Reasoning · Tencent

Hunyuan-Turbo Mini on GPQA Diamond: score, rank and what it means

Hunyuan-Turbo Mini scores 63.1 on GPQA Diamond, placing it #383 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.

Score
63.1
0–100 scale
Rank
#383
of 600
Percentile
36th
within the index
Benchmark avg
65.6
all models
Leader
87.0
GPT-5.2 Reasoning
Gap to leader
23.9
points

Score distribution

The chart shows the top 10 models on GPQA Diamond, with Hunyuan-Turbo Mini highlighted. Longer bars mean higher scores.

GPT-5.2 Reasoning 87.0
GPT-5.1 Max 86.4
Claude Opus 4.5 Pro 86.0
GPT-5.1 Reasoning 85.6
Claude Sonnet 4.5 Reasoning 85.2
GPT-5.2 84.9
o4 Turbo 84.8
Claude Opus 4.5 Max 84.6
Grok 4 Turbo 84.5
Claude Sonnet 4.5 Max 84.4

Top 10 leaderboard

#ModelProviderContextInput / 1MAvg score
1 GPT-5.2 Reasoning OpenAI 1000000 $20.98 87.5
2 GPT-5.1 Max OpenAI 2000000 $20.50 87.9
3 Claude Opus 4.5 Pro Anthropic 200000 $20.98 89.1
4 GPT-5.1 Reasoning OpenAI 400000 $20.27 88.3
5 Claude Sonnet 4.5 Reasoning Anthropic 400000 $19.48 87.7
6 GPT-5.2 OpenAI 2000000 $19.71 88.0
7 o4 Turbo OpenAI 2000000 $19.34 86.6
8 Claude Opus 4.5 Max Anthropic 1000000 $20.90 87.8
9 Grok 4 Turbo xAI 1000000 $19.21 86.7
10 Claude Sonnet 4.5 Max Anthropic 1000000 $19.90 87.2

What this score means

On GPQA Diamond, Hunyuan-Turbo Mini sits at #383 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 23.9 points. The median model in this index scores 68.8, which puts Hunyuan-Turbo Mini below the midpoint.

Tencent models average 67.7 on this benchmark. Hunyuan-Turbo Mini is 4.6 points below that provider line, so it is a weaker result than the average Tencent model.

Among mid-range models, the average is 62.9. Hunyuan-Turbo Mini is 0.2 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 63.1 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 $6.59 per million input tokens, Hunyuan-Turbo Mini is above the median input price of $4.00 across the 600 models scored on GPQA Diamond. The models immediately around it in the ranking are shown below so you can compare score and price together.

ModelProviderContextInput / 1MAvg score
Claude 3.7 Sonnet Nano Anthropic 65536 $6.93 68.9
Mixtral Turbo Base Together AI 32768 $2.53 69.7
Molmo Chat Allen Institute for AI 32768 $2.40 66.7
Firesearch Reasoning Fireworks AI 32768 $7.74 69.0
Hunyuan-Turbo Mini Tencent 8192 $6.59 68.3
Gemini 3 Flash Nano Google DeepMind 32768 $7.56 69.4
Embed 4 Reasoning Cohere 4096 $5.50 65.3

Other benchmarks for Hunyuan-Turbo Mini

BenchmarkScoreRankPercentileRelative
MMLU-Pro 73.1 367 39th
SWE-bench Verified 52.3 403 33th
HumanEval 73.7 362 40th
MATH-500 69.0 387 36th
AIME 2025 63.9 357 41th
LiveCodeBench 59.2 377 37th
IFEval 76.7 381 37th
TruthfulQA 73.8 359 40th
HellaSwag 82.0 346 42th
MMMU 64.5 385 36th
Arena Elo 1343.8 374 38th

FAQ

What score does Hunyuan-Turbo Mini get on GPQA Diamond?

Hunyuan-Turbo Mini scores 63.1 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.

How does Hunyuan-Turbo Mini rank on GPQA Diamond?

It ranks #383 of 600 models, in the 36th percentile. The leader is GPT-5.2 Reasoning at 87.0.

Is Hunyuan-Turbo Mini good at GPQA Diamond?

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 GPQA Diamond score calculated?

GPQA Diamond is a reasoning 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 Hunyuan-Turbo Mini cost?

Hunyuan-Turbo Mini costs $6.59 per million input tokens and $31.70 per million output tokens.

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