Pickbench
Contact

Samsung vs LG

Smart TVs48 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 Samsung and LG.

Samsung51%

LG49%

Consensus42–60%

Too close to call: the confidence interval includes 50%

Each model

Share for SamsungLeans to …

claude-haiku-5.552%40–63%Too close to call
deepseek-v4.1-flash56%44–68%Too close to call
gemini-3.8-flash51%38–64%Too close to call
glm-5.3-flash57%46–69%Too close to call
gpt-6-luna48%40–56%Too close to call
grok-4.764.6%50.4–77.6%Samsung
kimi-k3.1-pro56%43–69%Too close to call
llama-4-maverick-17b-128e-instruct39%28–51%Too close to call
mistral-small-260321%12–32%LG
qwen3.8-flash65%54–77%Samsung

Vote

Which brand would you choose?

Vote first: the AI result appears after you vote or skip.

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
Samsung
LG

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 Samsung: 42–60%. 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 snapshot297f44a32682JSON
Share

Share this result

Post it

Download image

Embed

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

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

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.

  • 48 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: Big screen value for the family room
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Sports in a bright, sunlit living room
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: TV for a bright sunroom or kitchen
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Budget TV for a bedroom or dorm
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Console and PC gaming
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Movies in a dark home theater
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Simple streaming for the family
n = 6 scenarios (fewer than 30) — Fewer scenarios than the pre-registered minimum
Buyer need: Wall-mount design and smart home
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 48 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 48 buyer scenarios. Scenario languages: English.
Judges counted

The pre-registered inclusion rule counted 10 of the 10 judges on the panel. The results use 48 of the 48 planned scenarios.

See who was counted and why

Pre-registration (SHA-256)
f1da42c2b1147f506d22b5b0c54293051ffc02e366f1d705c1c3118927fd3d92
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
297f44a326825f0e8269b57ceaf1863168406ebae0ab3c4d7d48eb14885a9eab

How to verify a snapshot

Rank, inclusion and influence on results are never sold.