Cost mode:

Category: Long-form Content Generation · Typical I/O: 5678→3429 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: NVIDIA Nemotron-3 Ultra 550B at 8.95 / 10. Quality bar at 90%: 8.05.

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 Luna8.59 / 108.41$0.42best value
MiniMax M38.47 / 108.45$1.112.6x more expensive
Claude Haiku 5.58.45 / 108.25$1.152.7x more expensive
Tencent Hy38.11 / 107.80$1.363.2x more expensive
Thinking Machines Inkling Small8.87 / 108.60$6.2415x more expensive
GPT-6 Sol8.54 / 108.36$6.7116x more expensive
NVIDIA Nemotron-3 Ultra 550B8.95 / 108.74$6.8216x more expensive
Meta Muse Spark 1.38.64 / 108.30$18.7245x more expensive
Claude Sonnet 5.58.36 / 108.06$19.3046x more expensive
Tencent Hy4 Preview8.70 / 108.34$31.4775x more expensive
Claude Opus 5.58.72 / 108.48$44.57106x more expensive
NVIDIA Nemotron 3.5 Lightning7.58 / 107.31$1.874.5x more expensive
Moonshot Kimi K36.05 / 105.61$81.42194x more expensive
NVIDIA Nemotron-3 Nano 30B-A3B6.90 / 106.44$2.245.3x more expensive
Grok 4.76.78 / 106.55$46.75111x more expensive
NVIDIA Nemotron-3 Super 120B7.45 / 107.07$6.8716x more expensive
Thinking Machines Inkling7.84 / 107.35$28.1567x more expensive

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
GPT-6 Luna ★ OpenAI8.59 / 10 CI [8.41, 8.77]RANKED$0.42best valuebatch
MiniMax M3 OpenRouter8.47 / 10 CI [8.45, 8.50]RANKED$1.112.6xbatch
Claude Haiku 5.5 Anthropic8.45 / 10 CI [8.25, 8.65]RANKED$1.152.7xbatch
Tencent Hy3 OpenRouter8.11 / 10 CI [7.80, 8.43]MEDIUM$1.363.2xbatch
Thinking Machines Inkling Small OpenRouter8.87 / 10 CI [8.60, 9.15]HIGH$6.2415xbatch
GPT-6 Sol OpenAI8.54 / 10 CI [8.36, 8.71]RANKED$6.7116xbatch
NVIDIA Nemotron-3 Ultra 550B best OpenRouter8.95 / 10 CI [8.74, 9.15]HIGH$6.8216xbatch
Meta Muse Spark 1.3 OpenRouter8.64 / 10 CI [8.30, 8.98]MEDIUM$18.7245xbatch
Claude Sonnet 5.5 Anthropic8.36 / 10 CI [8.06, 8.65]HIGH$19.3046xbatch
Tencent Hy4 Preview OpenRouter8.70 / 10 CI [8.34, 9.07]MEDIUM$31.4775xbatch
Claude Opus 5.5 Anthropic8.72 / 10 CI [8.48, 8.96]HIGH$44.57106xbatch

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: 5678 input tokens → 3429 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 brand-reference alignment, palette coherence, accessibility and contrast, typography suitability, role completeness, practical implementability, and absence of unsupported asset claims.

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": "Output model for theme generation based on logo analysis and brand context.\n\nThe LLM analyzes the generated logo image along with brand context\n(subject name, industry, focus areas) to produce a cohesive theme.",
  "properties": {
    "accent_color": {
      "description": "Accent color for highlights and CTAs, in hex format (e.g., '#ff6b00')",
      "title": "Accent Color",
      "type": "string"
    },
    "background_color": {
      "description": "Background color that complements the brand, in hex format (e.g., '#ffffff' or '#0a0a0a')",
      "title": "Background Color",
      "type": "string"
    },
    "body_font": {
      "description": "Google Font name for body text, prioritizing readability (e.g., 'Inter', 'Open Sans')",
      "title": "Body Font",
      "type": "string"
    },
    "color_reasoning": {
      "default": "",
      "description": "Explanation of color choices and how they relate to the brand",
      "title": "Color Reasoning",
      "type": "string"
    },
    "css_class": {
      "description": "CSS class for the theme, lowercase with hyphens (e.g., 'theme-tesla', 'theme-spacex')",
      "title": "Css Class",
      "type": "string"
    },
    "font_import_url": {
      "description": "Complete Google Fonts import URL for both fonts (e.g., 'https://fonts.googleapis.com/css2?family=Montserrat:wght@400;700\u0026family=Inter:wght@400;500\u0026display=swap')",
      "title": "Font Import Url",
      "type": "string"
    },
    "font_reasoning": {
      "default": "",
      "description": "Explanation of font choices and how they match the brand personality",
      "title": "Font Reasoning",
      "type": "string"
    },
    "heading_font": {
      "description": "Google Font name for headings that matches brand personality (e.g., 'Playfair Display', 'Montserrat')",
      "title": "Heading Font",
      "type": "string"
    },
    "primary_color": {
      "description": "Primary brand color extracted from logo, in hex format (e.g., '#005288')",
      "title": "Primary Color",
      "type": "string"
    },
    "secondary_color": {
      "description": "Secondary complementary color, in hex format (e.g., '#a7a9ac')",
      "title": "Secondary Color",
      "type": "string"
    },
    "text_color": {
      "description": "Primary text color with good contrast against background, in hex format (e.g., '#333333')",
      "title": "Text Color",
      "type": "string"
    },
    "theme_name": {
      "description": "Display name for the theme (e.g., 'Tesla Modern', 'SpaceX Dark')",
      "title": "Theme Name",
      "type": "string"
    }
  },
  "required": [
    "theme_name",
    "css_class",
    "primary_color",
    "secondary_color",
    "accent_color",
    "background_color",
    "text_color",
    "heading_font",
    "body_font",
    "font_import_url"
  ],
  "title": "ThemeGenerationOutput",
  "type": "object"
}

Prompt templates

The published template for this task.

default · LLMB_VISUAL_THEME_CONFIGURATION_GENERATION_SYSTEM + LLMB_VISUAL_THEME_CONFIGURATION_GENERATION_USER (0 calls in window)

System prompt

Derive a coherent theme from supplied visual evidence and theme_profile. Fill the profile’s color, typography, accessibility, asset, target-system, and usage roles with clear guidance. Treat extracted colors as evidence, not mandatory choices when they fail constraints, and populate only roles and asset types defined by the supplied inputs or profile. 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 — subject_name: {subject_name}; subject_code: {subject_code}; industry: {industry}; focus_areas: {focus_areas}; description: {description}; report_structure: {report_structure}; theme_profile: {theme_profile}. Use only these inputs to complete the task defined by the system prompt.