Llama 4 Maverick Base on MMLU-Pro: score, rank and what it means
Llama 4 Maverick Base scores 83.3 on MMLU-Pro, placing it #100 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 4 Maverick Base 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 4 Maverick Base sits at #100 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 8.3 points. The median model in this index scores 76.1, which puts Llama 4 Maverick Base above the midpoint.
Meta AI models average 73.0 on this benchmark. Llama 4 Maverick Base is 10.3 points above that provider line, so it is a stronger result than the average Meta AI model.
Among multimodal models, the average is 80.0. Llama 4 Maverick Base is 3.3 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 83.3 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 $4.68 per million input tokens, Llama 4 Maverick Base is above 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 |
|---|---|---|---|---|
| Claude Haiku 4.5 Reasoning | Anthropic | 128000 | $15.11 | 81.4 |
| DeepSeek-R1 Chat | DeepSeek | 400000 | $5.62 | 82.4 |
| Qwen3 Chat | Alibaba Cloud | 128000 | $4.75 | 79.1 |
| Kimi K2 | Moonshot AI | 128000 | $5.85 | 83.0 |
| Llama 4 Maverick Base | Meta AI | 128000 | $4.68 | 79.1 |
| Mistral Large 3 Chat | Mistral AI | 1000000 | $4.90 | 79.1 |
| Reka Core Pro | Reka AI | 65536 | $13.44 | 79.9 |
Other benchmarks for Llama 4 Maverick Base
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| GPQA Diamond | 71.8 | 224 | 63th | |
| SWE-bench Verified | 67.9 | 148 | 75th | |
| HumanEval | 85.4 | 107 | 82th | |
| MATH-500 | 83.3 | 142 | 76th | |
| AIME 2025 | 77.8 | 117 | 81th | |
| LiveCodeBench | 69.3 | 203 | 66th | |
| IFEval | 87.4 | 122 | 80th | |
| TruthfulQA | 81.2 | 150 | 75th | |
| HellaSwag | 89.2 | 109 | 82th | |
| MMMU | 73.8 | 197 | 67th | |
| Arena Elo | 1425.2 | 129 | 79th |
FAQ
What score does Llama 4 Maverick Base get on MMLU-Pro?
Llama 4 Maverick Base scores 83.3 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.
How does Llama 4 Maverick Base rank on MMLU-Pro?
It ranks #100 of 600 models, in the 83rd percentile. The leader is GPT-5.2 Pro at 91.6.
Is Llama 4 Maverick Base 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 4 Maverick Base cost?
Llama 4 Maverick Base costs $4.68 per million input tokens and $21.03 per million output tokens.