Commonsense · Alibaba Cloud

QwQ Chat on HellaSwag: score, rank and what it means

QwQ Chat scores 87.9 on HellaSwag, placing it #147 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.

Score
87.9
0–100 scale
Rank
#147
of 600
Percentile
76th
within the index
Benchmark avg
81.4
all models
Leader
96.1
GPT-5.2 Max
Gap to leader
8.2
points

Score distribution

The chart shows the top 10 models on HellaSwag, with QwQ Chat 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, QwQ Chat sits at #147 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 8.2 points. The median model in this index scores 83.6, which puts QwQ Chat above the midpoint.

Alibaba Cloud models average 83.1 on this benchmark. QwQ Chat is 4.8 points above that provider line, so it is a stronger result than the average Alibaba Cloud model.

Among multimodal models, the average is 86.8. QwQ Chat is 1.1 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 87.9 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 $4.28 per million input tokens, QwQ Chat is above 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
Sonar Pro Max Perplexity 128000 $14.01 81.1
ERNIE 4.0 Baidu 32768 $11.37 76.7
Llama 3.1 Turbo Together AI 200000 $4.17 76.8
Magistral Mistral AI 200000 $4.75 79.8
QwQ Chat Alibaba Cloud 65536 $4.28 78.2
Doubao 1.5 Pro Max ByteDance 400000 $14.93 81.2
GLM-4.5 Instruct Zhipu AI 200000 $4.70 78.8

Other benchmarks for QwQ Chat

BenchmarkScoreRankPercentileRelative
MMLU-Pro 80.6 167 72th
GPQA Diamond 72.8 203 66th
SWE-bench Verified 68.3 139 77th
HumanEval 83.4 151 75th
MATH-500 82.1 166 72th
AIME 2025 76.1 140 77th
LiveCodeBench 68.8 213 65th
IFEval 85.2 186 69th
TruthfulQA 79.8 191 68th
MMMU 75.7 145 76th
Arena Elo 1414.1 173 71th

FAQ

What score does QwQ Chat get on HellaSwag?

QwQ Chat scores 87.9 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.

How does QwQ Chat rank on HellaSwag?

It ranks #147 of 600 models, in the 76th percentile. The leader is GPT-5.2 Max at 96.1.

Is QwQ Chat good at HellaSwag?

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 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 QwQ Chat cost?

QwQ Chat costs $4.28 per million input tokens and $16.00 per million output tokens.

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