Llama 3.1 Turbo Instruct on HellaSwag: score, rank and what it means
Llama 3.1 Turbo Instruct scores 86.8 on HellaSwag, placing it #185 of 600 models in the index. The benchmark average is 81.4 and the leader is GPT-5.2 Max at 96.1.
Score distribution
The chart shows the top 10 models on HellaSwag, with Llama 3.1 Turbo Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 2 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 3 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 4 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 5 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 6 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 7 | GPT-5.2 Vision | OpenAI | 1000000 | $19.03 | 87.4 |
| 8 | Gemini 3 Pro Turbo | Google DeepMind | 400000 | $19.62 | 86.4 |
| 9 | GPT-5.1 Reasoning | OpenAI | 400000 | $20.27 | 88.3 |
| 10 | o4 Pro | OpenAI | 1000000 | $19.37 | 87.3 |
What this score means
On HellaSwag, Llama 3.1 Turbo Instruct sits at #185 of 600. The leader, GPT-5.2 Max, scores 96.1, so the gap to the top is 9.3 points. The median model in this index scores 83.6, which puts Llama 3.1 Turbo Instruct above the midpoint.
Together AI models average 78.2 on this benchmark. Llama 3.1 Turbo Instruct is 8.6 points above that provider line, so it is a stronger result than the average Together AI model.
Among multimodal models, the average is 86.8. Llama 3.1 Turbo Instruct is 0.0 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 86.8 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 $4.21 per million input tokens, Llama 3.1 Turbo Instruct is above the median input price of $4.00 across the 600 models scored on HellaSwag. 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 |
|---|---|---|---|---|
| Hunyuan-Large Chat | Tencent | 200000 | $4.35 | 78.1 |
| Llama 3.3 Instruct | Meta AI | 128000 | $4.33 | 76.6 |
| DeepSeek-Coder Instruct | DeepSeek | 32768 | $4.34 | 77.7 |
| Llama 3 Baseten Instruct | Baseten | 200000 | $3.91 | 76.2 |
| Llama 3.1 Turbo Instruct | Together AI | 32768 | $4.21 | 76.7 |
| Llama 3.3 Groq | Groq | 32768 | $3.98 | 76.8 |
| ERNIE 4.0 Reasoning | Baidu | 65536 | $12.20 | 77.8 |
Other benchmarks for Llama 3.1 Turbo Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 80.3 | 176 | 71th | |
| GPQA Diamond | 73.4 | 191 | 68th | |
| SWE-bench Verified | 68.3 | 137 | 77th | |
| HumanEval | 79.2 | 260 | 57th | |
| MATH-500 | 80.3 | 203 | 66th | |
| AIME 2025 | 70.5 | 249 | 59th | |
| LiveCodeBench | 66.9 | 255 | 58th | |
| IFEval | 86.0 | 158 | 74th | |
| TruthfulQA | 81.0 | 156 | 74th | |
| MMMU | 71.3 | 265 | 56th | |
| Arena Elo | 1417.0 | 161 | 73th |
FAQ
What score does Llama 3.1 Turbo Instruct get on HellaSwag?
Llama 3.1 Turbo Instruct scores 86.8 on HellaSwag in this index. The benchmark average is 81.4 and the top score is 96.1.
How does Llama 3.1 Turbo Instruct rank on HellaSwag?
It ranks #185 of 600 models, in the 69th percentile. The leader is GPT-5.2 Max at 96.1.
Is Llama 3.1 Turbo Instruct good at HellaSwag?
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 HellaSwag score calculated?
HellaSwag is a commonsense 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 Llama 3.1 Turbo Instruct cost?
Llama 3.1 Turbo Instruct costs $4.21 per million input tokens and $17.73 per million output tokens.