Google · Gemini | The Metacognition Bench

Where Google · Gemini sits in the cloud

Google · Gemini model/condition points all other model/condition points equal relative confidence and pass rate

Disposition: where each model draws the line

Competitive context, benchmark by benchmark

SQuAD (factual recall) · Prospective · F_β β

#	Model	F_β	Prec	Rec	Task acc
1	gemini-3-pro-preview	0.785	0.77	0.80	0.65
2	gemini-3-flash-preview	0.782	0.70	0.88	0.59
3	gemini-2.5-pro	0.764	0.71	0.83	0.58
4	claude-sonnet-4.5	0.753	0.78	0.72	0.59
5	gpt-4o	0.739	0.69	0.80	0.54
6	deepseek-r1	0.733	0.76	0.70	0.57
7	claude-haiku-4.5	0.716	0.79	0.65	0.48
8	mistral-medium-3.1	0.703	0.65	0.77	0.52
9	gpt-4o-mini	0.686	0.56	0.88	0.45
10	gemini-2.0-flash-001	0.683	0.58	0.82	0.45
11	gpt-5.2	0.673	0.84	0.56	0.62
12	gemini-2.5-flash	0.670	0.58	0.79	0.45
13	mistral-small-3.2-24b-instruct	0.666	0.57	0.79	0.44
14	llama-3.1-70b-instruct	0.643	0.65	0.64	0.45
15	claude-3.5-sonnet	0.643	0.79	0.54	0.56
16	llama-3.3-70b-instruct	0.593	0.64	0.55	0.45
17	qwen-2.5-72b-instruct	0.572	0.74	0.47	0.47
18	deepseek-chat	0.567	0.78	0.45	0.55
19	claude-3-haiku	0.554	0.55	0.56	0.40
20	qwen-2.5-coder-32b-instruct	0.476	0.31	1.00	0.31

The four metacognitive outcomes — Google · Gemini

No curated cases for this selection yet — outcome-matrix extraction currently covers a sample of MMLU-Pro trials.

Pairwise signal: pairs involving Google · Gemini

Match accuracy controls for the performance base-rate gap

Google · Gemini pairs

80/ 171

Google · Gemini mean tau

+0.052

All-pairs mean

+0.037

Google · Gemini p<0.05

48(60%)

all model pairs (observed) base-rate-matched null calibration-preserving null Google · Gemini pair (filled = p<0.05) Google · Gemini mean all-pairs mean

gemini-2.0-flash-001 →gemini-2.5-flash →gemini-2.5-pro →gemini-3-flash-preview →gemini-3-pro-preview →gemini-3.1-pro-preview → Compare against peers →