AI Model Size and Benchmark Scores

Does a larger AI language model always perform better? This page is for students who want to understand how model size relates to benchmark results. It compares 20 models from Hugging Face's Open LLM Leaderboard to show why parameter count alone is not enough to judge performance.

Model data

The data comes from a March 20, 2025 snapshot. These models were selected across several providers and sizes, rather than sampled randomly. All selected evaluations use the same numerical precision, bfloat16, with a chat template enabled (the "instruct" variant, aka the version you'd be using locally).

Parameters are measured in billions. Scores are normalized from 0 to 100, with higher scores indicating better results. The average combines six benchmarks, while IFEval and MATH L5 are shown separately. Normalized scores are not raw accuracy percentages, but they can be used to compare models. The rows are compared below.

Model Parameters (B) Average score IFEval MATH L5
Qwen2.5-0.5B-Instruct 0.500 8.14 30.71 0.00
Qwen2.5-1.5B-Instruct 1.500 18.43 44.76 22.05
Qwen2.5-3B-Instruct 3.000 27.16 64.75 36.78
Llama-3.2-3B-Instruct 3.213 24.20 73.93 17.67
Phi-3-mini-4k-instruct 3.821 25.97 56.13 11.63
Mistral-7B-Instruct-v0.1 7.242 12.77 44.87 2.27
Mistral-7B-Instruct-v0.2 7.242 18.51 54.96 3.02
Mistral-7B-Instruct-v0.3 7.248 19.23 54.65 3.85
Phi-3-small-8k-instruct 7.392 32.34 64.97 18.87
Qwen2.5-7B-Instruct 7.616 35.20 75.85 50.00
Meta-Llama-3-8B-Instruct 8.030 23.91 74.08 8.69
gemma-2-9b-it 9.000 32.07 74.36 19.49
Phi-3-medium-4k-instruct 13.960 33.10 64.23 19.56
Qwen2.5-14B-Instruct 14.770 41.31 81.58 54.76
gemma-2-27b-it 27.227 36.17 79.78 23.87
Qwen2.5-32B-Instruct 32.764 46.60 83.46 62.54
Llama-3.1-70B-Instruct 70.554 43.41 86.69 38.07
Llama-3.3-70B-Instruct 70.554 44.85 89.98 48.34
Meta-Llama-3-70B-Instruct 70.554 36.37 80.99 24.47
Qwen2.5-72B-Instruct 72.706 47.98 86.38 59.82
Table 1. Selected model evaluations, sorted by parameter count. Source: Hugging Face Open LLM Leaderboard, March 20, 2025. Scores are rounded to two decimals.

Comparing a smaller and a larger model

Model Parameters (B) Average score
Qwen2.5-14B-Instruct 14.770 41.31
Meta-Llama-3-70B-Instruct 70.554 36.37
Table 2. The two shaded rows from the main dataset.

More parameters do not guarantee a higher score

Qwen2.5-14B-Instruct has 14.770 billion parameters and an average score of 41.31. Meta-Llama-3-70B-Instruct has 70.554 billion parameters and scores 36.37. The smaller model scores 4.94 points higher despite having about 79% fewer parameters.

This comparison shows why students should check benchmark results instead of assuming the largest model will perform best. However, these models come from different families and differ in more than size. The data does not establish what caused the score difference or how the models would perform on every task.

Glossary

Parameters
Numerical values learned during training. Parameter count is one way to describe the size of an AI model.
Benchmark
A set of tasks and scoring rules used to evaluate a model's performance.
Normalized score
A result adjusted to the leaderboard's 0–100 scale, accounting for the benchmark's baseline.
IFEval
A benchmark that tests whether a model follows verifiable instructions, such as using a required format.
MATH L5
The level-5 subset of the MATH benchmark, which contains challenging competition mathematics problems.