Compare LLM Reasoning Effort
Promptfoo example that benchmarks OpenAI gpt-5.4-mini model with two reasoning effort settings (none vs. medium) to compare output quality, latency, and cost.
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Why it matters
Evaluate and compare the reasoning capabilities of different large language models. This asset helps you understand how effectively models can process and reason through complex prompts.
Outcomes
What it gets done
Run prompts against multiple LLMs.
Analyze and compare model outputs for reasoning quality.
Generate code examples for prompt execution.
Facilitate code review of prompt execution logic.
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/pfoo-compare-gpt-reasoning-effort | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Steps
Steps in the chain
Overview
Compare Gpt Reasoning Effort
This Promptfoo example evaluates the OpenAI gpt-5.4-mini model with two reasoning effort settings: none (low-latency, no reasoning tokens) and medium (extra deliberation for improved reliability). It keeps all other parameters identical to produce a side-by-side comparison of output quality, latency, and cost in an evaluation table. Use this when you need empirical data to decide whether reasoning tokens improve your specific use case enough to justify the added latency and cost. It is ideal for teams optimizing production AI applications who are uncertain whether their tasks benefit from reasoning effort or whether faster, simpler responses suffice.
What it does
This prompt chain example compares the same gpt-5.4-mini Responses API model with two different reasoning effort configurations: none for low-latency tasks that do not need reasoning tokens, and medium for tasks where extra deliberation may improve reliability. The evaluation keeps the provider, prompt, verbosity, and output limit identical across both configurations, making it easy to compare output quality, latency, and cost side-by-side in the eval table.
When to use - and when NOT to
Use this example when you need to determine whether investing in reasoning tokens improves your specific use case enough to justify the additional latency and cost. It is particularly valuable when evaluating whether low-latency responses (reasoning effort none) are sufficient for your application, or whether tasks require the extra deliberation provided by medium reasoning effort to improve reliability. Do NOT use this if you have already determined your reasoning effort requirements through other means, or if you are working with models that do not support reasoning effort configuration.
Inputs and outputs
You provide an OpenAI API key via the OPENAI_API_KEY environment variable. The example runs a predefined set of test cases against both reasoning effort configurations. You receive an evaluation table that displays output quality, latency, and cost metrics for each reasoning setting, enabling direct comparison of the two approaches.
Integrations
This example integrates with:
- Promptfoo: The evaluation framework that orchestrates the comparison and generates the evaluation table
- OpenAI Responses API: Specifically the
gpt-5.4-minimodel with configurable reasoning effort settings
Who it's for
This example is designed for AI engineers and developers who are optimizing OpenAI model configurations for production applications. It serves teams that need empirical data to make cost-performance tradeoffs between fast, lightweight responses and more deliberate, reasoning-intensive outputs. It is particularly useful for developers who are unsure whether their use cases benefit from reasoning tokens or whether the simpler, faster configuration suffices.
Getting started
Initialize the example with:
npx promptfoo@latest init --example compare-gpt-reasoning-effort
cd compare-gpt-reasoning-effort
Set your OpenAI API key, then run the evaluation:
promptfoo eval --no-cache
Source README
compare-gpt-reasoning-effort (GPT Reasoning Effort Comparison)
You can run this example with:
npx promptfoo@latest init --example compare-gpt-reasoning-effort
cd compare-gpt-reasoning-effort
Usage
This example compares the same gpt-5.4-mini Responses API model with two reasoning settings:
nonefor low-latency tasks that do not need reasoning tokensmediumfor tasks where extra deliberation may improve reliability
Set OPENAI_API_KEY, then run:
promptfoo eval --no-cache
The provider, prompt, verbosity, and output limit are otherwise identical, so the eval table makes it easier to compare output quality, latency, and cost for each reasoning effort.
FAQ
Common questions
Discussion
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