo | 7.82 / 10 CI [7.56, 8.09] | HIGH | $0.50 | 2.9x | batch |
| Tencent Hy3 OpenRouter | 7.78 / 10 CI [7.53, 8.03] | HIGH | $0.99 | 5.8x | batch |
| NVIDIA Nemotron-3 Super 120B OpenRouter | 7.88 / 10 CI [7.64, 8.12] | HIGH | $1.34 | 7.8x | batch |
| Gemini 3.8 Flash Gemini | 7.93 / 10 CI [7.72, 8.14] | HIGH | $3.25 | 19x | batch |
| Claude Sonnet 5.5 Anthropic | 8.42 / 10 CI [8.18, 8.66] | HIGH | $4.12 | 24x | batch |
| GPT-6 Sol OpenAI | 7.97 / 10 CI [7.63, 8.31] | MEDIUM | $4.14 | 24x | batch |
| Thinking Machines Inkling Small OpenRouter | 8.18 / 10 CI [7.86, 8.49] | MEDIUM | $4.49 | 26x | batch |
| GLM-5.3 Flash Z.AI | 8.37 / 10 CI [8.05, 8.70] | MEDIUM | $5.59 | 33x | batch |
| Claude Opus 5.5 Anthropic | 8.45 / 10 CI [8.21, 8.69] | HIGH | $8.12 | 48x | batch |
| Meta Muse Spark 1.3 OpenRouter | 8.07 / 10 CI [7.71, 8.43] | MEDIUM | $13.46 | 79x | batch |
| Thinking Machines Inkling OpenRouter | 8.45 / 10 CI [8.29, 8.62] | RANKED | $18.97 | 111x | batch |
| Moonshot Kimi K3 best Moonshot AI | 8.57 / 10 CI [8.39, 8.76] | RANKED | $24.05 | 141x | batch |
| Qwen 3.8 Max Alibaba Cloud (DashScope) | 7.77 / 10 CI [7.45, 8.10] | MEDIUM | $35.06 | 205x | batch |
| Tencent Hy4 Preview OpenRouter | 8.23 / 10 CI [7.95, 8.51] | HIGH | $37.39 | 219x | batch |
| Grok 4.7 xAI | 7.90 / 10 CI [7.49, 8.31] | MEDIUM | $42.42 | 248x | batch |
| GLM-5.3 Z.AI | 8.46 / 10 CI [8.24, 8.69] | HIGH | $107.34 | 628x | 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: 2884 input tokens → 491 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 grounding, specificity, promotional effectiveness without hype, platform-native style, clarity, and preservation of the same supported message across variants. Deterministic platform limits are 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.
{
"properties": {
"bluesky_post_text": {
"description": "Engaging Bluesky post under 260 characters. MUST include the literal placeholder \u003cghost_url\u003e where the link should appear. For model-specific LLM benchmark posts, include model/provider hashtags such as #ClaudeSonnet, #Sonnet5, or #Anthropic when relevant. For stock/equity posts, include the ticker as a hashtag, e.g. #AAPL.",
"title": "Bluesky Post Text",
"type": "string"
}
},
"required": [
"bluesky_post_text"
],
"title": "AutoBlueskyPostOutput",
"type": "object"
}Prompt templates
The published template for this task.
default ·
LLMB_PROMOTIONAL_MESSAGE_GENERATION_SYSTEM +
LLMB_PROMOTIONAL_MESSAGE_GENERATION_USER
(966 calls in window)
System prompt
Communicate one specific, source-grounded reason to engage with the content. Apply channel_profile and optional voice_profile without changing the underlying claim. Use channel_profile as the sole source of channel-specific length, formatting, link, label, mention, and line-break rules. Avoid clickbait, invented urgency, unsupported superlatives, engagement bait, and generic announcement filler. 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 — title: {title}; content: {content}; channel_profile: {channel_profile}; voice_profile: {voice_profile}. Use only these inputs to complete the task defined by the system prompt.