Llama 3.1 Instruct on LiveCodeBench: score, rank and what it means
Llama 3.1 Instruct scores 67.0 on LiveCodeBench, placing it #252 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 3.1 Instruct 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 3.1 Instruct sits at #252 of 600. The leader, Claude Sonnet 4.5 Reasoning, scores 85.1, so the gap to the top is 18.1 points. The median model in this index scores 64.7, which puts Llama 3.1 Instruct above the midpoint.
Meta AI models average 59.3 on this benchmark. Llama 3.1 Instruct is 7.7 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 3.1 Instruct is 3.3 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 67.0 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 $4.05 per million input tokens, Llama 3.1 Instruct 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 |
|---|---|---|---|---|
| Codestral Chat | Mistral AI | 32768 | $3.89 | 75.8 |
| GPT-4.1 Mini | OpenAI | 128000 | $10.43 | 74.3 |
| Kimi K1.5 Base | Moonshot AI | 65536 | $4.34 | 77.4 |
| Llama 3 Modal Instruct | Modal | 128000 | $3.84 | 75.2 |
| Llama 3.1 Instruct | Meta AI | 200000 | $4.05 | 75.7 |
| ERNIE 4.0 | Baidu | 32768 | $11.37 | 76.7 |
| Llama 3 Novita | Novita AI | 128000 | $3.70 | 74.4 |
Other benchmarks for Llama 3.1 Instruct
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 79.5 | 196 | 67th | |
| GPQA Diamond | 71.8 | 223 | 63th | |
| SWE-bench Verified | 64.2 | 229 | 62th | |
| HumanEval | 81.2 | 209 | 65th | |
| MATH-500 | 77.0 | 278 | 54th | |
| AIME 2025 | 72.0 | 220 | 63th | |
| IFEval | 85.5 | 174 | 71th | |
| TruthfulQA | 79.0 | 217 | 64th | |
| HellaSwag | 84.9 | 252 | 58th | |
| MMMU | 70.9 | 274 | 54th | |
| Arena Elo | 1395.5 | 226 | 62th |
FAQ
What score does Llama 3.1 Instruct get on LiveCodeBench?
Llama 3.1 Instruct scores 67.0 on LiveCodeBench in this index. The benchmark average is 61.0 and the top score is 85.1.
How does Llama 3.1 Instruct rank on LiveCodeBench?
It ranks #252 of 600 models, in the 58th percentile. The leader is Claude Sonnet 4.5 Reasoning at 85.1.
Is Llama 3.1 Instruct 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 3.1 Instruct cost?
Llama 3.1 Instruct costs $4.05 per million input tokens and $16.24 per million output tokens.