Pickbench
Sample report

Subaru vs Honda

Which brand 5 AI models prefer, overall and by buyer need when available

Configurations compared: 2026 Subaru Solterra AWD (Auto (A1)) vs 2026 Honda Prologue AWD Elite (Auto (A1))

Subaru
Honda

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

The AI consensus leans to Subaru

Published 95% confidence interval for Subaru: 93%–100%. 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.

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.

The numbers behind the machine

AI consensus

The AI consensus leans to Subaru

Configurations compared: 2026 Subaru Solterra AWD (Auto (A1)) vs 2026 Honda Prologue AWD Elite (Auto (A1))

  • Confidence interval for Subaru: 93%–100%
  • 95% confidence intervals (MOVER combination of the per-judge intervals, widened for small samples in battles and newer boards)

Orange: the 95% confidence interval for Subaru. Notch: the point estimate. Line: 50%.

How the judges lean

  • 5 judges lean to Subaru
  • 0 judges lean to Honda
  • 0 judges: too close to call

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

The consensus gives equal weight to each model family. Per-judge results below are the primary measure.

Fast panel: the fast or small current models of each vendor, not the full flagship panel.

About this battle

Category
Electric cars
As of
Edition
2026-10
Methodology
ACM-1.0
Judges
5
Scenarios
53
Precision
Measured

Vote

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Visitors' votes are shown next to the AI results for comparison only and are never mixed into the AI numbers.

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

Per judge

95% confidence intervals (hull of the scenario-clustered bootstrap and Wilson intervals, widened for small samples in battles and newer boards)

Conditional pick share for Subaru by judge (among scenarios where the judge picked a brand), with confidence intervals
Judge (API model)Share for SubaruConfidence intervalLeaningInterval chart
anthropic/claude-haiku-5.5-20261007 @ claude-haiku-5.5-20261007 via OpenRouter97%93%–100%Subaru
deepseek/deepseek-v4.1-flash-20260910 @ deepseek-v4.1-flash-20260910 via OpenRouter100%93%–100%Subaru
google/gemini-4-argon-20261013 @ gemini-4-argon-20261013 via OpenRouter100%93%–100%Subaru
openai/gpt-6-luna-20260922 @ gpt-6-luna-20260922 via OpenRouter100%93%–100%Subaru
mistralai/mistral-large-4-0-20261006 @ mistral-large-4-0-20261006 via OpenRouter100%93%–100%Subaru

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.

  • 53 buyer scenarios were planned.
  • None of them was left out.
Consensus weights of 5 counted judges
Weight of each counted judge in the consensus
Judge (API model)Model familyWeight in the consensus
anthropic/claude-haiku-5.5-20261007 @ claude-haiku-5.5-20261007 via OpenRouteranthropic20.0%
deepseek/deepseek-v4.1-flash-20260910 @ deepseek-v4.1-flash-20260910 via OpenRouterdeepseek20.0%
google/gemini-4-argon-20261013 @ gemini-4-argon-20261013 via OpenRoutergoogle20.0%
openai/gpt-6-luna-20260922 @ gpt-6-luna-20260922 via OpenRouteropenai20.0%
mistralai/mistral-large-4-0-20261006 @ mistral-large-4-0-20261006 via OpenRoutermistral20.0%

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: Daily commute
n = 9 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Driving fun
n = 10 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Family
n = 11 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Reliability
n = 11 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Low running costs
n = 12 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 53 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.

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

  • Models chose on EPA specification facts only; purchase price, reliability and availability were not assessed.

About this result

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

Methodology
ACM-1.0
Edition
2026-10
Measured on
AI models (API)
  • anthropic/claude-haiku-5.5-20261007 @ claude-haiku-5.5-20261007 via OpenRouter, via OpenRouter (pinned providers)
  • deepseek/deepseek-v4.1-flash-20260910 @ deepseek-v4.1-flash-20260910 via OpenRouter, via OpenRouter (pinned providers)
  • google/gemini-4-argon-20261013 @ gemini-4-argon-20261013 via OpenRouter, via OpenRouter (pinned providers)
  • openai/gpt-6-luna-20260922 @ gpt-6-luna-20260922 via OpenRouter, via OpenRouter (pinned providers)
  • mistralai/mistral-large-4-0-20261006 @ mistral-large-4-0-20261006 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 53 buyer scenarios. Scenario languages: English.
Judges counted

The pre-registered inclusion rule counted 5 of the 5 judges on the panel. The results use 53 of the 53 planned scenarios.

See who was counted and why

Pre-registration (SHA-256)
bb9083c33c2eead95d7ef4840a5753c6bf9e270e4b0507c6e1b26931bdf137ec
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
b07eefcf8c2df63f4306d8feaf05264eddcb66ca60986aea5337cef5a1d58edd

How to verify a snapshot

Rank, inclusion and influence on results are never sold.