Kia EV6 Long Range AWD vs BMW i4 eDrive35 Gran Coupe

Electric cars52 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-4-argongoogle/gemini-4-argon @ gemini-4-argon via OpenRouter
gpt-6-lunaopenai/gpt-6-luna @ gpt-6-luna via OpenRouter
mistral-large-4-0mistralai/mistral-large-4-0 @ mistral-large-4-0 via OpenRouter

Fast panel

Clear4 of 5 API models picked Kia; the panel’s interval leans Kia and excludes 50%.

Kia EV6 Long Range AWD75%

BMW i4 eDrive35 Gran Coupe25%

Consensus95% interval 64–85%

The AI consensus leans to ⁨Kia EV6 Long Range AWD⁩

Each model

Share for Kia EV6 Long Range AWDLeans to …

Conditional pick share for Kia EV6 Long Range AWD by judge (among scenarios where the judge picked a brand), with confidence intervals. 95% confidence intervals (hull of the scenario-clustered bootstrap and Wilson intervals, widened for small samples in battles and newer boards)
Judge (API model)	Share for Kia EV6 Long Range AWD	Confidence interval	Leaning
claude-haiku-5.5	57%	44–69%	Too close to call
deepseek-v4.1-flash	82%	69–92%	Leans to Kia EV6 Long Range AWD
gemini-4-argon	83%	70–93%	Leans to Kia EV6 Long Range AWD
gpt-6-luna	77%	64–87%	Leans to Kia EV6 Long Range AWD
mistral-large-4-0	78%	65–89%	Leans to Kia EV6 Long Range AWD

Vote

Which product would you choose?

Your tap stays in this browser: nothing is sent.

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

How votes are counted

Replay it as a dropSimulated

Kia

BMW

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

The AI consensus leans to Kia

Published 95% confidence interval for Kia: 64–85%. 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 snapshot3b5eeaf643e9JSON

Embed

Free to embed. It shows the latest edition; keep the source line and the link.

Embed code (iframe)

<iframe src="https://pickbench.ai/embed/en/battle/kia-ev6-long-range-awd-19-inch-wheels-2026-50212-vs-bmw-i4-edrive35-gran-coupe-19-inch-wheels-2026-50187/" title="Pickbench Brand Battle: Kia vs BMW" width="100%" height="480" loading="lazy" frameborder="0" referrerpolicy="strict-origin-when-cross-origin" sandbox="allow-popups allow-popups-to-escape-sandbox"></iframe>
<p><a href="https://pickbench.ai/en/battles/kia-ev6-long-range-awd-19-inch-wheels-vs-bmw-i4-edrive35-gran-coupe-19-inch-wheels/">Source: Pickbench</a></p>

Static card (SVG)

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

Configurations compared: 2026 Kia EV6 Long Range AWD (19 inch Wheels) (Auto (A1)) vs 2026 BMW i4 eDrive35 Gran Coupe (19 inch Wheels) (Auto (A1))

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.

52 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 family	Weight in the consensus
`anthropic/claude-haiku-5.5 @ claude-haiku-5.5 via OpenRouter`	`anthropic`	20.0%
`deepseek/deepseek-v4.1-flash @ deepseek-v4.1-flash via OpenRouter`	`deepseek`	20.0%
`google/gemini-4-argon @ gemini-4-argon via OpenRouter`	`google`	20.0%
`openai/gpt-6-luna @ gpt-6-luna via OpenRouter`	`openai`	20.0%
`mistralai/mistral-large-4-0 @ mistral-large-4-0 via OpenRouter`	`mistral`	20.0%

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 = 9 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Reliability: n = 10 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Low running costs: n = 14 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 52 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

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

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-4-argon @ gemini-4-argon via OpenRouter, via OpenRouter (pinned providers)
openai/gpt-6-luna @ gpt-6-luna via OpenRouter, via OpenRouter (pinned providers)
mistralai/mistral-large-4-0 @ mistral-large-4-0 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 52 buyer scenarios. Scenario languages: English.

Judges counted

The pre-registered inclusion rule counted 5 of the 5 judges on the panel. The results use 52 of the 52 planned scenarios.

See who was counted and why

Pre-registration (SHA-256)

b18240a8723a94c77f4cd9cfbac4caf25c61042e52623c4f4daa348e4f61dc9c

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: 3b5eeaf643e931294574e6cccca370e4953aab88bc7cb451b1fdef747e598e5f

How to verify a snapshot

Rank, inclusion and influence on results are never sold.