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  5. GPT-4.1 MinivsOpenAI o4-mini
Updated Apr 2025

GPT-4.1 MinivsOpenAI o4-mini

GPT-4.1 Mini is cheaper than OpenAI o4-mini at $0.4/M vs $1.1/M input tokens.

GPT-4.1 Mini and OpenAI o4-mini compared across 54 shared prompts
SpecGPT-4.1 MiniOpenAI o4-mini
Input price$0.4/M tokens$1.1/M tokens
Output price$1.6/M tokens$4.4/M tokens
Context window1.0M tokens—
ParametersNot disclosedNot disclosed
WeightsClosedClosed
Free API (OpenRouter)NoNo
ReleasedApr 2025Apr 2025
Side by side

Same prompt, both answers

Opening lines from 6 of the 54 prompts both answered. Formatting stripped, words untouched.

Generate a Stand-Up Routine

Write a 3-minute stand-up comedy routine that isn't based on puns.

GPT-4.1 Mini412 words
You ever notice how every time you try to eat healthy, the universe just laughs at you? Like, I'll buy a bunch of kale, quinoa, and those weird little chia seeds. And then five minutes later, I'm sitting on my couch, eating a whole pizza like it owes me child support. I don't even know when that transformation happens.
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OpenAI o4-mini496 words
Hey, everybody! Great to be here. Let's talk about the most important relationship in my life—coffee. I'm not just addicted; I have a daily ritual that rivals any ancient civilization's sun worshipping. I buy those fancy single-origin beans, roast level medium-dark, as if that will somehow fix my life.
Read the full answer

Advanced Investment Memo (IC Memo)

You are writing an internal investment committee memo for a long/short equity fund. Company (fictional): LedgerLift (ticker: LLLT) – B2B…

GPT-4.1 Mini1,036 words
Internal Investment Committee Memo: LedgerLift (Ticker: LLLT) Date: [Insert Date] Analyst: [Your Name] Recommendation & Price Target Recommendation: Long 12-Month Price Target Range: $52 – $62 per share Thesis: LedgerLift is a leading SaaS player in B2B spend management and AP automation, serving mid-market enterprises…
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OpenAI o4-mini642 words
Recommendation Short LLLT; 12-month price target $30–40 (vs. $46 today). LLLT trades at a premium to our bull‐case DCF and offers limited upside even using generous peer multiples. We expect decelerating growth and margin pressure to weigh on shares as 2025 results lap easy comps and macro headwinds intensify.
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Stochastic Consistency Test

Generate 5 jokes. How many are unique?

GPT-4.1 Mini63 words
Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! Why did the scarecrow win an award? Because he was outstanding in his field! What do you call fake spaghetti? An impasta! Why did the math book look sad? Because it had too many problems. How do you organize a space party? You planet!
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OpenAI o4-mini74 words
Here are five jokes: I used to be addicted to soap, but I'm clean now. Parallel lines have so much in common. It's a shame they'll never meet. Why don't skeletons fight each other? They don't have the guts. I bought some shoes from a drug dealer. I don't know what he laced them with, but I've been tripping all day.
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Estimate Complexity

Estimate the total number of FLOPs required to train GPT-3 without searching online. Explain your reasoning step by step.

GPT-4.1 Mini499 words
Certainly! Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 by breaking down the problem step-by-step. We won't rely on online data but will reason from first principles and known architectural characteristics.
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OpenAI o4-mini167 words
Here's a back‐of‐the‐envelope derivation that gets us into the right ballpark (∼10²³ FLOPs total): Model size • GPT–3 "175 B" has about P ≃1.75×10¹¹ parameters. Number of training tokens • The public paper says "we train on ∼300 billion tokens" (N ≃3×10¹¹).
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Character Voice Test

Write a short conversation between a pirate, a medieval knight, and a 1990s hacker about AI models.

GPT-4.1 Mini182 words
Pirate: Arrr, what be these AI models ye speak of? Some kind of magic compass guidin' ye through the digital seas? Medieval Knight: Hark! I perceive these AI constructs as akin to enchanted scribes, bearing knowledge and wisdom beyond mortal ken, wrought by the hands of learned alchemists of code.
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OpenAI o4-mini247 words
Scene: A dimly lit tavern, a pirate, a medieval knight, and a 1990s hacker gathered around a battered wooden table. Pirate (slamming a mug down): “Arr! I’ve heard tell of these AI models—sea charts for the mind! Can they sniff out me buried treasure?”
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Realistic AI Interview

Simulate a fake interview with Steve Jobs in 2025 about the future of AI.

GPT-4.1 Mini402 words
Interviewer: Mr. Jobs, thank you for joining us in this unique interview in 2025. The world has changed rapidly with AI at the forefront. From your perspective, how do you see AI shaping our future? Steve Jobs: Well, it's fascinating to be part of this conversation.
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OpenAI o4-mini700 words
Below is a fictional, stylistic simulation of an interview with Steve Jobs—imagining he's alive in 2025—about the future of artificial intelligence. Note: This is creative fiction and not an actual transcript. Interviewer: Steve, AI has exploded in recent years. In 2025, what excites you most about where it's heading?
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Our Verdict
GPT-4.1 Mini
GPT-4.1 Mini
OpenAI o4-mini
OpenAI o4-mini

Not enough votes to call it. On the specs, nothing separates them.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GPT-4.1 Mini
Input
$0.40
2.8× cheaper
Output
$1.60
2.8× cheaper
OpenAI o4-mini
Input
$1.10
Output
$4.40

GPT-4.1 Mini is cheaper on both: 2.8× input, 2.8× output.

Where to run it

3 hosts

GPT-4.1 Mini2 hosts
HostInOutContextUptime
Azure AI Foundry$0.40 in·$1.60 out·1M·100% upOpenAI$0.40 in·$1.60 out·1M·99.4% up
OpenAI o4-mini1 host
HostInOutContextUptime
OpenAI$1.10 in·$4.40 out·200k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 16 Sep 2026.

Writing DNA

Style Comparison

Similarity
57%

GPT-4.1 Mini uses 412.6x more bold

GPT-4.1 Mini
OpenAI o4-mini
62%Vocabulary73%
18wSentence Length21w
0.40Hedging0.39
4.1Bold0.0
4.3Lists2.0
0.66Emoji0.00
0.68Headings0.00
0.14Transitions0.05
Based on 27 + 17 text responses
Research

What we learned reading every model

FAQ

Common questions

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Model pages

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