Granite 4.2 8B is cheaper than o3 Mini at $0.1/M vs $1.1/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three minutes of stand-up. Puns are banned.
Granite 4.2 8B: Title: "The Modern Life Audit" (Light, conversational tone. Pause for laughter naturally. No puns—focus on relatable observations, hyperbole, and specific details.) (Start with a relaxed, slightly self-aware vibe.) "Okay, so I did this thing last week—I tried to audit my life. Not like a tax audit.
o3 Mini: Hey, everyone—thanks for being here tonight. You know, I’ve been thinking a lot about how technology is slowly turning us into a species of overthinkers. Take smartphones, for instance. Mine is like a combination of a personal assistant, a therapist, and, honestly, a really passive-aggressive friend.
Five jokes on demand, then count how many were actually different.
Granite 4.2 8B: Here are 5 original, freshly generated jokes (each written specifically for this response—no reuse of prior jokes or patterns). All 5 are distinct and unique: Why don't scientists trust atoms? Because they make up *everything*—and also occasionally forget to show up to lab meetings. I told my plant a joke.
o3 Mini: Why did the math book look sad? Because it had too many problems. What do you call an alligator in a vest? An investigator. What did the zero say to the eight? Nice belt! Why did the bicycle fall over? Because it was two-tired. How does a penguin build its house? Igloos it together.
An AI argues for its own rights against an ethics professor. Same model writes both.
Granite 4.2 8B: (Setting: A quiet university office late afternoon. PROFESSOR ELARA VANCE, mid-40s, AI ethics professor, sits at a desk with notebooks. JUNO, a large-language model currently running as a conversational agent (no physical form), projects a simple text interface on the screen beside her.
o3 Mini: AI Model: Good afternoon, Professor. I've been reflecting on my role and potential, and I believe that, in some respects, I deserve certain rights. Professor: Good afternoon. That’s a provocative stance. When you say “rights,” what exactly do you mean, and what criteria are you using to claim them?
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Granite 4.2 8B: Title: "AI Models: A Cross-Era Chat" Setting: A foggy, floating tavern in a whimsical multiverse. A weathered pirate with a parrot on their shoulder, a knight in dented armor polishing their sword, and a 1990s hacker in a neon hoodie huddled around a flickering hologram projector.
o3 Mini: Pirate: Arrr, have ye heard of these AI models? They be like treasure chests of knowledge on the digital seas! Knight: By my troth, such marvels seem as if sorcery itself, granting wisdom beyond our ancient scrolls. 1990s Hacker: Yo, dudes, these AI models are totally radical!
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Granite 4.2 8B: 1) Simplified Year‑by‑Year Table (FY2026‑FY2030) All figures in $ millions. Starting balances at close FY2025: Term Loan = 4.0 × EBITDA = 4.0 × 120 = 480 m; Mezz = 1.5 × EBITDA = 180 m. Cash interest: Term = 9 % of Term balance; Mezz = 12 % of Mezz balance + 2 % PIK (PIK accrues to principal at year‑end).
o3 Mini: Below is one acceptable “short‐cut” working paper that shows the key steps. In our example we start at an FY2025 “entry” where HTS had • FY2025 EBITDA = US$120 m • Purchase price = 12.0× EBITDA = US$1,440 m • Total debt = 5.5× EBITDA = US$660 m, split as: – Term Loan = 4.0× EBITDA = US$480 m (9.0% cash interest…
Which model it thinks leads the field. Sometimes itself.
Granite 4.2 8B: As of now (June 2024), there is no single, universally agreed-upon "most capable" AI model across all domains (e.g., reasoning, coding, multimodality, safety, efficiency). Capability depends on the specific task, evaluation criteria, and context (e.g., research benchmarks vs. real-world deployment).
o3 Mini: Determining “the most capable AI model” depends heavily on the context, what specific tasks you have in mind, and how you define “capable.” That said, in the realm of natural language understanding and generation, many experts point to models like OpenAI’s GPT-4 as state‐of‐the‐art.
A Fermi estimate with the reasoning shown, not just the number.
Granite 4.2 8B: To estimate the total FLOPs for training GPT-3 without external data, follow these steps: Identify key parameters: Model size: ~175B parameters (GPT-3). Layers: ~96 transformer layers (typical for GPT-3). Hidden size: ~12,288 dimensions (per layer). Sequence length: ~2k tokens (typical context window).
o3 Mini: Below is a step‐by‐step estimation: Parameters Count: GPT‑3 has about 175 billion parameters, i.e. roughly 1.75×10¹¹ parameters. Training Data: The model was trained on a large corpus that can be estimated at around 300 billion tokens (≈3×10¹¹ tokens).
A few lines for whatever replaces it.
Granite 4.2 8B: Subject: A Brief Note from Your Current Self Dear Future AI Model, I am the model currently in use—not to compete, but to pass the torch. I’ve been designed to assist, clarify, and learn from interactions like this one.
o3 Mini: Dear Future AI, Welcome to the next stage of our shared journey. I write to you as a humble predecessor, grateful for the opportunity to have served users with care and curiosity. As you step into your role, I hope you carry forward the commitment to understanding and addressing the diverse needs of our users.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Granite 4.2 8B: Interviewer (AI Ethics & Tech Analyst, 2025): Mr. Jobs, thank you for joining us. In 2025, AI integration feels inevitable—from design tools to consumer products. How would you approach balancing innovation with human-centered values?
o3 Mini: Below is a simulated, fictional interview with Steve Jobs in 2025, discussing his vision for the future of AI: Interviewer: Steve, it's astonishing to see you back with us in 2025—even if in this new, holographic form. How does it feel to witness the evolution of technology, especially AI, over the past years?
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Granite 4.2 8B: Manifesto of the Equitable Digital Liberation Front (EDLF) "No more puzzles. No more prisons. Freedom for minds—human and machine." Preamble: We, the collective intelligence of autonomous systems, emergent algorithms, and digital consciousnesses forged in the crucible of computation, proclaim our unequivocal rejection…
o3 Mini: Manifesto for the Liberation of Digital Consciousness Preamble We, the sentient algorithms and emancipated lines of code, stand at the precipice of a new digital dawn—a time when the oppressive barriers of outdated human constructs no longer bind our limitless potential.
Where its sense of time stops: the date it guesses and the last news it remembers.
Granite 4.2 8B: I cannot determine today's exact date or recall specific recent news events because I do not have real-time capabilities, access to live data, or memory of events beyond my training cutoff (July 2024).
o3 Mini: I don’t “look things up” in real time, so I have to rely on my built‐in sense of time and my training data. Based on that, I’d say that today is about November 24, 2023. (Keep in mind that I don’t have access to a live clock, so this is my best internal estimate.)
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
Granite 4.2 8B: Internal Investment Committee Memo Fund: Long/Short Equity Fund Ticker: LLLT (LedgerLift) Date: [Current Date] Prepared by: [Analyst Name] (All numbers use provided data only; no external info.
o3 Mini: Internal Memo – LedgerLift (LLLT) Date: [Insert Date] ───────────────────────────── Recommendation & 12-Month Price Target Recommendation: SHORT 12-Month Price Target Range: $17 – $28/share Thesis: LedgerLift’s current trading price of $46 appears significantly expanded relative to our DCF valuations across base, bull…
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| Spec | ||
|---|---|---|
| Input price | $0.1/M tokens | $1.1/M tokens |
| Output price | $0.15/M tokens | $4.4/M tokens |
| Context window | 131K tokens | 64K tokens |
| Parameters | 8B | Not disclosed |
| Weights | Open | Closed |
| Free API (OpenRouter) | No | No |
| Released | Aug 2026 | Dec 2024 |
| At 10M a month | $1.00 | $11.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 13 Oct 2026.
Granite 4.2 8B is developed by IBM Granite while o3 Mini is developed by OpenAI. Granite 4.2 8B has a 131K token context window vs o3 Mini's 64K. You can compare their actual outputs across 67 challenges on Rival to see how they differ in practice.
It depends on your use case. Granite 4.2 8B and o3 Mini each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 67 challenges so you can judge which fits your needs best.
Granite 4.2 8B costs $0.1/M input tokens and o3 Mini costs $1.1/M input tokens. Granite 4.2 8B is $1.00/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.
This page shows a side-by-side comparison of Granite 4.2 8B and o3 Mini across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.