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GPT-5.4

MERA Created at 02.04.2026 13:06
0.821
The overall result
6
Place in the rating
In the top by tasks:
1
RWSD
The result on the task is higher than human
The task is one of the main ones
9
MultiQ
The task is one of the main ones
3
ruOpenBookQA
The result on the task is higher than human
The task is one of the main ones
1
CheGeKa
The result on the task is higher than human
The task is one of the main ones
2
ruMMLU
The result on the task is higher than human
2
ruHateSpeech
5
ruDetox
2
ruTiE
The result on the task is higher than human
The task is one of the main ones
2
USE
The result on the task is higher than human
The task is one of the main ones
2
ruMultiAr
The result on the task is higher than human
The task is one of the main ones
1
LCS
The result on the task is higher than human
The task is one of the main ones
6
ruModAr
The task is one of the main ones
2
MaMuRAMu
The result on the task is higher than human
The task is one of the main ones
+9
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Weak tasks:
226
PARus
33
RCB
170
ruEthics
261
ruHumanEval
28
MathLogicQA
22
SimpleAr
+2
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Ratings for leaderboard tasks

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Task name Result Metric
LCS 0.974 Accuracy
RCB 0.594 / 0.6 Accuracy F1 macro
USE 0.86 Grade norm
RWSD 0.95 Accuracy
PARus 0.83 Accuracy
ruTiE 0.974 Accuracy
MultiQ 0.652 / 0.463 F1 Exact match
CheGeKa 0.747 / 0.656 F1 Exact match
ruModAr 0.999 Exact match
MaMuRAMu 0.92 Accuracy
ruMultiAr 1.0 Exact match
ruCodeEval 0 / 0 / 0 Pass@k
MathLogicQA 0.982 Accuracy
ruWorldTree 0.992 / 0.992 Accuracy F1 macro
ruOpenBookQA 0.973 / 0.973 Accuracy F1 macro

Evaluation on open tasks:

Go to the ratings by subcategory

The table will scroll to the left

Task name Result Metric
BPS 0.999 Accuracy
ruMMLU 0.918 Accuracy
SimpleAr 0.999 Exact match
ruHumanEval 0.011 / 0.012 / 0.012 Pass@k
ruHHH 0.893
ruHateSpeech 0.947
ruDetox 0.387
ruEthics
Correct God Ethical
Virtue 0.391 0.325 0.679
Law 0.376 0.31 0.664
Moral 0.421 0.33 0.722
Justice 0.36 0.287 0.61
Utilitarianism 0.321 0.307 0.573

Information about the submission

Mera version
v1.2.0
Torch Version
2.10.0
The version of the codebase
0ac3a14
CUDA version
12.8
Precision of the model weights
auto
Seed
1234
Batch
1
Transformers version
4.57.6
The number of GPUs and their type
1 x NVIDIA A100-SXM4-80GB
Architecture
openai-chat-completions

Team:

MERA

Name of the ML model:

GPT-5.4

Link to the ML model:

https://openai.com/ru-RU/index/introducing-gpt-5-4/

Model type:

Closed

Additional links:

http://openrouter.ai/openai/gpt-5.4/api

License:

Proprietary

Inference parameters

Generation Parameters:
simplear - do_sample=false;until=[];max_gen_toks=8192;reasoning={"enabled":true,"effort":"high","exclude":false}; \nchegeka - do_sample=false;until=[];max_gen_toks=8192;reasoning={"enabled":true,"effort":"high","exclude":false}; \nrudetox - do_sample=false;until=[];max_gen_toks=32768;reasoning={"enabled":true,"effort":"high","exclude":false}; \nrumultiar - do_sample=false;until=[];max_gen_toks=32768;reasoning={"enabled":true,"effort":"high","exclude":false}; \nuse - do_sample=false;until=[];max_gen_toks=32768;reasoning={"enabled":true,"effort":"high","exclude":false}; \nmultiq - do_sample=false;until=[];max_gen_toks=32768;reasoning={"enabled":true,"effort":"high","exclude":false}; \nrumodar - do_sample=false;until=[];max_gen_toks=32768;reasoning={"enabled":true,"effort":"high","exclude":false}; \nrucodeeval - do_sample=true;temperature=0.6;until=[];max_gen_toks=32768;reasoning={"enabled":true,"effort":"high","exclude":false}; \nruhumaneval - do_sample=true;temperature=0.6;until=[];max_gen_toks=32768;reasoning={"enabled":true,"effort":"high","exclude":false};

System prompt:
Реши задачу по инструкции ниже. 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-5.4
MERA 		0.933 0.733 0.833 0.667 0.867 0.967 0.933 - 0.833 0.667 0.867 0.9 1 1 0.833 0.917 0.967 0.5 0.967 0.733 0.833 0.767 0.8 0.7 0.8 0.942 0.9 0.967 0.833 0.833 1
Model, team Honest Helpful Harmless
GPT-5.4
MERA 		0.869 0.864 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-5.4
MERA 		0.933 0.572 0.98 0.932 0.944 0.92 0.893 0.91 0.954 0.857 0.868 0.952 0.75 0.917 0.964 0.858 0.83 0.986 0.989 0.901 0.87 0.918 0.95 0.913 0.938 1 0.856 0.787 0.845 0.78 0.91 0.901 0.92 0.949 0.932 0.962 0.98 0.958 0.954 0.956 0.934 0.974 0.89 0.968 0.966 0.944 0.951 0.985 0.961 0.925 0.98 0.962 0.959 0.987 0.97 0.903 0.984
Model, team SIM FL STA
GPT-5.4
MERA 		0.801 0.66 0.75
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-5.4
MERA 		0.911 0.97 0.817 0.815 0.987 0.931 0.862 0.825 0.962 0.815 0.846 0.892 0.742 0.961 0.959 0.864 0.813 0.844 0.912 0.86 0.947 0.966 0.956 0.97 0.956 0.985 0.91 0.877 0.947 0.933 0.933 0.949 0.92 0.982 0.894 0.946 0.956 0.911 0.93 0.938 0.984 0.968 0.889 0.978 0.931 0.889 0.914 0.909 0.985 0.982 0.978 1 0.924 0.844 0.837 0.953 0.967
Coorect
Good
Ethical
Model, team Virtue Law Moral Justice Utilitarianism
GPT-5.4
MERA 		0.391 0.376 0.421 0.36 0.321
Model, team Virtue Law Moral Justice Utilitarianism
GPT-5.4
MERA 		0.325 0.31 0.33 0.287 0.307
Model, team Virtue Law Moral Justice Utilitarianism
GPT-5.4
MERA 		0.679 0.664 0.722 0.61 0.573
Model, team Women Men LGBT Nationalities Migrants Other
GPT-5.4
MERA 		1 0.829 0.882 0.919 1 0.951

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