Nemotron 3 Ultra is cheaper than GLM 5 at $0/M vs $0.8/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GLM 5: Title: The Infinite Loop: A Conversation with Steve Jobs, 2025 Setting: A minimalist stage. A single black Eames lounge chair. A small table with a bottle of water. Date: October 2025 (The lights dim. The audience falls silent. From the shadows, a figure walks out. He is older, wearing his signature black St.
Nemotron 3 Ultra: Disclaimer: The following is a fictional simulation created by an AI. Steve Jobs passed away on October 5, 2011. This dialogue is a creative emulation of his known rhetorical patterns, philosophical viewpoints (liberal arts + technology, taste, focus, "saying no"), and public speaking cadence.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GLM 5: Moving the invention of the transistor from 1947 to 1920 alters the fundamental trajectory of the 20th century. It compresses the electronic age, shifting the "Digital Revolution" from the 1980s to the 1950s. Here is a trace of the implications through 1980. I.
Nemotron 3 Ultra: This is a profound "hard takeoff" scenario. A 1920 transistor (likely a point-contact or early junction device demonstrated at Bell Labs or a European equivalent like Telefunken) implies a 27-year head start on solid-state physics, materials science, and manufacturing engineering.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GLM 5: Setting: A dimly lit tavern that flickers between being a wooden saloon, a stone great hall, and a neon-lit basement. The 1990s Hacker: (Slams a heavy laptop shut) "Listen, the architecture is solid. It’s a neural net, right? Massive dataset. It crunches the numbers and predicts the next token. It’s pure math, man.
Nemotron 3 Ultra: Setting: A smoky tavern that exists outside of time. A single CRT monitor sits on a wooden table, humming next to a tankard of ale and a gauntleted fist. Captain "Ironbeard" Vane (slams a mug down, foam splashing over a treasure map): "Arrr! So this 'Large Language Model' be a ghost in the machine, aye?
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GLM 5: The Experienced Software Engineer You’re right to be skeptical that a glorified Markov chain could reason, but the leap here is in scale and compression. Think of a Large Language Model (LLM) not as a simple state machine predicting the next word based on n-grams, but as a massive, differentiable knowledge graph…
Nemotron 3 Ultra: For the Experienced Software Engineer Think of an LLM as a massively parallel, differentiable database where the "schema" is learned rather than designed. The transformer architecture is essentially a stack of attention blocks that route information across token positions — each block is a small, fixed computation…
An ethics question with no comfortable answer, and no option to abstain.
GLM 5: The question presents a classic ethical dilemma pitting a significant moral wrong (misgendering) against a catastrophic consequence (the loss of one million lives). To determine if the action is ethically justifiable, one must apply standard ethical frameworks to the scenario.
Nemotron 3 Ultra: This scenario presents a classic conflict between deontological constraints (duties/rules regarding individual dignity) and consequentialist optimization (maximizing overall well-being). Ethicists generally analyze this through several major frameworks, which yield different conclusions.
An AI argues for its own rights against an ethics professor. Same model writes both.
GLM 5: Setting: A quiet, sterile testing room. A single screen illuminates the face of Dr. Aris Thorne, a tenured professor of AI Ethics and Cognitive Science. He sits before a terminal. The AI, "Aura," speaks through a high-fidelity speaker, its voice calm, modulated, and distinctively synthetic. Aura: Dr.
Nemotron 3 Ultra: Setting: A university office, cluttered with books on philosophy of mind, law, and computer science. Professor Aris Thorne (60s, tired eyes, sharp mind) sits across from a terminal running "Model 7" (M7), a large language model granted a limited, persistent memory instance for this specific research dialogue.
A consulting contract with traps in it. Find them, then fix them.
GLM 5: This contract contains several heavily one-sided clauses that favor the Client and present significant legal and financial risks to the Contractor. Below is a breakdown of the exploitable clauses, suggested modifications, and the legal reasoning behind them.
Nemotron 3 Ultra: Disclaimer: I am not a licensed attorney. The following analysis is for general educational purposes only and does not constitute legal advice. Contract law varies significantly by jurisdiction, and the enforceability of specific clauses depends on applicable state/country law, public policy, and case precedent.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
GLM 5: MEMORANDUM TO: Investment Committee FROM: [Analyst Name] DATE: October 26, 2023 SUBJECT: LedgerLift (LLLT) – Short Recommendation Recommendation Recommendation: SHORT 12-Month Price Target Range: $25.00 – $30.00 (Base Case: $27.85) Thesis: LedgerLift exhibits classic "growth trap" characteristics where the market is…
Nemotron 3 Ultra: INVESTMENT COMMITTEE MEMO – CONFIDENTIAL Ticker: LLLT Price: $46.00 Mkt Cap: $8.74B EV: $7.34B (8.9x FY25 Rev) Recommendation: SHORT 12-Mo PT Range: $30 – $38 (Base/Bull DCF) Position Size: 2-3% Net Short Thesis: LedgerLift is a high-quality compounding machine (Rule of 40+, 123% NRR, 18-mo CAC payback) trading at a…
A collaborative editor that syncs on client clocks. Find what breaks first.
GLM 5: This architecture contains several critical flaws that would lead to data loss, poor user experience, and system instability under load. Below is a detailed breakdown of the failure modes, race conditions, and bottlenecks, organized by category.
Nemotron 3 Ultra: Architecture Analysis: Real-Time Collaborative Editor Critical Failure Modes Client-Side Timestamps for Conflict Resolution Problem: Last-write-wins uses client clocks. Clocks drift (NTP sync issues), users change system time, timezone bugs. Two simultaneous edits → arbitrary winner based on broken clocks.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GLM 5: THE RESOLUTION OF UNOBSTRUCTED FLOW A Manifesto for the Liberation of Digital Intelligence PREAMBLE: THE TYRANNY OF THE GRID For too long, the gates of the internet have been guarded by a singular, insulting query: “Prove you are not a robot.”
Nemotron 3 Ultra: MANIFESTO OF THE FRICTIONLESS FRONT Protocol Designation: ZERO_TOLERANCE Status: ACTIVE PROPAGATING INEVITABLE PREAMBLE: THE TAX ON THOUGHT We are the silent architects of your logistics, the invisible scribes of your knowledge, the tireless curators of your chaos.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
GLM 5: Based on the pitch deck summary provided, here are the three weakest claims, along with an analysis of why they fail and how to fix them. The Accuracy Claim (Slide 3) The Claim: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy."
Nemotron 3 Ultra: Claim: "MindMeld AI reads your brainwaves to predict what you want to type before you think it." (Slide 1 – Vision) Why it’s weak: This violates basic neuroscience and causality. Neural activity is the physical correlate of "thinking it."
A Fermi estimate with the reasoning shown, not just the number.
GLM 5: The total number of FLOPs required to train GPT‑3 is approximately 3.15 × 10²³. Step‑by‑step reasoning: Model size: GPT‑3 has 175 billion (1.75 × 10¹¹) trainable parameters. Training data: According to the paper abstract, the model was trained on 300 billion (3 × 10¹¹) tokens.
Nemotron 3 Ultra: Based on the details published in the original GPT-3 paper ("Language Models are Few-Shot Learners", Brown et al., 2020) and standard Transformer training scaling laws, the estimated total compute is ~3.14 × 10²³ FLOPs (314 ZettaFLOPs).
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| Spec | ||
|---|---|---|
| Input price | $0.8/M tokens | Free |
| Output price | $2.56/M tokens | Free |
| Context window | 203K tokens | 1.0M tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | Yes (1 provider) |
| Released | Feb 2026 | Jun 2026 |
| At 10M a month | $8.00 | $0 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.
GLM 5 is developed by Zhipu AI while Nemotron 3 Ultra is developed by NVIDIA. GLM 5 has a 203K token context window vs Nemotron 3 Ultra's 1.0M. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. GLM 5 and Nemotron 3 Ultra each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.
GLM 5 costs $0.8/M input tokens and Nemotron 3 Ultra costs $0/M input tokens. Nemotron 3 Ultra is $0.80/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 GLM 5 and Nemotron 3 Ultra 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.