Llama 4 Maverick on LiveCodeBench: score, rank and what it means
Llama 4 Maverick scores 75.7 on LiveCodeBench, placing it #87 of 600 models in the index. The benchmark average is 61.0 and the leader is Claude Sonnet 4.5 Reasoning at 85.1.
Score distribution
The chart shows the top 10 models on LiveCodeBench, with Llama 4 Maverick highlighted. Longer bars mean higher scores.
Top 10 leaderboard
| # | Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|---|
| 1 | Claude Sonnet 4.5 Reasoning | Anthropic | 400000 | $19.48 | 87.7 |
| 2 | Grok 4 Reasoning | xAI | 2000000 | $19.67 | 87.8 |
| 3 | Claude Opus 4.5 | Anthropic | 400000 | $19.42 | 87.7 |
| 4 | GPT-5.2 Max | OpenAI | 1000000 | $21.39 | 89.2 |
| 5 | Grok 4 Turbo | xAI | 1000000 | $19.21 | 86.7 |
| 6 | GPT-5.1 Turbo | OpenAI | 2000000 | $19.19 | 86.1 |
| 7 | GPT-5.2 Pro | OpenAI | 2000000 | $21.32 | 89.6 |
| 8 | GPT-5.2 Reasoning | OpenAI | 1000000 | $20.98 | 87.5 |
| 9 | Claude Opus 4.5 Pro | Anthropic | 200000 | $20.98 | 89.1 |
| 10 | Gemini 3 Pro Max | Google DeepMind | 2000000 | $20.57 | 87.9 |
What this score means
On LiveCodeBench, Llama 4 Maverick sits at #87 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 9.4 points. The median model in this index scores 64.7, which puts Llama 4 Maverick above the midpoint.
Meta AI models average 59.3 on this benchmark. Llama 4 Maverick is 16.4 points above that provider line, so it is a stronger result than the average Meta AI model.
Among multimodal models, the average is 70.3. Llama 4 Maverick is 5.4 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 75.7 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 $5.36 per million input tokens, Llama 4 Maverick is above the median input price of $4.00 across the 600 models scored on LiveCodeBench. 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 |
|---|---|---|---|---|
| Gemini 2.5 Pro Vision | Google DeepMind | 200000 | $14.91 | 82.0 |
| Llama 4 Maverick Instruct | Meta AI | 200000 | $5.33 | 81.7 |
| Claude 3.7 Sonnet Vision | Anthropic | 400000 | $13.96 | 81.0 |
| Kimi K1.5 Chat | Moonshot AI | 128000 | $5.11 | 80.2 |
| Llama 4 Maverick | Meta AI | 200000 | $5.36 | 81.6 |
| Sonar Pro Max | Perplexity | 128000 | $14.01 | 81.1 |
| GPT-5.2 Mini | OpenAI | 200000 | $13.34 | 80.4 |
Other benchmarks for Llama 4 Maverick
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 84.7 | 67 | 89th | |
| GPQA Diamond | 76.8 | 107 | 82th | |
| SWE-bench Verified | 71.8 | 77 | 87th | |
| HumanEval | 87.3 | 83 | 86th | |
| MATH-500 | 82.7 | 151 | 75th | |
| AIME 2025 | 79.6 | 94 | 84th | |
| IFEval | 87.5 | 117 | 81th | |
| TruthfulQA | 82.6 | 115 | 81th | |
| HellaSwag | 88.2 | 134 | 78th | |
| MMMU | 80.4 | 54 | 91th | |
| Arena Elo | 1447.0 | 84 | 86th |
FAQ
What score does Llama 4 Maverick get on LiveCodeBench?
Llama 4 Maverick scores 75.7 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.
How does Llama 4 Maverick rank on LiveCodeBench?
It ranks #87 of 600 models, in the 86th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.
Is Llama 4 Maverick good at LiveCodeBench?
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 LiveCodeBench score calculated?
LiveCodeBench is a coding 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 cost?
Llama 4 Maverick costs $5.36 per million input tokens and $25.19 per million output tokens.