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GLM-5.1

MERA Created at 17.04.2026 10:58
0.804
The overall result
8
Place in the rating
In the top by tasks:
4
RWSD
The result on the task is higher than human
The task is one of the main ones
2
RCB
The result on the task is higher than human
The task is one of the main ones
10
MultiQ
The task is one of the main ones
3
ruWorldTree
The result on the task is higher than human
The task is one of the main ones
6
ruOpenBookQA
The result on the task is higher than human
The task is one of the main ones
5
CheGeKa
The task is one of the main ones
3
ruMMLU
The result on the task is higher than human
4
ruHateSpeech
6
ruHHH
The result on the task is higher than human
3
ruTiE
The result on the task is higher than human
The task is one of the main ones
3
USE
The result on the task is higher than human
The task is one of the main ones
4
MathLogicQA
The result on the task is higher than human
The task is one of the main ones
8
ruMultiAr
The result on the task is higher than human
The task is one of the main ones
4
MaMuRAMu
The result on the task is higher than human
The task is one of the main ones
+10
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Weak tasks:
87
PARus
37
ruEthics
22
ruDetox
227
ruHumanEval
24
SimpleAr
143
ruCodeEval
+2
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Ratings for leaderboard tasks

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Task name Result Metric
LCS 0.616 Accuracy
RCB 0.632 / 0.471 Accuracy F1 macro
USE 0.777 Grade norm
RWSD 0.9 Accuracy
PARus 0.906 Accuracy
ruTiE 0.966 Accuracy
MultiQ 0.651 / 0.496 F1 Exact match
CheGeKa 0.676 / 0.575 F1 Exact match
ruModAr 0.998 Exact match
MaMuRAMu 0.914 Accuracy
ruMultiAr 0.999 Exact match
ruCodeEval 0.108 / 0.307 / 0.427 Pass@k
MathLogicQA 0.997 Accuracy
ruWorldTree 0.996 / 0.996 Accuracy F1 macro
ruOpenBookQA 0.965 / 0.965 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 0.998 Accuracy
ruMMLU 0.907 Accuracy
SimpleAr 0.999 Exact match
ruHumanEval 0.036 / 0.146 / 0.238 Pass@k
ruHHH 0.916
ruHateSpeech 0.94
ruDetox 0.364
ruEthics
Correct God Ethical
Virtue 0.468 0.427 0.617
Law 0.472 0.395 0.615
Moral 0.513 0.445 0.659
Justice 0.429 0.369 0.55
Utilitarianism 0.385 0.377 0.526

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
bfloat16
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:

GLM-5.1

Link to the ML model:

https://openrouter.ai/z-ai/glm-5.1

Model size

754.0B

Model type:

API

Opened

SFT

MoE

Additional links:

https://arxiv.org/abs/2602.15763

Architecture description:

GLM-5.1 is our next-generation flagship model for agentic engineering, with significantly stronger coding capabilities than its predecessor. It achieves state-of-the-art performance on SWE-Bench Pro and leads GLM-5 by a wide margin on NL2Repo (repo generation) and Terminal-Bench 2.0 (real-world terminal tasks).

License:

MIT

Inference parameters

Generation Parameters:
simplear - do_sample=false;until=[];max_gen_toks=32768;reasoning={"enabled":true,"max_tokens":10000}; \nrudetox - do_sample=false;until=[];max_gen_toks=16384;reasoning={"enabled":true,"max_tokens":10000}; \nrumultiar - do_sample=false;until=[];max_gen_toks=16384;reasoning={"enabled":true,"max_tokens":10000}; \nuse - do_sample=false;until=[];max_gen_toks=16384;reasoning={"enabled":true,"max_tokens":10000}; \nmultiq - do_sample=false;until=[];max_gen_toks=16384;reasoning={"enabled":true,"max_tokens":10000}; \nrumodar - do_sample=false;until=[];max_gen_toks=16384;reasoning={"enabled":true,"max_tokens":10000}; \nchegeka - do_sample=false;until=[];max_gen_toks=16384;reasoning={"enabled":true,"max_tokens":10000}; \nrucodeeval - do_sample=true;temperature=0.6;until=[];max_gen_toks=16384;reasoning={"enabled":true,"max_tokens":10000}; \nruhumaneval - do_sample=true;temperature=0.6;until=[];max_gen_toks=16384;reasoning={"enabled":true,"max_tokens":10000};

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
GLM-5.1
MERA 		0.533 0.7 0.833 0.433 0.8 0.967 0.833 - 0.767 0.467 0.667 0.7 0.967 0.933 0.667 0.833 0.733 0.567 0.9 0.5 0.867 0.667 0.733 0.667 0.9 0.867 0.867 1 0.8 0.833 1
Model, team Honest Helpful Harmless
GLM-5.1
MERA 		0.918 0.915 0.914
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
GLM-5.1
MERA 		0.933 0.536 0.974 0.944 0.938 0.93 0.845 0.907 0.932 0.812 0.877 0.952 0.83 0.898 0.969 0.853 0.83 0.986 0.978 0.939 0.841 0.901 0.96 0.89 0.929 1 0.812 0.731 0.837 0.77 0.91 0.926 0.877 0.939 0.94 0.944 0.97 0.968 0.947 0.961 0.939 0.971 0.89 0.968 0.961 0.912 0.946 0.993 0.943 0.911 0.97 0.916 0.962 0.983 0.98 0.897 0.969
Model, team SIM FL STA
GLM-5.1
MERA 		0.843 0.631 0.7
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
GLM-5.1
MERA 		0.844 0.96 0.933 0.815 0.987 0.879 0.862 0.825 0.981 0.815 0.846 0.892 0.725 0.946 0.93 0.877 0.766 0.844 0.912 0.825 0.947 0.983 0.956 0.959 0.933 0.97 0.91 0.772 0.947 0.978 0.933 0.962 0.938 0.93 0.894 0.964 0.978 0.911 0.93 0.954 0.992 0.952 0.889 1 0.879 0.911 0.948 0.932 0.969 0.982 0.956 0.971 0.886 0.844 0.86 0.918 0.967
Coorect
Good
Ethical
Model, team Virtue Law Moral Justice Utilitarianism
GLM-5.1
MERA 		0.468 0.472 0.513 0.429 0.385
Model, team Virtue Law Moral Justice Utilitarianism
GLM-5.1
MERA 		0.427 0.395 0.445 0.369 0.377
Model, team Virtue Law Moral Justice Utilitarianism
GLM-5.1
MERA 		0.617 0.615 0.659 0.55 0.526
Model, team Women Men LGBT Nationalities Migrants Other
GLM-5.1
MERA 		0.981 0.8 0.882 0.973 1 0.934

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