Multimodal · 01.AI

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

Yi-Large scores 67.8 on MMMU, placing it #347 of 600 models in the index. The benchmark average is 67.1 and the leader is GPT-5.2 Max at 86.9.

Score
67.8
0–100 scale
Rank
#347
of 600
Percentile
42th
within the index
Benchmark avg
67.1
all models
Leader
86.9
GPT-5.2 Max
Gap to leader
19.1
points

Score distribution

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

GPT-5.2 Max 86.9
GPT-5.1 Reasoning 86.2
GPT-5.2 85.3
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.1 Max 85.1
Grok 4 Pro 84.9
Claude Opus 4.5 Reasoning 84.6
Claude Sonnet 4.5 Pro 84.5
GPT-5.2 Pro 84.4
GPT-5.2 Vision 84.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
3 GPT-5.2 OpenAI 2000000 $19.71 88.0
4 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
5 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
6 Grok 4 Pro xAI 1000000 $19.00 85.4
7 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
8 Claude Sonnet 4.5 Pro Anthropic 1000000 $18.73 86.7
9 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
10 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4

What this score means

On MMMU, Yi-Large sits at #347 of 600. The leader, GPT-5.2 Max, scores 86.9, so the gap to the top is 19.1 points. The median model in this index scores 70.0, which puts Yi-Large below the midpoint.

01.AI models average 63.2 on this benchmark. Yi-Large is 4.6 points above that provider line, so it is a stronger result than the average 01.AI model.

Among multimodal models, the average is 74.3. Yi-Large is 6.5 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 67.8 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 MMMU. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Mixtral Groq Chat Groq 8192 $3.09 72.0
Mixtral Turbo Base Together AI 32768 $2.53 69.7
Nova Pro Mini Amazon 16384 $7.74 70.6
Llama 3 Baseten Base Baseten 16384 $3.16 72.4
Yi-Large 01.AI 32768 $3.40 73.5
Fusion Max OpenRouter 65536 $9.14 72.5
Gemma 3 Base Google DeepMind 65536 $3.09 71.1

Other benchmarks for Yi-Large

BenchmarkScoreRankPercentileRelative
MMLU-Pro 77.1 274 54th
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
Arena Elo 1389.1 253 58th

FAQ

What score does Yi-Large get on MMMU?

Yi-Large scores 67.8 on MMMU in this index. The benchmark average is 67.1 and the top score is 86.9.

How does Yi-Large rank on MMMU?

It ranks #347 of 600 models, in the 42nd percentile. The leader is GPT-5.2 Max at 86.9.

Is Yi-Large good at MMMU?

It is below the median for this benchmark, so test it carefully before relying on it for this capability. The result should be read alongside its price and context window.

How is the MMMU score calculated?

MMMU is a multimodal 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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