Hunyuan-Turbo Nano on GPQA Diamond: score, rank and what it means
Hunyuan-Turbo Nano scores 58.2 on GPQA Diamond, placing it #425 of 600 models in the index. The benchmark average is 65.6 and the leader is GPT-5.2 Reasoning at 87.0.
Score distribution
The chart shows the top 10 models on GPQA Diamond, with Hunyuan-Turbo Nano highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg 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 Nano sits at #425 of 600. The leader, GPT-5.2 Reasoning, scores 87.0, so the gap to the top is 28.8 points. The median model in this index scores 68.8, which puts Hunyuan-Turbo Nano below the midpoint.
Tencent models average 67.7 on this benchmark. Hunyuan-Turbo Nano is 9.5 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 Nano is 4.7 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 58.2 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 $4.65 per million input tokens, Hunyuan-Turbo Nano 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.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| StableLM 2 Base | Stability AI | 8192 | $1.49 | 61.9 |
| Embed 4 Turbo | Cohere | 4096 | $5.13 | 64.0 |
| SmolLM3 Instruct | Hugging Face | 8192 | $1.69 | 63.6 |
| Pythia Instruct | EleutherAI | 8192 | $1.41 | 60.8 |
| Hunyuan-Turbo Nano | Tencent | 32768 | $4.65 | 62.7 |
| DeepSeek-R1 8B | DeepSeek | 16384 | $1.87 | 64.3 |
| GLM-4.6 7B | Zhipu AI | 16384 | $1.52 | 61.8 |
Other benchmarks for Hunyuan-Turbo Nano
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 66.2 | 438 | 27th | |
| SWE-bench Verified | 49.4 | 426 | 29th | |
| HumanEval | 67.1 | 421 | 30th | |
| MATH-500 | 66.0 | 413 | 31th | |
| AIME 2025 | 51.5 | 445 | 26th | |
| LiveCodeBench | 51.0 | 439 | 27th | |
| IFEval | 73.0 | 417 | 31th | |
| TruthfulQA | 67.9 | 413 | 31th | |
| HellaSwag | 76.1 | 426 | 29th | |
| MMMU | 63.0 | 398 | 34th | |
| Arena Elo | 1298.8 | 422 | 30th |
FAQ
What score does Hunyuan-Turbo Nano get on GPQA Diamond?
Hunyuan-Turbo Nano scores 58.2 on GPQA Diamond in this index. The benchmark average is 65.6 and the top score is 87.0.
How does Hunyuan-Turbo Nano rank on GPQA Diamond?
It ranks #425 of 600 models, in the 29th percentile. The leader is GPT-5.2 Reasoning at 87.0.
Is Hunyuan-Turbo Nano 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 Nano cost?
Hunyuan-Turbo Nano costs $4.65 per million input tokens and $25.39 per million output tokens.