Knowledge · 01.AI

Yi-Large on MMLU-Pro: score, rank and what it means

Yi-Large scores 77.1 on MMLU-Pro, placing it #274 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
77.1
0–100 scale
Rank
#274
of 600
Percentile
54th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
14.5
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Yi-Large highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, Yi-Large sits at #274 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 14.5 points. The median model in this index scores 76.1, which puts Yi-Large above the midpoint.

01.AI models average 70.0 on this benchmark. Yi-Large is 7.1 points above that provider line, so it is a stronger result than the average 01.AI model.

Among multimodal models, the average is 80.0. Yi-Large is 2.9 points below that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 77.1 as a relative signal, not an absolute guarantee. Benchmark scores depend on prompt format, evaluation harness and contamination controls. Use this page to narrow the shortlist, then test the model on your own data.

Price context

At $3.40 per million input tokens, Yi-Large is below the median input price of $4.00 across the 600 models scored on MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Yi-Lightning Chat 01.AI 32768 $3.79 75.5
Nemotron 4 Chat NVIDIA 65536 $3.99 75.0
Nova Lite Max Amazon 200000 $9.79 73.4
MiniMax-Text Base MiniMax 128000 $3.66 74.9
Yi-Large 01.AI 32768 $3.40 73.5
Claude Haiku 4.5 Mini Anthropic 16384 $8.76 71.6
DBRX Databricks 65536 $3.29 73.3

Other benchmarks for Yi-Large

BenchmarkScoreRankPercentileRelative
GPQA Diamond 70.7 242 60th
SWE-bench Verified 63.7 241 60th
HumanEval 79.4 254 58th
MATH-500 76.2 295 51th
AIME 2025 66.2 327 46th
LiveCodeBench 62.7 338 44th
IFEval 81.2 304 49th
TruthfulQA 78.0 255 58th
HellaSwag 85.2 237 61th
MMMU 67.8 347 42th
Arena Elo 1389.1 253 58th

FAQ

What score does Yi-Large get on MMLU-Pro?

Yi-Large scores 77.1 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Yi-Large rank on MMLU-Pro?

It ranks #274 of 600 models, in the 54th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Yi-Large good at MMLU-Pro?

Yes, relative to the index: it is above the median and inside the top half of scores. The result should be read alongside its price and context window.

How is the MMLU-Pro score calculated?

MMLU-Pro is a knowledge benchmark. Scores in this index are modelled estimates on a 0–100 scale (or Elo for Arena), derived from capability tier and benchmark difficulty. They are not official leaderboard results.

What does Yi-Large cost?

Yi-Large costs $3.40 per million input tokens and $13.35 per million output tokens.

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