Compare GPT-5 Models: Reasoning, Speed & Cost

We explore the differences between OpenAI’s GPT-5 family (e.g., GPT-5, GPT-5-mini, and GPT-5-nano) by gathering benchmark performance and the costs to run the benchmarks.

Price vs. success: Key takeaways

We used the GPT-5 family in our analysis. We used six benchmarks across various areas, including reasoning, coding, instruction-following, and math.

Our analysis revealed:

• On average across benchmarks, GPT-5 (high) and GPT-5 (medium) deliver nearly identical success rates (65% vs. 64%), yet GPT-5 (high) costs almost twice as more ($511 vs. $280). They are followed by GPT-5-mini (high), GPT-5 (low), and GPT-5-mini (medium), with success rates of 62%, 61%, and 60%, respectively, at lower prices of $105, $90, and $28. This shows that by accepting a ~5% drop in success rate, tasks can be completed at up to 18 times lower cost by switching from GPT-5 (high) to GPT-5-mini (medium).
• GPT-5-mini (high) outperforms GPT-5 (low) in nearly every benchmark, and does so at the same or lower cost. In IFBench, success rates are 75% vs. 67%; in AIME 2025, 97% vs. 83%; in Humanity’s Last Exam, 20% vs. 18%; and in GPQA Diamond, 83% vs. 81%. They tie on SciCode at 39%, yet GPT-5-mini (high) still comes in at a lower cost.
• The most expensive model, GPT-5 (high), outperforms the second-best performer on three benchmarks, and even then, the margin is no greater than 3%. In all other benchmarks, it is outperformed by cheaper alternatives.

Key differences between GPT models

The three models differ primarily in reasoning depth, speed, and cost. The new features can be used across all models, but their impact varies by model.

Reasoning

• GPT-5 provides the strongest reasoning capability. It is appropriate for complex, multi-step problems in coding, scientific analysis, or decision support.
• GPT-5 mini offers strong reasoning for structured prompts with predictable task boundaries.
• GPT-5 nano has moderate reasoning performance and works best on tasks that do not require deep analysis.
• Minimal reasoning mode can be used with all models and provides the most significant benefit for GPT-5 nano and GPT-5 mini, given their speed advantage.

Speed

• GPT-5 nano is the fastest option and is effective for real-time or large-scale workloads.
• GPT-5 mini balances speed with reasoning, making it suitable for regular production workloads.
• GPT-5 is slower because it performs more internal reasoning, but this results in more precise output.
• Minimal reasoning mode can further reduce latency, particularly for nano.

Cost

• GPT-5 nano has the lowest cost per token. It is preferred for high-volume tasks such as batch classification or summarization.
• GPT-5 mini sits in the mid-range, offering a balance between capability and cost.
• GPT-5 is the most expensive model and is typically used when accuracy and consistency take priority.
• Verbosity settings influence cost because higher verbosity produces more output tokens.

Cite this research

Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.

@misc{ermut2026,
  author = {Ermut, Sıla and Alper, Şevval},
  title  = {{Compare GPT-5 Models: Reasoning, Speed & Cost}},
  year   = {2026},
  month  = aug,
  howpublished    = {\url{https://aimultiple.com/llm-parameters}},
  note   = {AIMultiple. Retrieved August 21, 2026}
}

Changelog

2 updates

2025

1. Dec 26

   Expanded the "What are LLM parameters?" section with details on model size, training fundamentals, and top k sampling.

2. Nov 21

   Removed the temperature parameter from the list of key parameters.

Sıla Ermut

Industry Analyst

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Sıla Ermut is an industry analyst at AIMultiple covering AI models, AI infrastructure, AI governance, and enterprise AI applications. Her research focuses mostly on the use of AI in marketing, healthcare, supply chains, and sustainability.
She previously worked as a recruiter in project management and consulting firms. Sıla holds a Master of Science degree in Social Psychology and a Bachelor of Arts degree in International Relations.

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Researched by

Şevval Alper

AI Researcher

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Şevval is an AI researcher at AIMultiple. She has previous research experience in pseudorandom number generation using chaotic systems.
Şevval focuses on AI coding tools, AI agents, and quantum technologies.

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