Llama 3 Novita Base on MMLU-Pro: score, rank and what it means
Llama 3 Novita Base scores 74.4 on MMLU-Pro, placing it #344 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 Novita 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 3 Novita Base sits at #344 of 600. The leader, GPT-5.2 Pro, scores 91.6, so the gap to the top is 17.2 points. The median model in this index scores 76.1, which puts Llama 3 Novita Base below the midpoint.
Novita AI models average 69.1 on this benchmark. Llama 3 Novita Base is 5.3 points above that provider line, so it is a stronger result than the average Novita AI model.
Among open-weight models, the average is 72.1. Llama 3 Novita Base is 2.3 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 74.4 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 $3.06 per million input tokens, Llama 3 Novita Base 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 Anyscale Base | Anyscale | 65536 | $3.23 | 72.9 |
| Molmo Instruct | Allen Institute for AI | 32768 | $2.53 | 68.3 |
| Auto Max | OpenRouter | 8192 | $8.41 | 71.2 |
| DBRX Base | Databricks | 32768 | $2.63 | 69.1 |
| Llama 3 Novita Base | Novita AI | 8192 | $3.06 | 70.3 |
| Phi-3.5 Chat | Microsoft | 16384 | $2.64 | 69.0 |
| DBRX Instruct | Databricks | 8192 | $3.21 | 72.4 |
Other benchmarks for Llama 3 Novita Base
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| GPQA Diamond | 67.8 | 314 | 48th | |
| SWE-bench Verified | 56.9 | 368 | 39th | |
| HumanEval | 73.3 | 368 | 39th | |
| MATH-500 | 71.9 | 367 | 39th | |
| AIME 2025 | 62.5 | 371 | 38th | |
| LiveCodeBench | 60.4 | 363 | 40th | |
| IFEval | 79.2 | 351 | 42th | |
| TruthfulQA | 76.5 | 293 | 51th | |
| HellaSwag | 82.3 | 341 | 43th | |
| MMMU | 67.7 | 350 | 42th | |
| Arena Elo | 1360.8 | 342 | 43th |
FAQ
What score does Llama 3 Novita Base get on MMLU-Pro?
Llama 3 Novita Base scores 74.4 on MMLU-Pro in this index. The benchmark average is 73.5 and the top score is 91.6.
How does Llama 3 Novita Base rank on MMLU-Pro?
It ranks #344 of 600 models, in the 43rd percentile. The leader is GPT-5.2 Pro at 91.6.
Is Llama 3 Novita Base good at MMLU-Pro?
It is below the median for this benchmark, so test it carefully before relying on it for this capability. 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 Novita Base cost?
Llama 3 Novita Base costs $3.06 per million input tokens and $13.44 per million output tokens.