Mathematics · 01.AI

Yi-Large Instruct on MATH-500: score, rank and what it means

Yi-Large Instruct scores 78.2 on MATH-500, placing it #254 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
78.2
0–100 scale
Rank
#254
of 600
Percentile
58th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
18.5
points

Score distribution

The chart shows the top 10 models on MATH-500, with Yi-Large Instruct highlighted. Longer bars mean higher scores.

GPT-5.2 Max 96.7
GPT-5.1 Reasoning 95.9
GPT-5.1 Pro 95.1
o4 Reasoning 94.8
Grok 4 Max 94.6
Grok 4 Reasoning 94.4
Claude Opus 4.5 Max 94.2
Claude Opus 4.5 93.7
Gemini 3 Pro Reasoning 93.3
Claude Opus 4.5 Turbo 93.2

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-5.1 Pro OpenAI 200000 $19.77 87.8
4 o4 Reasoning OpenAI 1000000 $19.69 87.5
5 Grok 4 Max xAI 2000000 $19.70 86.6
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
8 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
9 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
10 Claude Opus 4.5 Turbo Anthropic 2000000 $19.57 85.7

What this score means

On MATH-500, Yi-Large Instruct sits at #254 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 18.5 points. The median model in this index scores 76.0, which puts Yi-Large Instruct above the midpoint.

01.AI models average 67.4 on this benchmark. Yi-Large Instruct is 10.8 points above that provider line, so it is a stronger result than the average 01.AI model.

Among multimodal models, the average is 81.3. Yi-Large Instruct is 3.1 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 78.2 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 $3.48 per million input tokens, Yi-Large Instruct is below the median input price of $4.00 across the 600 models scored on MATH-500. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Llama 4 Scout Base Meta AI 32768 $3.95 75.3
Gemini 3 Pro Nano Google DeepMind 8192 $9.11 73.2
Llama 3.3 Groq Chat Groq 32768 $4.06 76.8
MiniMax-Text Chat MiniMax 128000 $4.26 77.8
Yi-Large Instruct 01.AI 32768 $3.48 73.4
Yi-Lightning Instruct 01.AI 128000 $4.16 76.5
Codestral Instruct Mistral AI 65536 $4.01 75.1

Other benchmarks for Yi-Large Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 77.8 252 58th
GPQA Diamond 70.0 265 56th
SWE-bench Verified 60.4 320 47th
HumanEval 77.1 307 49th
AIME 2025 64.9 339 44th
LiveCodeBench 65.9 277 54th
IFEval 81.2 305 49th
TruthfulQA 74.5 342 43th
HellaSwag 84.3 269 55th
MMMU 73.2 214 64th
Arena Elo 1390.6 248 59th

FAQ

What score does Yi-Large Instruct get on MATH-500?

Yi-Large Instruct scores 78.2 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does Yi-Large Instruct rank on MATH-500?

It ranks #254 of 600 models, in the 58th percentile. The leader is GPT-5.2 Max at 96.7.

Is Yi-Large Instruct good at MATH-500?

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 MATH-500 score calculated?

MATH-500 is a mathematics 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 Instruct cost?

Yi-Large Instruct costs $3.48 per million input tokens and $13.46 per million output tokens.

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