Coding · Alibaba Cloud

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

QwQ Chat scores 68.8 on LiveCodeBench, placing it #213 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.

Score
68.8
0–100 scale
Rank
#213
of 600
Percentile
65th
within the index
Benchmark avg
61.0
all models
Leader
85.1
Claude Sonnet 4.5 Reasoning
Gap to leader
16.3
points

Score distribution

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

Claude Sonnet 4.5 Reasoning 85.1
Grok 4 Reasoning 85.1
Claude Opus 4.5 85.0
GPT-5.2 Max 84.8
Grok 4 Turbo 84.7
GPT-5.1 Turbo 84.6
GPT-5.2 Pro 84.6
GPT-5.2 Reasoning 84.6
Claude Opus 4.5 Pro 84.1
Gemini 3 Pro Max 83.6

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
2 Grok 4 Reasoning xAI 2000000 $19.67 87.8
3 Claude Opus 4.5 Anthropic 400000 $19.42 87.7
4 GPT-5.2 Max OpenAI 1000000 $21.39 89.2
5 Grok 4 Turbo xAI 1000000 $19.21 86.7
6 GPT-5.1 Turbo OpenAI 2000000 $19.19 86.1
7 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
8 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
9 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On LiveCodeBench, QwQ Chat sits at #213 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 16.3 points. The median model in this index scores 64.7, which puts QwQ Chat above the midpoint.

Alibaba Cloud models average 64.0 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 70.3. QwQ Chat is 1.5 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 68.8 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 $4.28 per million input tokens, QwQ Chat is above the median input price of $4.00 across the 600 models scored on LiveCodeBench. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
GLM-4.5 Chat Zhipu AI 65536 $4.53 78.3
Phi-4 Chat Microsoft 65536 $3.66 74.7
Qwen3-Coder Chat Alibaba Cloud 128000 $4.59 78.4
ERNIE 4.0 Pro Baidu 200000 $12.58 77.5
QwQ Chat Alibaba Cloud 65536 $4.28 78.2
Qwen3 Base Alibaba Cloud 65536 $4.33 77.4
Nova Lite Pro Amazon 200000 $9.69 74.1

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
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 LiveCodeBench?

QwQ Chat scores 68.8 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.

How does QwQ Chat rank on LiveCodeBench?

It ranks #213 of 600 models, in the 65th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.

Is QwQ Chat good at LiveCodeBench?

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 LiveCodeBench score calculated?

LiveCodeBench 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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