Llama 3.3 Instruct on Arena Elo: score, rank and what it means
Llama 3.3 Instruct scores 1418.0 on Arena Elo, placing it #157 of 600 models in the index. The benchmark average is 1349.9 and the leader is Claude Opus 4.5 Max at 1501.9.
Score distribution
The chart shows the top 10 models on Arena Elo, with Llama 3.3 Instruct highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | Claude Opus 4.5 Max | Anthropic | 1000000 | $20.90 | 87.8 |
| 2 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 3 | GPT-5.2 Turbo | OpenAI | 1000000 | $20.73 | 87.4 |
| 4 | Claude Opus 4.5 Reasoning | Anthropic | 2000000 | $20.91 | 88.2 |
| 5 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 6 | Gemini 3 Pro Turbo | Google DeepMind | 400000 | $19.62 | 86.4 |
| 7 | Grok 4 Max | xAI | 2000000 | $19.70 | 86.6 |
| 8 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 9 | GPT-5.1 Pro | OpenAI | 200000 | $19.77 | 87.8 |
| 10 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
What this score means
On Arena Elo, Llama 3.3 Instruct sits at #157 of 600. The leader, Claude Opus 4.5 Max, scores 1501.9, so the gap to the top is 83.9 points. The median model in this index scores 1373.8, which puts Llama 3.3 Instruct above the midpoint.
Meta AI models average 1342.5 on this benchmark. Llama 3.3 Instruct is 75.5 points above that provider line, so it is a stronger result than the average Meta AI model.
Among multimodal models, the average is 1409.6. Llama 3.3 Instruct is 8.4 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 1418.0 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 $4.33 per million input tokens, Llama 3.3 Instruct is above the median input price of $4.00 across the 600 models scored on Arena Elo. 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 Instruct | Alibaba Cloud | 1000000 | $5.06 | 79.8 |
| Qwen3-Coder Instruct | Alibaba Cloud | 128000 | $4.66 | 78.1 |
| GLM-4.5 Chat | Zhipu AI | 65536 | $4.53 | 78.3 |
| ERNIE 4.0 Pro | Baidu | 200000 | $12.58 | 77.5 |
| Llama 3.3 Instruct | Meta AI | 128000 | $4.33 | 76.6 |
| DeepSeek-V3.2 Base | DeepSeek | 128000 | $5.05 | 80.2 |
| Reka Core Max | Reka AI | 128000 | $13.41 | 79.3 |
Other benchmarks for Llama 3.3 Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 77.7 | 254 | 58th | |
| GPQA Diamond | 70.1 | 257 | 57th | |
| SWE-bench Verified | 63.7 | 239 | 60th | |
| HumanEval | 82.1 | 185 | 69th | |
| MATH-500 | 81.9 | 168 | 72th | |
| AIME 2025 | 76.9 | 131 | 78th | |
| LiveCodeBench | 67.1 | 247 | 59th | |
| IFEval | 85.4 | 176 | 71th | |
| TruthfulQA | 79.0 | 219 | 64th | |
| HellaSwag | 86.9 | 182 | 70th | |
| MMMU | 72.0 | 245 | 59th |
FAQ
What score does Llama 3.3 Instruct get on Arena Elo?
Llama 3.3 Instruct scores 1418.0 on Arena Elo in this index. The benchmark average is 1349.9 and the top score is 1501.9.
How does Llama 3.3 Instruct rank on Arena Elo?
It ranks #157 of 600 models, in the 74th percentile. The leader is Claude Opus 4.5 Max at 1501.9.
Is Llama 3.3 Instruct good at Arena Elo?
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 Arena Elo score calculated?
Arena Elo is a preference 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 3.3 Instruct cost?
Llama 3.3 Instruct costs $4.33 per million input tokens and $19.59 per million output tokens.