Best LLMs for Bench Hypothesis Post Generation
Write the short social messages that argue one hypothesis, using only the approved evidence behind it.
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.
| Model | Quality score | CI low | Cost / 1k runs | vs best value |
|---|---|---|---|---|
| GPT-6 Luna | 8.46 / 10 | 8.29 | $0.37 | best value |
| Claude Haiku 5.5 | 8.70 / 10 | 8.49 | $0.41 | 1.1x more expensive |
| Gemini 3.5 Flash Lite | 8.47 / 10 | 8.30 | $0.49 | 1.3x more expensive |
| DeepSeek V4.1 Flash | 8.40 / 10 | 8.14 | $2.21 | 6x more expensive |
| Tencent Hy3 | 8.30 / 10 | 7.92 | $3.24 | 8.8x more expensive |
| Qwen 3.8 Flash | 8.14 / 10 | 7.86 | $3.34 | 9.1x more expensive |
| Gemini 3.8 Flash | 8.70 / 10 | 8.59 | $3.70 | 10x more expensive |
| GPT-6 Sol | 8.62 / 10 | 8.43 | $4.17 | 11x more expensive |
| Thinking Machines Inkling Small | 8.24 / 10 | 7.91 | $4.27 | 12x more expensive |
| MiniMax M3 | 8.61 / 10 | 8.39 | $4.38 | 12x more expensive |
| NVIDIA Nemotron-3 Ultra 550B | 8.23 / 10 | 7.98 | $4.41 | 12x more expensive |
| Claude Sonnet 5.5 | 8.76 / 10 | 8.59 | $4.46 | 12x more expensive |
| Qwen 3.7 Plus | 8.47 / 10 | 8.34 | $7.61 | 21x more expensive |
| Meta Muse Spark 1.3 | 8.67 / 10 | 8.55 | $12.57 | 34x more expensive |
| Claude Opus 5.5 | 8.77 / 10 | 8.60 | $13.41 | 37x more expensive |
| Thinking Machines Inkling | 8.29 / 10 | 8.02 | $15.26 | 42x more expensive |
| Moonshot Kimi K3 | 8.82 / 10 | 8.58 | $40.91 | 111x more expensive |
| Tencent Hy4 Preview | 8.36 / 10 | 8.15 | $48.55 | 132x more expensive |
| Qwen 3.8 Max | 8.71 / 10 | 8.58 | $49.58 | 135x more expensive |
| Grok 4.7 | 8.29 / 10 | 8.09 | $91.92 | 250x more expensive |
| GLM-5.3 | 8.71 / 10 | 8.57 | $173.38 | 472x more expensive |
| NVIDIA Nemotron-3 Super 120B | 7.81 / 10 | 7.43 | $1.41 | 3.8x more expensive |
| NVIDIA Nemotron-3 Nano 30B-A3B | 7.15 / 10 | 6.76 | $1.18 | 3.2x more expensive |
Cost breakdown
| Model | Quality | Confidence | Cost / 1k runs | Overpay | Mode |
|---|---|---|---|---|---|
| GPT-6 Luna ★ OpenAI | 8.46 / 10 CI [8.29, 8.63] | RANKED | $0.37 | best value | batch |
| Claude Haiku 5.5 Anthropic | 8.70 / 10 CI [8.49, 8.92] | HIGH | $0.41 | 1.1x | batch |
| Gemini 3.5 Flash Lite Gemini | 8.47 / 10 CI [8.30, 8.63] | RANKED | $0.49 | 1.3x | batch |
| DeepSeek V4.1 Flash DeepSeek | 8.40 / 10 CI [8.14, 8.65] | HIGH | $2.21 | 6x | batch |
| Tencent Hy3 OpenRouter | 8.30 / 10 CI [7.92, 8.68] | MEDIUM | $3.24 | 8.8x | batch |
| Qwen 3.8 Flash Alibaba Cloud (DashScope) | 8.14 / 10 CI [7.86, 8.43] | HIGH | $3.34 | 9.1x | batch |
| Gemini 3.8 Flash Gemini | 8.70 / 10 CI [8.59, 8.81] | RANKED | $3.70 | 10x | batch |
| GPT-6 Sol OpenAI | 8.62 / 10 CI [8.43, 8.82] | RANKED | $4.17 | 11x | batch |
| Thinking Machines Inkling Small OpenRouter | 8.24 / 10 CI [7.91, 8.57] | MEDIUM | $4.27 | 12x | batch |
| MiniMax M3 OpenRouter | 8.61 / 10 CI [8.39, 8.82] | HIGH | $4.38 | 12x | batch |
| NVIDIA Nemotron-3 Ultra 550B OpenRouter | 8.23 / 10 CI [7.98, 8.48] | HIGH | $4.41 | 12x | batch |
| Claude Sonnet 5.5 Anthropic | 8.76 / 10 CI [8.59, 8.93] | RANKED | $4.46 | 12x | batch |
| Qwen 3.7 Plus Alibaba Cloud (DashScope) | 8.47 / 10 CI [8.34, 8.59] | RANKED | $7.61 | 21x | batch |
| Meta Muse Spark 1.3 OpenRouter | 8.67 / 10 CI [8.55, 8.80] | RANKED | $12.57 | 34x | batch |
| Claude Opus 5.5 Anthropic | 8.77 / 10 CI [8.60, 8.94] | RANKED | $13.41 | 37x | batch |
| Thinking Machines Inkling OpenRouter | 8.29 / 10 CI [8.02, 8.55] | HIGH | $15.26 | 42x | batch |
| Moonshot Kimi K3 best Moonshot AI | 8.82 / 10 CI [8.58, 9.06] | HIGH | $40.91 | 111x | batch |
| Tencent Hy4 Preview OpenRouter | 8.36 / 10 CI [8.15, 8.56] | HIGH | $48.55 | 132x | batch |
| Qwen 3.8 Max Alibaba Cloud (DashScope) | 8.71 / 10 CI [8.58, 8.84] | RANKED | $49.58 | 135x | batch |
| Grok 4.7 xAI | 8.29 / 10 CI [8.09, 8.48] | RANKED | $91.92 | 250x | batch |
| GLM-5.3 Z.AI | 8.71 / 10 CI [8.57, 8.85] | RANKED | $173.38 | 472x | batch |
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}