Llama 3.1 Turbo Chat on MMLU-Pro: score, rank and what it means
Llama 3.1 Turbo Chat scores 80.8 on MMLU-Pro, placing it #155 of 600 models in the index. The benchmark average is 73.5 and the leader is GPT-5.2 Pro at 91.6.
Score distribution
The chart shows the top 10 models on MMLU-Pro, with Llama 3.1 Turbo Chat highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg 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, Llama 3.1 Turbo Chat sits at #155 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 10.8 points. The median model in this index scores 76.1, which puts Llama 3.1 Turbo Chat above the midpoint.
Together AI models average 69.4 on this benchmark. Llama 3.1 Turbo Chat is 11.4 points above that provider line, so it is a stronger result than the average Together AI model.
Among multimodal models, the average is 80.0. Llama 3.1 Turbo Chat is 0.8 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 80.8 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 $3.95 per million input tokens, Llama 3.1 Turbo Chat is below 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.
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| Qwen3-Max Mini | Alibaba Cloud | 128000 | $11.13 | 76.5 |
| ERNIE 4.0 Max | Baidu | 32768 | $12.40 | 78.9 |
| ERNIE 4.5 Max | Baidu | 128000 | $14.41 | 80.6 |
| Llama 3 Lambda Instruct | Lambda | 32768 | $3.92 | 75.8 |
| Llama 3.1 Turbo Chat | Together AI | 200000 | $3.95 | 76.1 |
| Magistral Instruct | Mistral AI | 65536 | $4.48 | 78.4 |
| Hunyuan-Large | Tencent | 65536 | $4.33 | 78.2 |
Other benchmarks for Llama 3.1 Turbo Chat
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| GPQA Diamond | 73.6 | 182 | 70th | |
| SWE-bench Verified | 64.2 | 230 | 62th | |
| HumanEval | 80.9 | 216 | 64th | |
| MATH-500 | 81.5 | 176 | 71th | |
| AIME 2025 | 73.0 | 202 | 66th | |
| LiveCodeBench | 69.9 | 194 | 68th | |
| IFEval | 83.0 | 243 | 60th | |
| TruthfulQA | 76.0 | 307 | 49th | |
| HellaSwag | 83.7 | 294 | 51th | |
| MMMU | 70.9 | 275 | 54th | |
| Arena Elo | 1404.5 | 195 | 68th |
FAQ
What score does Llama 3.1 Turbo Chat get on MMLU-Pro?
Llama 3.1 Turbo Chat scores 80.8 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.
How does Llama 3.1 Turbo Chat rank on MMLU-Pro?
It ranks #155 of 600 models, in the 74th percentile. The leader is GPT-5.2 Pro at 91.6.
Is Llama 3.1 Turbo Chat good at MMLU-Pro?
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 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 Llama 3.1 Turbo Chat cost?
Llama 3.1 Turbo Chat costs $3.95 per million input tokens and $20.39 per million output tokens.