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Red Bull vs Monster Energy

Energy drinks60 scenarios

Panel, equal weight

  • claude-haiku-5.5anthropic/claude-haiku-5.5 @ claude-haiku-5.5 via OpenRouter
  • deepseek-v4.1-flashdeepseek/deepseek-v4.1-flash @ deepseek-v4.1-flash via OpenRouter
  • gemini-3.8-flashgoogle/gemini-3.8-flash @ gemini-3.8-flash via OpenRouter
  • glm-5.3-flashz-ai/glm-5.3-flash @ glm-5.3-flash via OpenRouter
  • gpt-6-lunaopenai/gpt-6-luna @ gpt-6-luna via OpenRouter
  • grok-4.7x-ai/grok-4.7 @ grok-4.7 via OpenRouter
  • kimi-k3.1-promoonshotai/kimi-k3.1-pro @ kimi-k3.1-pro via OpenRouter
  • llama-4-maverick-17b-128e-instructmeta-llama/llama-4-maverick-17b-128e-instruct @ llama-4-maverick-17b-128e-instruct via OpenRouter
  • mistral-small-2603mistralai/mistral-small-2603 @ mistral-small-2603 via OpenRouter
  • qwen3.8-flashqwen/qwen3.8-flash @ qwen3.8-flash via OpenRouter
Fast panel

Too closeThe panel’s interval includes 50%: taken together, these 10 API models do not pick between Red Bull and Monster Energy.

Red Bull51%

Monster Energy49%

Consensus42–61%

Too close to call: the confidence interval includes 50%

Each model

Share for Red BullLeans to …

claude-haiku-5.553%41–66%Too close to call
deepseek-v4.1-flash75%64–84%Red Bull
gemini-3.8-flash40%28–53%Too close to call
glm-5.3-flash61%48–73%Too close to call
gpt-6-luna51%40–61%Too close to call
grok-4.741%30–53%Too close to call
kimi-k3.1-pro61%48–72%Too close to call
llama-4-maverick-17b-128e-instruct40%29–51%Too close to call
mistral-small-260348%38–57%Too close to call
qwen3.8-flash46%34–57%Too close to call

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How votes are counted

Replay it as a dropSimulated
Red Bull
Monster Energy

Simulation: scenario paths made up to reproduce the published per-judge shares, not measured rows.

Too close to call: the confidence interval includes 50%

Published 95% confidence interval for Red Bull: 42–61%. Press Drop to watch simulated scenarios land.

How to read it

Each ball is a simulated buying scenario. Each row of pins is an AI model, pushing the ball toward the brand it picked.

One drop is one sample. Drop again and the pile shifts; the orange band shows where the balance lands in 95% of drops.

A leader is named only when the band clears the middle. The paths are simulated from the published per-judge shares: no model saw these scenarios.

Tap to drop. Hold to keep dropping.

The numbers behind the machine
Signed snapshotd0f6eb97fb8dJSON
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How to read this
  • Each scenario offers the models both brands; the split is how often each is chosen. The headline is the panel consensus with equal model weights.
  • The band is the 95% interval for the left brand's share. A model leans to a brand only when its whole interval sits on one side of 50%.
  • 95% confidence intervals (MOVER combination of the per-judge intervals, widened for small samples in battles and newer boards). The consensus gives equal weight to each model family. Per-judge results below are the primary measure.
  • A single model's view is one judge, not the consensus.
  • The drop machine is simulated: its paths reproduce the published per-model shares.
  • Need cuts are withheld below 30 scenarios.
  • Each page shows the latest edition. Dates are in the downloadable JSON.
  • Fast panel: the fast or small current models of each vendor, not the full flagship panel.
Details and sources

Results computed but not published Only the overall consensus and per-judge results are published for this battle: each buyer need has fewer than 30 scenarios, the pre-registered minimum for a need row. See who was counted and why

Who was counted

The rule that decides which judges count was fixed before the run. Judges that met it are counted with the weights below; the others are listed with their reasons.

  • 60 buyer scenarios were planned.
  • None of them was left out.
Consensus weights of 10 counted judges
Weight of each counted judge in the consensus
Judge (API model)Model familyWeight in the consensus
anthropic/claude-haiku-5.5 @ claude-haiku-5.5 via OpenRouteranthropic10.0%
deepseek/deepseek-v4.1-flash @ deepseek-v4.1-flash via OpenRouterdeepseek10.0%
google/gemini-3.8-flash @ gemini-3.8-flash via OpenRoutergoogle10.0%
z-ai/glm-5.3-flash @ glm-5.3-flash via OpenRouterz-ai10.0%
openai/gpt-6-luna @ gpt-6-luna via OpenRouteropenai10.0%
x-ai/grok-4.7 @ grok-4.7 via OpenRouterxai10.0%
moonshotai/kimi-k3.1-pro @ kimi-k3.1-pro via OpenRoutermoonshot10.0%
meta-llama/llama-4-maverick-17b-128e-instruct @ llama-4-maverick-17b-128e-instruct via OpenRoutermeta10.0%
mistralai/mistral-small-2603 @ mistral-small-2603 via OpenRoutermistral10.0%
qwen/qwen3.8-flash @ qwen3.8-flash via OpenRouterqwen10.0%

Judges not counted

Every judge on the panel met the rule.

Results computed but not published

Per-need scenario counts (n) are computed deterministically from the pre-registered plan.

Buyer need: Bold flavor for mornings and shifts
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Bright citrus and tropical flavors
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Lightly sweet for all-day sipping
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Multi-packs and variety boxes
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Smooth afternoon pick-me-up
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Softer carbonation
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Tart berry and sour fruit taste
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Transparent ingredient labeling
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Wide store and travel availability
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Zero sugar with a clean finish
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum

Methodology

Each judge is an AI model called through an API; “About this result” names each model and how it was called. In each buyer scenario, the judge chooses between the two brands. The scenario is the unit of analysis.

The results are based on 60 buyer scenarios.

A judge leans to a brand only when its confidence interval excludes 50%. Point estimates alone never count as a win.

Precision figures on this page are measured.

Precision
Measured
Consensus, needs and languages
95% confidence intervals (MOVER combination of the per-judge intervals, widened for small samples in battles and newer boards)
Per judge
95% confidence intervals (hull of the scenario-clustered bootstrap and Wilson intervals, widened for small samples in battles and newer boards)
Scenario languages
English

Data warnings

  • Brand names only: the models saw each option as a brand name, with no product facts, and answered from their own knowledge.

About this result

What was measured, how, when, and how to check it.

Methodology
ACM-1.0
AI models (API)
  • anthropic/claude-haiku-5.5 @ claude-haiku-5.5 via OpenRouter, via OpenRouter (pinned providers)
  • deepseek/deepseek-v4.1-flash @ deepseek-v4.1-flash via OpenRouter, via OpenRouter (pinned providers)
  • google/gemini-3.8-flash @ gemini-3.8-flash via OpenRouter, via OpenRouter (pinned providers)
  • z-ai/glm-5.3-flash @ glm-5.3-flash via OpenRouter, via OpenRouter (pinned providers)
  • openai/gpt-6-luna @ gpt-6-luna via OpenRouter, via OpenRouter (pinned providers)
  • x-ai/grok-4.7 @ grok-4.7 via OpenRouter, via OpenRouter (pinned providers)
  • moonshotai/kimi-k3.1-pro @ kimi-k3.1-pro via OpenRouter, via OpenRouter (pinned providers)
  • meta-llama/llama-4-maverick-17b-128e-instruct @ llama-4-maverick-17b-128e-instruct via OpenRouter, via OpenRouter (pinned providers)
  • mistralai/mistral-small-2603 @ mistral-small-2603 via OpenRouter, via OpenRouter (pinned providers)
  • qwen/qwen3.8-flash @ qwen3.8-flash via OpenRouter, via OpenRouter (pinned providers)
Conditions
Each model was called through the route listed next to it and chose between the two brands in 60 buyer scenarios. Scenario languages: English.
Judges counted

The pre-registered inclusion rule counted 10 of the 10 judges on the panel. The results use 60 of the 60 planned scenarios.

See who was counted and why

Pre-registration (SHA-256)
57be7161d7397c42ed46206aee51f92c7e170f9cfea97b893f48e6bde0d31464
What the pre-registration fixes
Signed snapshot

Verify this result: download the signed snapshot (JSON)

Signing key (kid)
FnG9tqyrr6Y0YUh6teXhpERjAKUSfzL78m4WdnEZ9Jg
Production key
SHA-256 of the payload
d0f6eb97fb8d9877b1ecb858ebe962641f52d0cccae63470b86483ff4e53c2a2

How to verify a snapshot

Rank, inclusion and influence on results are never sold.