Coding · Alibaba Cloud

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

QwQ Chat scores 83.4 on HumanEval, placing it #151 of 600 models in the index. The benchmark average is 74.0 and the leader is GPT-5.2 Pro at 96.8.

Score
83.4
0–100 scale
Rank
#151
of 600
Percentile
75th
within the index
Benchmark avg
74.0
all models
Leader
96.8
GPT-5.2 Pro
Gap to leader
13.4
points

Score distribution

The chart shows the top 10 models on HumanEval, with QwQ Chat highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 96.8
Gemini 3 Pro Pro 95.0
o4 Max 94.6
Grok 4 Reasoning 93.8
Claude Opus 4.5 93.7
GPT-5.2 Max 93.6
Claude Opus 4.5 Pro 93.4
Grok 4 Max 93.4
GPT-5.2 Turbo 93.2
GPT-5.1 Reasoning 92.9

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 Gemini 3 Pro Pro Google DeepMind 200000 $19.80 87.3
3 o4 Max OpenAI 2000000 $19.51 86.2
4 Grok 4 Reasoning xAI 2000000 $19.67 87.8
5 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
6 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
7 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
8 Grok 4 Max xAI 2000000 $19.70 86.6
9 GPT-5.2 Turbo OpenAI 1000000 $20.73 87.4
10 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3

What this score means

On HumanEval, QwQ Chat sits at #151 of 600. The leader, GPT-5.2 Pro, scores 96.8, so the gap to the top is 13.4 points. The median model in this index scores 77.4, which puts QwQ Chat above the midpoint.

Alibaba Cloud models average 77.1 on this benchmark. QwQ Chat is 6.3 points above that provider line, so it is a stronger result than the average Alibaba Cloud model.

Among multimodal models, the average is 82.2. QwQ Chat is 1.2 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 83.4 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 HumanEval. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Command A Max Cohere 400000 $14.28 79.9
Command A Turbo Cohere 32768 $13.53 79.8
DeepSeek-R1 Base DeepSeek 400000 $5.05 80.1
Nova Pro Reasoning Amazon 200000 $13.48 79.4
QwQ Chat Alibaba Cloud 65536 $4.28 78.2
Reka Core Pro Reka AI 65536 $13.44 79.9
Hunyuan-Turbo Reasoning Tencent 128000 $11.89 77.5

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
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
HellaSwag 87.9 147 76th
MMMU 75.7 145 76th
Arena Elo 1414.1 173 71th

FAQ

What score does QwQ Chat get on HumanEval?

QwQ Chat scores 83.4 on HumanEval in this index. The benchmark average is 74.0 and the top score is 96.8.

How does QwQ Chat rank on HumanEval?

It ranks #151 of 600 models, in the 75th percentile. The leader is GPT-5.2 Pro at 96.8.

Is QwQ Chat good at HumanEval?

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 HumanEval score calculated?

HumanEval is a coding 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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