Commonsense · 01.AI

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

Yi-Large Base scores 83.0 on HellaSwag, placing it #322 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
83.0
0–100 scale
Rank
#322
of 600
Percentile
46th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
13.1
points

Score distribution

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

GPT-5.2 Max 96.1
GPT-5.2 Turbo 95.5
GPT-5.2 Pro 95.3
Claude Opus 4.5 Pro 95.2
Claude Sonnet 4.5 Reasoning 95.2
Grok 4 Reasoning 95.2
GPT-5.2 Vision 94.6
Gemini 3 Pro Turbo 94.6
GPT-5.1 Reasoning 94.4
o4 Pro 94.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
2 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
3 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
4 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 Grok 4 Reasoning xAI 2000000 $19.67 87.8
7 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
8 Gemini 3 Pro Turbo Google DeepMind 400000 $19.62 86.4
9 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
10 o4 Pro OpenAI 1000000 $19.37 87.3

What this score means

On HellaSwag, Yi-Large Base sits at #322 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 13.1 points. The median model in this index scores 83.6, which puts Yi-Large Base below the midpoint.

01.AI models average 78.8 on this benchmark. Yi-Large Base is 4.2 points above that provider line, so it is a stronger result than the average 01.AI model.

Among open-weight models, the average is 80.2. Yi-Large Base is 2.8 points above that group. This is the more useful comparison if you are shortlisting within the same tier rather than across the whole index.

Read a 83.0 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 $2.78 per million input tokens, Yi-Large Base is below the median input price of $4.00 across the 600 models scored on HellaSwag. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Nova Pro Mini Amazon 16384 $7.74 70.6
Claude Sonnet 4.5 Nano Anthropic 8192 $8.93 72.0
Llama 3.3 Base Meta AI 32768 $3.63 74.0
Reka Flash Reka AI 128000 $9.95 74.1
Yi-Large Base 01.AI 16384 $2.78 70.3
Phi-3.5 Microsoft 16384 $2.81 70.9
Yi-Large Chat 01.AI 65536 $3.49 73.5

Other benchmarks for Yi-Large Base

BenchmarkScoreRankPercentileRelative
MMLU-Pro 75.4 322 46th
GPQA Diamond 68.0 312 48th
SWE-bench Verified 56.8 370 38th
HumanEval 73.3 369 39th
MATH-500 71.2 378 37th
AIME 2025 60.4 386 36th
LiveCodeBench 60.6 361 40th
IFEval 80.4 327 46th
TruthfulQA 75.9 311 48th
MMMU 68.6 333 45th
Arena Elo 1353.7 357 41th

FAQ

What score does Yi-Large Base get on HellaSwag?

Yi-Large Base scores 83.0 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does Yi-Large Base rank on HellaSwag?

It ranks #322 of 600 models, in the 46th percentile. The leader is GPT-5.2 Max at 96.1.

Is Yi-Large Base good at HellaSwag?

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 HellaSwag score calculated?

HellaSwag is a commonsense 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 Base cost?

Yi-Large Base costs $2.78 per million input tokens and $10.84 per million output tokens.

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