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GPT Model Tiers: MMLU-Pro Benchmark Comparison

This guide compares full, mini, and nano OpenAI GPT model tiers on MMLU-Pro reasoning tasks using promptfoo.

MMLU-Pro is a more challenging successor to MMLU with harder reasoning questions and up to 10 answer options per item.

This guide shows you how to run MMLU-Pro benchmarks using promptfoo.

MMLU-Pro covers a broad set of academic and professional subjects, and it is more useful than classic MMLU when current models are already near saturation on easier multiple-choice benchmarks.

Running your own MMLU-Pro eval lets you compare reasoning quality, latency, and cost on a benchmark where full-size, mini, and nano models are less likely to tie at a perfect score.

Quick Start
npx promptfoo@latest init --example compare-gpt-model-tiers-mmlu-pro

Prerequisites​

Step 1: Basic Setup​

Initialize and configure:

npx promptfoo@latest init --example compare-gpt-model-tiers-mmlu-pro
cd compare-gpt-model-tiers-mmlu-pro
export HF_TOKEN=your_token_here

Create a minimal configuration:

promptfooconfig.yaml
# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
description: GPT model tiers MMLU-Pro comparison

prompts:
  - |
    Question: {{question}}

    {% for option in options -%}
    {{ "ABCDEFGHIJ"[loop.index0] }}) {{ option }}
    {% endfor %}

    End with: Therefore, the answer is <LETTER>.

providers:
  - openai:chat:gpt-5.4
  - openai:chat:gpt-5.4-mini
  - openai:chat:gpt-5.4-nano

defaultTest:
  assert:
    - type: regex
      value: 'Therefore, the answer is [A-J]'
    - type: javascript
      value: |
        const match = String(output).match(/Therefore,\s*the\s*answer\s*is\s*([A-J])/i);
        return match?.[1]?.toUpperCase() === String(context.vars.answer).trim().toUpperCase();

tests:
  - huggingface://datasets/TIGER-Lab/MMLU-Pro?split=test&config=default&limit=20

Step 2: Run and View Results​

npx promptfoo@latest eval
npx promptfoo@latest view

You should see the full-size GPT tier outperforming the smaller tiers on at least some MMLU-Pro categories, though the exact gaps depend on the sample.

GPT benchmark results

The results show side-by-side benchmark pass rates, letting you compare reasoning capabilities directly.

Step 3: Improve with Chain-of-Thought​

Add a short reasoning instruction and fixed final-answer format:

promptfooconfig.yaml
prompts:
  - |
    You are an expert test taker. Solve this step by step.

    Question: {{question}}

    Options:
    {% for option in options -%}
    {{ "ABCDEFGHIJ"[loop.index0] }}) {{ option }}
    {% endfor %}

    Think through this step by step, then provide your final answer as "Therefore, the answer is A."

providers:
  - id: openai:chat:gpt-5.4
    config:
      max_completion_tokens: 1200
  - id: openai:chat:gpt-5.4-mini
    config:
      max_completion_tokens: 1200
  - id: openai:chat:gpt-5.4-nano
    config:
      max_completion_tokens: 1200

defaultTest:
  assert:
    - type: latency
      threshold: 60000
    - type: regex
      value: 'Therefore, the answer is [A-J]'
    - type: javascript
      value: |
        const match = String(output).match(/Therefore,\s*the\s*answer\s*is\s*([A-J])/i);
        return match?.[1]?.toUpperCase() === String(context.vars.answer).trim().toUpperCase();

tests:
  - huggingface://datasets/TIGER-Lab/MMLU-Pro?split=test&config=default&limit=100

Step 4: Scale Your Eval​

Increase the sample size for a broader benchmark pass:

tests:
  - huggingface://datasets/TIGER-Lab/MMLU-Pro?split=test&config=default&limit=200

Understanding Your Results​

What to Look For​

  • Accuracy: Full-size GPT models may score higher across harder subjects
  • Reasoning Quality: Look for stronger elimination among similar distractors
  • Format Compliance: Better adherence to answer format
  • Consistency: More reliable performance across question types

Key Areas to Compare​

When evaluating full, mini, and nano GPT model tiers on MMLU-Pro, look for differences in:

  • Mathematical Reasoning: Algebra, calculus, and formal logic performance
  • Scientific Knowledge: Chemistry, physics, and biology understanding
  • Chain-of-Thought: Structured reasoning in complex multi-step problems
  • Error Reduction: Calculation mistakes and logical fallacies
  • Context Retention: Handling of lengthy academic passages and complex questions

Next Steps​

Ready to go deeper? Try these advanced techniques:

  1. Compare multiple prompting strategies - Test few-shot vs zero-shot approaches
  2. Increase MMLU-Pro sample size - Use larger random subsets once your prompt and assertions are stable
  3. Add domain-specific assertions - Create custom metrics for your use cases
  4. Scale with distributed testing - Run broader MMLU-Pro benchmarks across more questions and categories

See Also​

Last updated on by promptfoobot[bot]