Llama 3 Anyscale Chat on MMLU-Pro: score, rank and what it means
Llama 3 Anyscale Chat scores 77.5 on MMLU-Pro, placing it #258 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 Anyscale 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 Anyscale Chat sits at #258 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 14.1 points. The median model in this index scores 76.1, which puts Llama 3 Anyscale Chat above the midpoint.
Anyscale models average 69.4 on this benchmark. Llama 3 Anyscale Chat is 8.1 points above that provider line, so it is a stronger result than the average Anyscale model.
Among multimodal models, the average is 80.0. Llama 3 Anyscale Chat is 2.5 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 77.5 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 $3.67 per million input tokens, Llama 3 Anyscale 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 |
|---|---|---|---|---|
| Llama 3.3 Instruct | Meta AI | 128000 | $4.33 | 76.6 |
| Llama 3 Baseten | Baseten | 128000 | $3.62 | 74.9 |
| Mixtral Turbo | Together AI | 65536 | $3.20 | 72.4 |
| Llama 3 Anyscale | Anyscale | 65536 | $3.94 | 74.7 |
| Llama 3 Anyscale Chat | Anyscale | 65536 | $3.67 | 74.6 |
| Llama 3 DeepInfra | DeepInfra | 128000 | $3.67 | 74.0 |
| Llama 3 Novita Instruct | Novita AI | 32768 | $3.94 | 75.6 |
Other benchmarks for Llama 3 Anyscale Chat
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| GPQA Diamond | 68.8 | 302 | 50th | |
| SWE-bench Verified | 61.8 | 283 | 53th | |
| HumanEval | 79.5 | 247 | 59th | |
| MATH-500 | 76.0 | 299 | 50th | |
| AIME 2025 | 72.9 | 205 | 66th | |
| LiveCodeBench | 66.3 | 268 | 55th | |
| IFEval | 84.2 | 210 | 65th | |
| TruthfulQA | 75.9 | 308 | 49th | |
| HellaSwag | 85.1 | 238 | 60th | |
| MMMU | 72.6 | 230 | 62th | |
| Arena Elo | 1373.6 | 302 | 50th |
FAQ
What score does Llama 3 Anyscale Chat get on MMLU-Pro?
Llama 3 Anyscale Chat scores 77.5 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.
How does Llama 3 Anyscale Chat rank on MMLU-Pro?
It ranks #258 of 600 models, in the 57th percentile. The leader is GPT-5.2 Pro at 91.6.
Is Llama 3 Anyscale 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 Anyscale Chat cost?
Llama 3 Anyscale Chat costs $3.67 per million input tokens and $14.56 per million output tokens.