Knowledge · Alibaba Cloud

Qwen3-Max Nano on MMLU-Pro: score, rank and what it means

Qwen3-Max Nano scores 76.0 on MMLU-Pro, placing it #308 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.

Score
76.0
0–100 scale
Rank
#308
of 600
Percentile
49th
within the index
Benchmark avg
73.5
all models
Leader
91.6
GPT-5.2 Pro
Gap to leader
15.6
points

Score distribution

The chart shows the top 10 models on MMLU-Pro, with Qwen3-Max Nano highlighted. Longer bars mean higher scores.

GPT-5.2 Pro 91.6
GPT-5.1 Max 91.3
Claude Opus 4.5 Pro 91.2
Claude Opus 4.5 Reasoning 90.7
Gemini 3 Pro Reasoning 90.1
GPT-5.1 Reasoning 90.0
GPT-5.2 89.9
Claude Opus 4.5 Max 89.5
GPT-5.2 Vision 89.5
Gemini 3 Pro Max 89.5

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Pro OpenAI 2000000 $21.32 89.6
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 Claude Opus 4.5 Reasoning Anthropic 2000000 $20.91 88.2
5 Gemini 3 Pro Reasoning Google DeepMind 200000 $19.69 87.5
6 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
7 GPT-5.2 OpenAI 2000000 $19.71 88.0
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 GPT-5.2 Vision OpenAI 1000000 $19.03 87.4
10 Gemini 3 Pro Max Google DeepMind 2000000 $20.57 87.9

What this score means

On MMLU-Pro, Qwen3-Max Nano sits at #308 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 15.6 points. The median model in this index scores 76.1, which puts Qwen3-Max Nano below the midpoint.

Alibaba Cloud models average 75.9 on this benchmark. Qwen3-Max Nano is 0.1 points above that provider line, so it is a stronger result than the average Alibaba Cloud model.

Among mid-range models, the average is 71.5. Qwen3-Max Nano is 4.5 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 76.0 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 $8.31 per million input tokens, Qwen3-Max Nano is above the median input price of $4.00 across the 600 models scored on MMLU-Pro. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Phi-3.5 Microsoft 16384 $2.81 70.9
Auto Turbo OpenRouter 65536 $7.96 70.9
Firefunction Fireworks AI 16384 $8.16 72.3
Fusion Turbo OpenRouter 8192 $8.55 71.7
Qwen3-Max Nano Alibaba Cloud 65536 $8.31 71.7
Llama 3 Replicate Instruct Replicate 128000 $3.71 75.1
Llama 3.1 Turbo Base Together AI 16384 $3.30 73.0

Other benchmarks for Qwen3-Max Nano

BenchmarkScoreRankPercentileRelative
GPQA Diamond 65.0 361 40th
SWE-bench Verified 59.2 339 44th
HumanEval 74.7 349 42th
MATH-500 75.6 313 48th
AIME 2025 69.1 280 53th
LiveCodeBench 62.5 343 43th
IFEval 79.3 346 42th
TruthfulQA 76.8 287 52th
HellaSwag 81.9 352 41th
MMMU 68.5 336 44th
Arena Elo 1348.2 368 39th

FAQ

What score does Qwen3-Max Nano get on MMLU-Pro?

Qwen3-Max Nano scores 76.0 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.

How does Qwen3-Max Nano rank on MMLU-Pro?

It ranks #308 of 600 models, in the 49th percentile. The leader is GPT-5.2 Pro at 91.6.

Is Qwen3-Max Nano good at MMLU-Pro?

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 MMLU-Pro score calculated?

MMLU-Pro is a knowledge 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 Qwen3-Max Nano cost?

Qwen3-Max Nano costs $8.31 per million input tokens and $42.90 per million output tokens.

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