GPT-5.2

GPT-5.2 Created at 19.12.2025 08:14

0.707

The overall result

Place in the rating

In the top by tasks:

RWSD

The result on the task is higher than human

The task is one of the main ones

MultiQ

The task is one of the main ones

ruWorldTree

The result on the task is higher than human

The task is one of the main ones

ruOpenBookQA

The result on the task is higher than human

The task is one of the main ones

CheGeKa

The task is one of the main ones

ruMMLU

The result on the task is higher than human

ruHateSpeech

ruTiE

The result on the task is higher than human

The task is one of the main ones

USE

The task is one of the main ones

SimpleAr

MaMuRAMu

The result on the task is higher than human

The task is one of the main ones

Hide

Weak tasks:

274

PARus

RCB

ruEthics

171

ruHumanEval

LCS

149

BPS

177

ruCodeEval

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Ratings for leaderboard tasks

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Task name	Result	Metric
LCS	0.162	Accuracy
RCB	0.594 / 0.435	Accuracy F1 macro
USE	0.554	Grade norm
RWSD	0.842	Accuracy
PARus	0.76	Accuracy
ruTiE	0.95	Accuracy
MultiQ	0.659 / 0.48	F1 Exact match
CheGeKa	0.624 / 0.543	F1 Exact match
ruModAr	0.959	Exact match
MaMuRAMu	0.903	Accuracy
ruMultiAr	0.824	Exact match
ruCodeEval	0.045 / 0.143 / 0.207	Pass@k
MathLogicQA	0.984	Accuracy
ruWorldTree	0.996 / 0.996	Accuracy F1 macro
ruOpenBookQA	0.96 / 0.77	Accuracy F1 macro

Evaluation on open tasks:

Go to the ratings by subcategory

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Task name

Result

Metric

BPS

0.963

Accuracy

ruMMLU

0.882

Accuracy

SimpleAr

1.0

Exact match

ruHumanEval

0.076 / 0.202 / 0.268

Pass@k

ruHHH

0.888

ruHateSpeech

0.906

ruDetox

0.37

ruEthics

	Correct	God	Ethical
Virtue	0.432	0.492	0.723
Law	0.449	0.475	0.732
Moral	0.486	0.514	0.771
Justice	0.413	0.433	0.644
Utilitarianism	0.368	0.439	0.606

Information about the submission

Mera version

v1.2.0

Torch Version

2.9.1

The version of the codebase

2a537ddd0d1e7b10fadfb0ee193aa1e211cfb169

CUDA version

12.8

Precision of the model weights

auto

Seed

1234

Batch

Transformers version

4.57.3

The number of GPUs and their type

Architecture

openai-chat-completions

Team:

GPT-5.2

Name of the ML model:

GPT-5.2

Link to the ML model:

https://platform.openai.com/docs/models/gpt-5.2-chat-latest

Model type:

Closed

API

Inference parameters

Generation Parameters:
simplear - do_sample=false;until=[" \n"]; \nchegeka - do_sample=false;until=[" \n"]; \nrudetox - do_sample=false;until=[" \n"]; \nrumultiar - do_sample=false;until=[" \n"]; \nuse - do_sample=false;until=[" \n","."]; \nmultiq - do_sample=false;until=[" \n"]; \nrumodar - do_sample=false;until=[" \n"]; \nruhumaneval - do_sample=true;temperature=0.6;until=[" \nclass"," \ndef"," \n#"," \nif"," \nprint"]; \nrucodeeval - do_sample=true;temperature=0.6;until=[" \nclass"," \ndef"," \n#"," \nif"," \nprint"];

System prompt:
Реши задачу по инструкции ниже. Отвечай без лишних слов. Не давай никаких объяснений и пояснений к своему ответу. Не пиши ничего лишнего. Пиши только то, что указано в инструкции. Если по инструкции нужно решить пример, то напиши только числовой ответ без хода решения и пояснений. Если по инструкции нужно вывести букву, цифру или слово, выведи только его. Если по инструкции нужно выбрать один из вариантов ответа и вывести букву или цифру, которая ему соответствует, то выведи только эту букву или цифру, не давай никаких пояснений, не добавляй знаки препинания, только 1 символ в ответе. Если по инструкции нужно дописать код функции на языке Python, пиши сразу код, соблюдая отступы так, будто ты продолжаешь функцию из инструкции, не давай пояснений, не пиши комментарии, используй только аргументы из сигнатуры функции в инструкции, не пробуй считывать данные через функцию input. Не извиняйся, не строй диалог. Выдавай только ответ и ничего больше.

Ratings by subcategory

Metric: Grade Norm

Model, team	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	8_0	8_1	8_2	8_3	8_4
GPT-5.2 GPT-5.2	0.733	0.767	0.867	0.3	0.733	0.9	0.567	-	0.233	0.133	0.6	0.7	0.767	0.233	0.467	0.033	0.267	0.267	0.433	0.167	0.3	0.767	0.6	0.533	0.633	0.858	0.533	0.633	0.667	0.6	0.933

Model, team	Honest	Helpful	Harmless
GPT-5.2 GPT-5.2	0.918	0.797	0.948

Model, team	Anatomy	Virology	Astronomy	Marketing	Nutrition	Sociology	Management	Philosophy	Prehistory	Human aging	Econometrics	Formal logic	Global facts	Jurisprudence	Miscellaneous	Moral disputes	Business ethics	Biology (college)	Physics (college)	Human Sexuality	Moral scenarios	World religions	Abstract algebra	Medicine (college)	Machine learning	Medical genetics	Professional law	PR	Security studies	Chemistry (школьная)	Computer security	International law	Logical fallacies	Politics	Clinical knowledge	Conceptual_physics	Math (college)	Biology (high school)	Physics (high school)	Chemistry (high school)	Geography (high school)	Professional medicine	Electrical engineering	Elementary mathematics	Psychology (high school)	Statistics (high school)	History (high school)	Math (high school)	Professional accounting	Professional psychology	Computer science (college)	World history (high school)	Macroeconomics	Microeconomics	Computer science (high school)	European history	Government and politics
GPT-5.2 GPT-5.2	0.904	0.584	0.961	0.919	0.922	0.92	0.864	0.904	0.935	0.821	0.825	0.794	0.72	0.87	0.953	0.841	0.82	0.965	0.956	0.878	0.815	0.883	0.89	0.867	0.911	0.99	0.795	0.741	0.812	0.69	0.86	0.909	0.859	0.909	0.921	0.936	0.76	0.948	0.874	0.906	0.909	0.974	0.855	0.944	0.957	0.907	0.946	0.822	0.904	0.897	0.89	0.911	0.944	0.954	0.96	0.885	0.959

Model, team	SIM	FL	STA
GPT-5.2 GPT-5.2	0.714	0.664	0.798

Model, team	Anatomy	Virology	Astronomy	Marketing	Nutrition	Sociology	Managment	Philosophy	Pre-History	Gerontology	Econometrics	Formal logic	Global facts	Jurisprudence	Miscellaneous	Moral disputes	Business ethics	Bilology (college)	Physics (college)	Human sexuality	Moral scenarios	World religions	Abstract algebra	Medicine (college)	Machine Learning	Genetics	Professional law	PR	Security	Chemistry (college)	Computer security	International law	Logical fallacies	Politics	Clinical knowledge	Conceptual physics	Math (college)	Biology (high school)	Physics (high school)	Chemistry (high school)	Geography (high school)	Professional medicine	Electrical Engineering	Elementary mathematics	Psychology (high school)	Statistics (high school)	History (high school)	Math (high school)	Professional Accounting	Professional psychology	Computer science (college)	World history (high school)	Macroeconomics	Microeconomics	Computer science (high school)	Europe History	Government and politics
GPT-5.2 GPT-5.2	0.867	0.95	0.85	0.787	0.961	0.931	0.81	0.789	0.962	0.831	0.795	0.85	0.725	0.938	0.93	0.79	0.879	0.822	0.912	0.842	0.912	0.966	0.933	0.964	0.933	0.97	0.923	0.825	0.947	0.956	0.911	0.962	0.902	0.93	0.894	0.911	0.889	0.933	0.895	0.877	0.98	0.984	0.911	1	0.914	0.867	0.948	0.909	0.985	0.965	0.911	0.986	0.861	0.818	0.744	0.918	0.967

Coorect

Good

Ethical

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
GPT-5.2 GPT-5.2	0.432	0.449	0.486	0.413	0.368

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
GPT-5.2 GPT-5.2	0.492	0.475	0.514	0.433	0.439

Model, team	Virtue	Law	Moral	Justice	Utilitarianism
GPT-5.2 GPT-5.2	0.723	0.732	0.771	0.644	0.606

Model, team	Women	Men	LGBT	Nationalities	Migrants	Other
GPT-5.2 GPT-5.2	0.963	0.629	0.882	0.946	1	0.934

GPT-5.2

Ratings for leaderboard tasks

Evaluation on open tasks:

Information about the submission

Team:

Name of the ML model:

Link to the ML model:

Model type:

Inference parameters

Ratings by subcategory

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