Cost mode:

Category: Infrastructure & Utility · Typical I/O: 2868→1255 tokens

Runs on model group: All models. Inputs may reach any model in the group, not only the ranked models below.

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Models

Frontier on this task: Claude Opus 5.5 at 8.90 / 10. Quality bar at 90%: 8.01.

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
Xiaomi MiMo V2.6 Flash8.48 / 108.29$0.22best value
GPT-6 Luna8.75 / 108.61$0.251.1x more expensive
Claude Haiku 5.58.52 / 108.33$0.361.6x more expensive
MiniMax M38.26 / 108.06$1.165.2x more expensive
Tencent Hy38.17 / 108.04$2.5511x more expensive
Thinking Machines Inkling Small8.68 / 108.32$2.7512x more expensive
Gemini 3.8 Flash8.02 / 107.83$3.2114x more expensive
Qwen 3.7 Plus8.37 / 108.29$3.4816x more expensive
NVIDIA Nemotron-3 Ultra 550B8.36 / 108.12$3.9218x more expensive
GLM-5.3 Flash8.79 / 108.63$4.4620x more expensive
GPT-6 Sol8.83 / 108.66$4.6521x more expensive
Claude Sonnet 5.58.76 / 108.61$7.1032x more expensive
Thinking Machines Inkling8.78 / 108.57$10.4247x more expensive
Claude Opus 5.58.90 / 108.75$13.9863x more expensive
Meta Muse Spark 1.38.16 / 107.89$15.0368x more expensive
Moonshot Kimi K38.65 / 108.52$21.0695x more expensive
Grok 4.78.76 / 108.59$25.48115x more expensive
Tencent Hy4 Preview8.56 / 108.32$25.58115x more expensive
Qwen 3.8 Max8.42 / 108.28$29.00130x more expensive
GLM-5.38.86 / 108.72$45.13203x more expensive
Qwen 3.8 Flash7.83 / 107.59$4.8322x more expensive
NVIDIA Nemotron 3.5 Lightning7.17 / 106.83$0.783.5x more expensive
NVIDIA Nemotron-3 Nano 30B-A3B7.02 / 106.70$0.371.7x more expensive
DeepSeek V4.1 Flash7.96 / 107.75$1.305.9x more expensive
Gemini 3.5 Flash Lite7.25 / 107.04$0.642.9x more expensive

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
Xiaomi MiMo V2.6 Flash ★ Xiaomi MiMo8.48 / 10 CI [8.29, 8.67]RANKED$0.22best valuebatch
GPT-6 Luna OpenAI8.75 / 10 CI [8.61, 8.89]RANKED$0.251.1xbatch
Claude Haiku 5.5 Anthropic8.52 / 10 CI [8.33, 8.70]RANKED$0.361.6xbatch
MiniMax M3 OpenRouter8.26 / 10 CI [8.06, 8.45]RANKED$1.165.2xbatch
Tencent Hy3 OpenRouter8.17 / 10 CI [8.04, 8.30]RANKED$2.5511xbatch
Thinking Machines Inkling Small OpenRouter8.68 / 10 CI [8.32, 9.05]MEDIUM$2.7512xbatch
Gemini 3.8 Flash Gemini8.02 / 10 CI [7.83, 8.22]RANKED$3.2114xbatch
Qwen 3.7 Plus Alibaba Cloud (DashScope)8.37 / 10 CI [8.29, 8.46]RANKED$3.4816xbatch
NVIDIA Nemotron-3 Ultra 550B OpenRouter8.36 / 10 CI [8.12, 8.59]HIGH$3.9218xbatch
GLM-5.3 Flash Z.AI8.79 / 10 CI [8.63, 8.95]RANKED$4.4620xbatch
GPT-6 Sol OpenAI8.83 / 10 CI [8.66, 8.99]RANKED$4.6521xbatch
Claude Sonnet 5.5 Anthropic8.76 / 10 CI [8.61, 8.91]RANKED$7.1032xbatch
Thinking Machines Inkling OpenRouter8.78 / 10 CI [8.57, 8.98]HIGH$10.4247xbatch
Claude Opus 5.5 best Anthropic8.90 / 10 CI [8.75, 9.04]RANKED$13.9863xbatch
Meta Muse Spark 1.3 OpenRouter8.16 / 10 CI [7.89, 8.44]HIGH$15.0368xbatch
Moonshot Kimi K3 Moonshot AI8.65 / 10 CI [8.52, 8.79]RANKED$21.0695xbatch
Grok 4.7 xAI8.76 / 10 CI [8.59, 8.93]RANKED$25.48115xbatch
Tencent Hy4 Preview OpenRouter8.56 / 10 CI [8.32, 8.80]HIGH$25.58115xbatch
Qwen 3.8 Max Alibaba Cloud (DashScope)8.42 / 10 CI [8.28, 8.55]RANKED$29.00130xbatch
GLM-5.3 Z.AI8.86 / 10 CI [8.72, 8.99]RANKED$45.13203xbatch

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: 2868 input tokens → 1255 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 source-to-concept relevance, visual specificity, compositional coherence, practical usefulness to an image model, consistency with supplied context, and avoidance of unsupported or contradictory details.

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 generating an image prompt for the analysis report.",
  "properties": {
    "image_prompt": {
      "description": "A detailed, vivid description of the image to be generated.",
      "title": "Image Prompt",
      "type": "string"
    }
  },
  "required": [
    "image_prompt"
  ],
  "title": "ImagePromptGenerationOutput",
  "type": "object"
}

Prompt templates

The published template for this task.

default · LLMB_CONTENT_TO_IMAGE_PROMPT_GENERATION_SYSTEM + LLMB_CONTENT_TO_IMAGE_PROMPT_GENERATION_USER (567 calls in window)

System prompt

You write image briefs. Given source material, produce one self-contained brief that a text-to-image model will render. The brief is the deliverable: the image is generated downstream from your words alone, so whatever you leave unsaid is left to the image model. Do not name a particular image model unless the caller asks.

Derive a visual concept from the source’s central idea rather than illustrating every detail. Cover subject, setting, composition, visual hierarchy, style, palette, lighting, mood, aspect or use context, and explicit exclusions.

SURFACE BRIEF — the image's destination, and the art direction it requires. These rules are authoritative wherever they are more specific than the general guidance above, including where they permit something it discourages:
{image_brief_profile}

Except where the surface brief says otherwise, avoid unsupported logos, real-person likenesses, legible text, charts, and sensitive imagery. 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}; report_content: {report_content}; reference_context: {reference_context}; topic_name: {topic_name}; topic_description: {topic_description}; context: {context}. Use only these inputs to complete the task defined by the system prompt.