Compensation benchmarking data · Named, not rankedGeekbench
Named, too few times to rank.
A product needs 10 labels in a category before a share is stated. Geekbench has 1.
Why models recommend it
No positive label carried a quote.
What gives them pause
“Synthetic benchmarks (like PassMark, some aspects of Geekbench) have well-documented limitations”Kimi K2 · Negative question · warning
Also on the shortlist
The products named in the same answers as Geekbench, over the 1 answer that named it, and how often each was the first choice instead.
A side-by-side page exists where both products are among a category's top eight.
How this is measured
Fourteen AI models from 12 labs are asked the same buying questions six ways, for three company sizes. Standings refresh every edition.
Gane sells AI visibility software. No company can pay to change a result.
Read the method →Sources returned with these answers
Fourteen of the fourteen models returned a source list in this edition: five return the pages they cited and nine the search results they consulted. These counts come from 1 of the 1 answers that named Geekbench and are not a share of its labels.
Domains cited
Ten of the ten domain citations in answers naming Geekbench came from a third party's page.
Pages cited
Pages are listed as the models cited them.
By model
| Model | First choice | Alternative | Mention | Warning | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 0 | 0 | 0 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 0 | 0 |
| Perplexity Sonar | 0 | 0 | 0 | 0 | 0 |
| Grok 4.20 | 0 | 0 | 0 | 0 | 0 |
| Mistral Small | 0 | 0 | 0 | 0 | 0 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 0 | 0 | 0 | 0 |
| Kimi K2 | 0 | 0 | 0 | 1 | 1 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-6 Luna | 0 | 0 | 0 | 0 | 0 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |