GPT-5 Tiers Explained: High vs. Medium vs. Low Performance
Z
Zack Saadioui
8/12/2025
The Ultimate Breakdown: GPT-5 High vs. GPT-5 Medium vs. GPT-5 Low Performance Differences
Alright, let's talk about GPT-5. If you’ve been anywhere near the tech world lately, you know the hype has been REAL. OpenAI finally dropped its next-gen model, & honestly, it’s a bit more complicated—& a lot more interesting—than just a single, monolithic "GPT-5."
Turns out, we didn't just get one new model. We got a whole family. And within that family, there are different performance tiers that are crucial to understand, whether you're a developer, a business owner, or just an AI enthusiast trying to figure out what’s what. This isn't just about "smarter" AI; it's about specialized performance for different needs.
So, we're going to break it all down: the "High," "Medium," & "Low" performance tiers of GPT-5. What do they mean? What are the actual, tangible differences? & most importantly, which one should you be using for your specific needs?
Let's get into it.
The Big Picture: It's Not One Model, It's a System
First things first, you need to understand that when we talk about GPT-5, we're not talking about a single entity. OpenAI has released a "unified system" that includes several different models & a smart "router." This router is pretty cool; it analyzes your request's complexity, the tools it might need, & your intent, then sends it to the best model for the job. For example, if you literally tell it to "think hard about this," it'll route your request to a more powerful reasoning model.
In the API, this gets even more granular. Developers have access to a family of models, primarily:
gpt-5: The main, powerful model designed for complex logic & multi-step tasks.
gpt-5-mini: A lighter, more cost-effective version for when speed & efficiency are key.
gpt-5-nano: An ultra-lightweight model optimized for near-instant, low-latency responses.
gpt-5-chat: A variant fine-tuned for enterprise-level conversational applications.
On top of these models, developers can specify a reasoning effort:
1
minimal
,
1
low
,
1
medium
, or
1
high
. This setting gives you even more control over the trade-off between speed, cost, & depth of thought.
So, when we talk about "High," "Medium," & "Low" performance, we're really talking about a combination of the model variant & the reasoning level selected. Let's dissect what each of these tiers looks like in practice.
GPT-5 High: The "Deep Thinker" for Mission-Critical Tasks
This is the top tier, the absolute best performance you can squeeze out of the GPT-5 system. Think of this as the "no-compromise" option.
What it is: GPT-5 High performance is typically achieved by using the main
1
gpt-5
model combined with a
1
high
reasoning effort setting. This configuration is designed for the most demanding tasks that require deep, multi-layered reasoning, accuracy, & a profound understanding of context.
Performance & Benchmarks:
The numbers for this tier are, frankly, staggering. It's where GPT-5 truly shines & sets new state-of-the-art (SOTA) records.
Coding: This is where the High tier blows everything else out of the water. On SWE-bench Verified, a benchmark for real-world software engineering tasks,
1
gpt-5
with high reasoning effort scores a remarkable 74.9%. This is a significant jump from its predecessor, o3, which scored 69.1%. What's even more impressive is the efficiency; it achieves this score using 22% fewer output tokens & 45% fewer tool calls than o3. On Aider Polyglot, which tests code editing across multiple languages, it hits 88%, a massive improvement.
Scientific & Math Reasoning: For tasks that require PhD-level scientific understanding, like the GPQA Diamond benchmark,
1
gpt-5
(high) scores 87.3% with tools. On competition-level math problems (AIME 2025), it achieves a 94.6% score without tools.
Multimodal Reasoning: This tier also excels at understanding complex visual information. On the MMMU benchmark (college-level visual reasoning), it scores 84.2%.
Key Characteristics of High Performance:
Deep Reasoning & Reduced Hallucinations: The "thinking" or high-reasoning mode is what sets this tier apart. The model generates an internal chain-of-thought before giving an answer, which dramatically reduces factual errors & hallucinations. Early tests show this can reduce errors by 4-10 times compared to older models.
Exceptional Instruction Following: It can follow very detailed & nuanced instructions with high accuracy. This is HUGE for complex development tasks or creating highly specific content.
Advanced Tool Use: The high-tier model is a master at using tools. It can reliably chain together dozens of API calls, both in sequence & in parallel, without losing its place. This is what enables true agent-like behavior—completing complex, multi-step workflows autonomously.
Higher Latency & Cost: Here's the trade-off. All this thinking takes time. The time to first token (TTFT), or latency, is noticeably higher in this mode. Artificial Analysis clocks the latency for GPT-5 (high) at around 71.42 seconds to receive the first token. It's also the most expensive option, as you're not just paying for input & output tokens, but also for the "reasoning tokens" generated internally, which are billed as output tokens.
When to Use GPT-5 High:
You'd use this tier for tasks where accuracy & depth are non-negotiable.
Complex Software Development: Fixing deep-seated bugs in a large codebase, architecting a new software component, or performing complex refactoring.
High-Stakes Content Creation: Writing legal documents, detailed financial reports, or in-depth technical documentation where errors could have serious consequences.
Advanced AI Agents: Building autonomous agents that need to perform a series of complex tasks, like reading a document, summarizing it, scheduling a meeting based on the summary, & then notifying participants.
GPT-5 Medium: The "All-Rounder" for Everyday Excellence
This is the workhorse tier. It strikes a fantastic balance between high-end performance & practical efficiency, making it the go-to choice for a wide range of professional applications.
What it is: GPT-5 Medium performance is what you get when you use the main
1
gpt-5
model with a
1
medium
reasoning effort, or potentially the
1
gpt-5-mini
model for less complex tasks. It's designed to be smart & capable without the intense computational overhead of the High tier.
Performance & Benchmarks:
The Medium tier is still a top performer, often outclassing previous generation models while being more efficient than its High-tier sibling.
Coding: In a code review benchmark by Qodo, the medium-budget variant of GPT-5 scored an impressive 72.2. This shows it's more than capable of handling sophisticated developer tasks, even if it doesn't quite reach the absolute peak of the High tier's 74.9% on SWE-bench.
General Intelligence: On the Artificial Analysis Intelligence Index, GPT-5 (medium) scores a 68, just one point shy of the High version's 69. This indicates that for many knowledge-based & reasoning tasks, the difference in raw intelligence is almost negligible.
Speed & Latency: This is where the Medium tier starts to pull ahead in practicality. The response times are faster than the High tier. While not instantaneous, you're not waiting as long for the model to "think." This makes for a much smoother, more interactive experience.
Key Characteristics of Medium Performance:
Balanced Speed & Quality: This is the sweet spot. You get high-quality, reliable outputs without the noticeable latency of the High tier. The code it generates is still idiomatic & good at multi-file reasoning.
Cost-Effective Power: You get most of the brainpower of the High tier but at a lower cost. This makes it viable for scaling applications that need strong AI capabilities without breaking the bank.
Great for Business Automation: Its ability to handle nuanced tasks efficiently makes it perfect for internal business workflows. Think about automating customer service responses or generating marketing copy.
For businesses looking to leverage this kind of power, this is where solutions like Arsturn come into play. A business could use the GPT-5 Medium tier to power an Arsturn chatbot trained on its own company data. This would allow the chatbot to provide instant, intelligent, & contextually aware customer support 24/7. It could answer detailed product questions, troubleshoot common issues, & even guide users through complex processes—all with a level of understanding that feels human, but with the efficiency of AI.
When to Use GPT-5 Medium:
This tier is ideal for the majority of professional & commercial use cases.
General Content Creation: Writing blog posts, articles, marketing emails, & social media content where quality & nuance are important.
Standard Development Tasks: Writing unit tests, refactoring smaller code snippets, generating boilerplate code, & getting quick bug fixes.
Interactive Customer Support: Powering sophisticated AI chatbots. With Arsturn, businesses can build no-code AI chatbots using this level of AI to engage website visitors, answer questions instantly, & capture leads effectively. The Medium tier ensures the bot is smart enough to handle complex queries without the latency that might frustrate a user.
Data Analysis & Summarization: Summarizing reports, extracting key information from documents, & analyzing customer feedback.
GPT-5 Low: The "Speed Demon" for Real-Time Interaction
This tier is all about one thing: speed. It's designed for applications where latency is the most critical factor & an instant response is more important than deep, philosophical reasoning.
What it is: GPT-5 Low performance is typically achieved by using the
1
gpt-5-mini
or
1
gpt-5-nano
models, often combined with
1
low
or
1
minimal
reasoning effort. The focus here is on delivering an answer as quickly as humanly (or AI-ly) possible.
Performance & Benchmarks:
While you might expect a huge drop-off in quality, the Low tier is surprisingly capable.
Coding: The "minimal" GPT-5 variant, designed for lightweight responsiveness, still achieved a score of 62.7 on the Qodo code review benchmark. This is still a very respectable score, placing it among top performers & highlighting the massive leap in baseline capability.
Speed: This is its main selling point. The
1
minimal
reasoning mode is built for tasks where you need the fastest possible time-to-first-token. The
1
gpt-5-nano
model is specifically optimized for this, making it around 25 times cheaper than the main model for latency-sensitive work.
Key Characteristics of Low Performance:
Near Real-Time Latency: Responses feel almost instantaneous. This is critical for use cases where the AI is part of a fluid, back-&-forth conversation or a fast-paced workflow.
Optimized for Simple Tasks: This tier excels at deterministic, lightweight tasks like data extraction, formatting text, simple classification, & short rewrites.
Highly Cost-Effective: The nano & mini models are significantly cheaper, making them ideal for high-volume applications where the cost per query needs to be extremely low.
Less Nuanced: The trade-off for speed is a reduction in deep reasoning. It might not catch the subtle nuances in a complex prompt or generate as creative or in-depth responses as the higher tiers. It’s more of a quick-answer machine than a deep-thinking partner.
When to Use GPT-5 Low:
This tier is perfect for high-frequency, low-complexity tasks.
Real-Time Website Engagement: This is a prime use case for platforms like Arsturn. A business can use a GPT-5 Low-powered chatbot to instantly greet website visitors, answer simple FAQs (like "What are your business hours?"), & route conversations to human agents. The near-zero latency ensures a seamless user experience that doesn't feel like you're waiting on a slow, clunky bot.
Data Formatting & Extraction: Quickly pulling specific pieces of information from a block of text, like extracting names & dates from an email.
Simple Chatbot Commands: Handling basic commands in a conversational interface, like "show me my last order" or "what's the weather?"
IDE Code Suggestions: Providing instant, in-line code completions & suggestions as a developer is typing.
The Bottom Line: It's All About Choosing the Right Tool for the Job
So, there you have it. GPT-5 isn't a single hammer; it's a full toolkit. The "High," "Medium," & "Low" performance tiers aren't just about good, better, best—they're about different tools for different jobs.
GPT-5 High is your precision power tool for the most complex, high-stakes work where accuracy is everything.
GPT-5 Medium is your versatile, reliable multi-tool, perfect for the vast majority of professional tasks that require a smart & efficient partner.
GPT-5 Low is your lightning-fast screwdriver for quick, simple jobs where speed is the name of the game.
Understanding these differences is key to effectively leveraging this new generation of AI. Whether you're building a next-gen application, looking to automate business processes, or simply trying to get the best possible response from a chatbot, knowing which tier to use will make all the difference.
For businesses, the exciting part is how accessible this tiered performance is. You can now build incredibly sophisticated customer experiences without always needing the most expensive, high-latency model. You could use a Arsturn chatbot powered by the Low tier for instant greetings, then seamlessly switch to the Medium tier when the conversation gets more complex, providing a smart, scalable, & cost-effective solution to boost conversions & provide personalized customer experiences 24/7.
Hope this was helpful & gives you a clearer picture of the new GPT-5 landscape. It's a pretty exciting time to be building with this stuff. Let me know what you think