Mathematics · Alibaba Cloud

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

QwQ Instruct scores 84.1 on MATH-500, placing it #124 of 600 models in the index. The benchmark average is 72.5 and the leader is GPT-5.2 Max at 96.7.

Score
84.1
0–100 scale
Rank
#124
of 600
Percentile
79th
within the index
Benchmark avg
72.5
all models
Leader
96.7
GPT-5.2 Max
Gap to leader
12.6
points

Score distribution

The chart shows the top 10 models on MATH-500, with QwQ 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, QwQ Instruct sits at #124 of 600. The leader, GPT-5.2 Max, scores 96.7, so the gap to the top is 12.6 points. The median model in this index scores 76.0, which puts QwQ Instruct above the midpoint.

Alibaba Cloud models average 76.4 on this benchmark. QwQ Instruct is 7.7 points above that provider line, so it is a stronger result than the average Alibaba Cloud model.

Among multimodal models, the average is 81.3. QwQ Instruct 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 84.1 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 $4.62 per million input tokens, QwQ Instruct is above 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
Command A Cohere 200000 $13.30 79.6
ERNIE 4.5 Max Baidu 128000 $14.41 80.6
Nova Pro Reasoning Amazon 200000 $13.48 79.4
Mistral Large 3 Mistral AI 128000 $5.41 81.7
QwQ Instruct Alibaba Cloud 65536 $4.62 79.0
Sonar Pro Turbo Perplexity 128000 $13.55 79.0
Qwen3 Base Alibaba Cloud 65536 $4.33 77.4

Other benchmarks for QwQ Instruct

BenchmarkScoreRankPercentileRelative
MMLU-Pro 79.8 189 69th
GPQA Diamond 73.5 187 69th
SWE-bench Verified 67.3 163 73th
HumanEval 81.2 210 65th
AIME 2025 74.7 161 73th
LiveCodeBench 72.4 139 77th
IFEval 87.7 110 82th
TruthfulQA 82.8 113 81th
HellaSwag 88.4 128 79th
MMMU 76.8 116 81th
Arena Elo 1420.8 146 76th

FAQ

What score does QwQ Instruct get on MATH-500?

QwQ Instruct scores 84.1 on MATH-500 in this index. The benchmark average is 72.5 and the top score is 96.7.

How does QwQ Instruct rank on MATH-500?

It ranks #124 of 600 models, in the 79th percentile. The leader is GPT-5.2 Max at 96.7.

Is QwQ 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 QwQ Instruct cost?

QwQ Instruct costs $4.62 per million input tokens and $17.65 per million output tokens.

Related