Cost mode:

Category: Financial Analysis & Trading Decisions · Typical I/O: 42004→1429 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 8.84 / 10. Quality bar at 90%: 7.96.

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
DeepSeek V4.1 Flash8.08 / 107.76$3.76best value
Xiaomi MiMo V2.6 Pro7.96 / 107.55$7.752.1x more expensive
Qwen 3.7 Plus8.28 / 108.12$12.923.4x more expensive
GLM-5.3 Flash8.55 / 108.33$25.246.7x more expensive
Thinking Machines Inkling Small8.46 / 108.22$38.3010x more expensive
Tencent Hy4 Preview8.30 / 108.08$56.8715x more expensive
Claude Sonnet 5.58.34 / 108.04$60.9816x more expensive
Thinking Machines Inkling8.59 / 108.33$70.0319x more expensive
Meta Muse Spark 1.38.13 / 107.94$74.3520x more expensive
Grok 4.78.05 / 107.70$95.3725x more expensive
Claude Opus 5.58.44 / 108.09$124.6033x more expensive
Qwen 3.8 Max7.99 / 107.76$138.1337x more expensive
Moonshot Kimi K38.84 / 108.70$156.1341x more expensive
GLM-5.38.59 / 108.44$228.9661x more expensive
GPT-6 Luna7.66 / 107.42$1.8152% cheaper
Xiaomi MiMo V2.6 Flash7.78 / 107.41$2.4635% cheaper
NVIDIA Nemotron-3 Super 120B7.21 / 106.97$6.191.6x more expensive
NVIDIA Nemotron-3 Nano 30B-A3B5.74 / 105.32$2.6729% cheaper
Gemini 3.5 Flash Lite7.16 / 106.82$5.611.5x more expensive
Tencent Hy37.71 / 107.46$5.861.6x more expensive
NVIDIA Nemotron-3 Ultra 550B7.95 / 107.65$38.8410x more expensive
GPT-6 Sol7.64 / 107.28$34.299.1x more expensive
NVIDIA Nemotron 3.5 Lightning6.58 / 106.14$3.75best value
MiniMax M37.72 / 107.58$14.623.9x more expensive
Gemini 3.8 Flash7.23 / 106.97$16.424.4x more expensive
Qwen 3.8 Flash6.71 / 106.34$20.685.5x more expensive

Cost breakdown

ModelQualityConfidenceCost / 1k runsOverpayMode
DeepSeek V4.1 Flash ★ DeepSeek8.08 / 10 CI [7.76, 8.41]MEDIUM$3.76best valuebatch
Xiaomi MiMo V2.6 Pro Xiaomi MiMo7.96 / 10 CI [7.55, 8.36]MEDIUM$7.752.1xbatch
Qwen 3.7 Plus Alibaba Cloud (DashScope)8.28 / 10 CI [8.12, 8.44]RANKED$12.923.4xbatch
GLM-5.3 Flash Z.AI8.55 / 10 CI [8.33, 8.77]HIGH$25.246.7xbatch
Thinking Machines Inkling Small OpenRouter8.46 / 10 CI [8.22, 8.71]HIGH$38.3010xbatch
Tencent Hy4 Preview OpenRouter8.30 / 10 CI [8.08, 8.52]HIGH$56.8715xbatch
Claude Sonnet 5.5 Anthropic8.34 / 10 CI [8.04, 8.64]MEDIUM$60.9816xbatch
Thinking Machines Inkling OpenRouter8.59 / 10 CI [8.33, 8.85]HIGH$70.0319xbatch
Meta Muse Spark 1.3 OpenRouter8.13 / 10 CI [7.94, 8.31]RANKED$74.3520xbatch
Grok 4.7 xAI8.05 / 10 CI [7.70, 8.40]MEDIUM$95.3725xbatch
Claude Opus 5.5 Anthropic8.44 / 10 CI [8.09, 8.80]MEDIUM$124.6033xbatch
Qwen 3.8 Max Alibaba Cloud (DashScope)7.99 / 10 CI [7.76, 8.22]HIGH$138.1337xbatch
Moonshot Kimi K3 best Moonshot AI8.84 / 10 CI [8.70, 8.98]RANKED$156.1341xbatch
GLM-5.3 Z.AI8.59 / 10 CI [8.44, 8.73]RANKED$228.9661xbatch

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: 42004 input tokens → 1429 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 faithful synthesis of all panel evidence, treatment of disagreement, direction and confidence calibration, constraint compliance, internal consistency, risk awareness, traceability, and absence of invented market facts.

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": "Individual voter's structured decision from the investment panel.",
  "properties": {
    "confidence": {
      "description": "Confidence level in the vote (0.0 to 1.0)",
      "maximum": 1,
      "minimum": 0,
      "title": "Confidence",
      "type": "number"
    },
    "direction": {
      "description": "Investment direction recommendation",
      "enum": [
        "BULLISH",
        "BEARISH",
        "NEUTRAL"
      ],
      "title": "Direction",
      "type": "string"
    },
    "entry_condition": {
      "default": "",
      "description": "Entry condition or price level (e.g., 'Buy at market', 'Limit order at $480', 'On pullback to 50-day MA')",
      "title": "Entry Condition",
      "type": "string"
    },
    "expected_pct_change": {
      "description": "Expected percentage change (e.g., 12.5 for +12.5%, -8.0 for -8%)",
      "title": "Expected Pct Change",
      "type": "number"
    },
    "expected_timeframe_days": {
      "description": "Expected timeframe in days for the move to materialize",
      "minimum": 1,
      "title": "Expected Timeframe Days",
      "type": "integer"
    },
    "key_reasoning": {
      "description": "2-3 sentence summary of the key reasoning behind this vote",
      "title": "Key Reasoning",
      "type": "string"
    },
    "position_size_pct": {
      "default": 2,
      "description": "Recommended position size as percentage of portfolio (e.g., 2.0 for 2%)",
      "maximum": 100,
      "minimum": 0,
      "title": "Position Size Pct",
      "type": "number"
    },
    "preferred_instrument": {
      "default": "",
      "description": "Specific tradeable instrument recommended (e.g., 'SPY', 'TSLA 200 call Apr 2025', '/ES futures')",
      "title": "Preferred Instrument",
      "type": "string"
    },
    "risk_factors": {
      "description": "Top risk factors considered in reaching this decision",
      "items": {
        "type": "string"
      },
      "title": "Risk Factors",
      "type": "array"
    },
    "stop_loss": {
      "default": "",
      "description": "Stop-loss level or condition (e.g., 'Stop at $460', 'Trail 5%', 'Exit if breaks below 200-day MA')",
      "title": "Stop Loss",
      "type": "string"
    }
  },
  "required": [
    "direction",
    "expected_pct_change",
    "expected_timeframe_days",
    "confidence",
    "key_reasoning"
  ],
  "title": "InvestPanelVote",
  "type": "object"
}

Prompt templates

The published template for this task.

default · LLMB_MULTI_PERSPECTIVE_DECISION_SYNTHESIS_SYSTEM + LLMB_MULTI_PERSPECTIVE_DECISION_SYNTHESIS_USER (516 calls in window)

System prompt

Evaluate all supplied perspectives, evidence, disagreements, and challenges before deciding. Apply analysis_profile as the sole source of decision domain, labels, confidence scale, constraints, allowed actions, quantitative framework, risk treatment, and prohibited conclusions. Separate evidence from stakeholder preference, expose unresolved conflicts, and do not convert missing evidence into confidence. 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 — decision_context: {decision_context}; perspectives: {perspectives}; challenges: {challenges}; current_state: {current_state}; constraints: {constraints}; supporting_summary: {supporting_summary}; decision_notes: {decision_notes}; analysis_profile: {analysis_profile}. Use only these inputs to complete the task defined by the system prompt.