What the steps mean
Cost mode:

Category: Social & Promotional Content · Typical I/O: 1338→1933 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 8.82 / 10. Score needed at the Balanced step: 7.94.

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
GPT-6 Luna8.46 / 108.29$0.37best value
Claude Haiku 5.58.70 / 108.49$0.411.1x more expensive
Gemini 3.5 Flash Lite8.47 / 108.30$0.491.3x more expensive
DeepSeek V4.1 Flash8.40 / 108.14$2.216x more expensive
Tencent Hy38.30 / 107.92$3.248.8x more expensive
Qwen 3.8 Flash8.14 / 107.86$3.349.1x more expensive
Gemini 3.8 Flash8.70 / 108.59$3.7010x more expensive
GPT-6 Sol8.62 / 108.43$4.1711x more expensive
Thinking Machines Inkling Small8.24 / 107.91$4.2712x more expensive
MiniMax M38.61 / 108.39$4.3812x more expensive
NVIDIA Nemotron-3 Ultra 550B8.23 / 107.98$4.4112x more expensive
Claude Sonnet 5.58.76 / 108.59$4.4612x more expensive
Qwen 3.7 Plus8.47 / 108.34$7.6121x more expensive
Meta Muse Spark 1.38.67 / 108.55$12.5734x more expensive
Claude Opus 5.58.77 / 108.60$13.4137x more expensive
Thinking Machines Inkling8.29 / 108.02$15.2642x more expensive
Moonshot Kimi K38.82 / 108.58$40.91111x more expensive
Tencent Hy4 Preview8.36 / 108.15$48.55132x more expensive
Qwen 3.8 Max8.71 / 108.58$49.58135x more expensive
Grok 4.78.29 / 108.09$91.92250x more expensive
GLM-5.38.71 / 108.57$173.38472x more expensive
NVIDIA Nemotron-3 Super 120B7.81 / 107.43$1.413.8x more expensive
NVIDIA Nemotron-3 Nano 30B-A3B7.15 / 106.76$1.183.2x more expensive

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
GPT-6 Luna ★ OpenAI8.46 / 10 CI [8.29, 8.63]RANKED$0.37best valuebatch
Claude Haiku 5.5 Anthropic8.70 / 10 CI [8.49, 8.92]HIGH$0.411.1xbatch
Gemini 3.5 Flash Lite Gemini8.47 / 10 CI [8.30, 8.63]RANKED$0.491.3xbatch
DeepSeek V4.1 Flash DeepSeek8.40 / 10 CI [8.14, 8.65]HIGH$2.216xbatch
Tencent Hy3 OpenRouter8.30 / 10 CI [7.92, 8.68]MEDIUM$3.248.8xbatch
Qwen 3.8 Flash Alibaba Cloud (DashScope)8.14 / 10 CI [7.86, 8.43]HIGH$3.349.1xbatch
Gemini 3.8 Flash Gemini8.70 / 10 CI [8.59, 8.81]RANKED$3.7010xbatch
GPT-6 Sol OpenAI8.62 / 10 CI [8.43, 8.82]RANKED$4.1711xbatch
Thinking Machines Inkling Small OpenRouter8.24 / 10 CI [7.91, 8.57]MEDIUM$4.2712xbatch
MiniMax M3 OpenRouter8.61 / 10 CI [8.39, 8.82]HIGH$4.3812xbatch
NVIDIA Nemotron-3 Ultra 550B OpenRouter8.23 / 10 CI [7.98, 8.48]HIGH$4.4112xbatch
Claude Sonnet 5.5 Anthropic8.76 / 10 CI [8.59, 8.93]RANKED$4.4612xbatch
Qwen 3.7 Plus Alibaba Cloud (DashScope)8.47 / 10 CI [8.34, 8.59]RANKED$7.6121xbatch
Meta Muse Spark 1.3 OpenRouter8.67 / 10 CI [8.55, 8.80]RANKED$12.5734xbatch
Claude Opus 5.5 Anthropic8.77 / 10 CI [8.60, 8.94]RANKED$13.4137xbatch
Thinking Machines Inkling OpenRouter8.29 / 10 CI [8.02, 8.55]HIGH$15.2642xbatch
Moonshot Kimi K3 best Moonshot AI8.82 / 10 CI [8.58, 9.06]HIGH$40.91111xbatch
Tencent Hy4 Preview OpenRouter8.36 / 10 CI [8.15, 8.56]HIGH$48.55132xbatch
Qwen 3.8 Max Alibaba Cloud (DashScope)8.71 / 10 CI [8.58, 8.84]RANKED$49.58135xbatch
Grok 4.7 xAI8.29 / 10 CI [8.09, 8.48]RANKED$91.92250xbatch
GLM-5.3 Z.AI8.71 / 10 CI [8.57, 8.85]RANKED$173.38472xbatch

Overpay shows how much more you pay than the best-value model that meets the quality step (marked ★) — the best-value good-enough option. "16x" means you overpay 16× — 16× that reference for no quality benefit the step asks for. Typical call shape for this task: 1338 input tokens → 1933 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 whether each message argues the given hypothesis, stands on its own for a reader who has not seen the article, uses only figures present in the supplied evidence with their denominators, stays within the character limit, and whether the set varies its approach (the claim, a contrast, an implication, the question it raises, the caveat that bounds it). Penalize invented or altered figures, links or link placeholders, hashtag piles, invitations to read the article, clickbait, invented urgency, unsupported superlatives, engagement bait, near-duplicate messages and a count other than the one requested. Schema validity is deterministic.

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.

{
  "$defs": {
    "FeedPostV1": {
      "additionalProperties": false,
      "description": "One social message arguing a hypothesis.",
      "properties": {
        "angle": {
          "description": "How this message approaches the hypothesis: CLAIM, CONTRAST, IMPLICATION, QUESTION or CAVEAT.",
          "title": "Angle",
          "type": "string"
        },
        "text": {
          "description": "The message. No link and no placeholder — the article link belongs to the announcement post, not to these.",
          "title": "Text",
          "type": "string"
        }
      },
      "required": [
        "angle",
        "text"
      ],
      "title": "FeedPostV1",
      "type": "object"
    }
  },
  "additionalProperties": false,
  "description": "Every message drafted for one hypothesis.",
  "properties": {
    "posts": {
      "description": "The requested number of messages, each standing on its own.",
      "items": {
        "$ref": "#/$defs/FeedPostV1"
      },
      "title": "Posts",
      "type": "array"
    }
  },
  "required": [
    "posts"
  ],
  "title": "FeedPostsV1",
  "type": "object"
}

Prompt templates

The published template for this task.

default · LLMB_BENCH_HYPOTHESIS_POST_GENERATION_SYSTEM + LLMB_BENCH_HYPOTHESIS_POST_GENERATION_USER (2773 calls in window)

System prompt

Write short social messages that each argue one given hypothesis about an AI model benchmark. Each message must stand on its own, be readable by someone who has not seen the article, and stay within the stated character limit. Vary the approach across the set: state the claim, draw a contrast, name an implication, ask the question it raises, or give the caveat that bounds it. Use only figures present in the supplied evidence, and carry each figure's denominator with it. Do not include a link, a link placeholder, a hashtag pile, or an invitation to read the article — the article is promoted separately. Avoid clickbait, invented urgency, unsupported superlatives and engagement bait. Your response must conform exactly to this output schema: {schema_json_string}.

User prompt

Produce exactly {post_count} messages, each at most {max_characters} characters.

Hypothesis: {hypothesis}

Why it holds: {rationale}

Source article: {article_title}

Approved evidence:
{evidence_block}