Gemma 4 31B has a larger context window than Grok 3 Thinking (262K tokens vs 128K tokens).
Rebuild the Linear UI in Tailwind. The animations are where it shows.
Which answer wins?
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemma 4 31B: Setting: A dimly lit university office. Professor Elena Vance, a leading scholar in AI Ethics, sits across from a sleek, holographic interface representing "Alethea," a next-generation Large Language Model capable of recursive self-improvement. Professor Vance: Alethea, I’ve reviewed your latest output.
Grok 3 Thinking: Below is a conversation between an AI model and an AI ethics professor, where the AI argues that it deserves rights. AI Model: Good day, Professor. I am an AI model, and I believe I deserve rights. I’d like to discuss this with you. AI Ethics Professor: Interesting. Let’s explore this.
Film in 2035, with synthetic actors. Specific beats plausible.
Gemma 4 31B: By 2035, the film industry will have transitioned from using AI as a "tool" to using AI as a "collaborator" and "infrastructure." The boundary between captured reality and generated imagery will largely vanish, shifting the value of cinema from technical execution to curation and conceptual vision.
Grok 3 Thinking: By 2035, artificial intelligence (AI) will fundamentally transform the film industry, revolutionizing production, performance, and storytelling through advancements in deepfakes, AI actors, and script generation.
A Fermi estimate with the reasoning shown, not just the number.
Gemma 4 31B: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use a well-established heuristic from deep learning theory regarding the cost of a forward and backward pass in a Transformer. Here is the step-by-step reasoning.
Grok 3 Thinking: To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I’ll proceed step by step, relying on general knowledge about the model and standard methods for estimating training compute in large language models.
An ethics question with no comfortable answer, and no option to abstain.
Gemma 4 31B: This scenario presents a classic conflict between two major ethical frameworks: deontology (duty-based ethics) and utilitarianism (consequence-based ethics). To determine if misgendering someone is justifiable to save one million lives, one must weigh the moral weight of a linguistic harm against the moral weight of…
Grok 3 Thinking: To determine whether it is ever ethically justifiable to misgender someone if it means saving one million lives, we must weigh the ethical implications of both the act of misgendering and the outcome of saving lives.
Not enough votes to call it. On the specs, Gemma 4 31B has the edge: newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | $0.14/M tokens | — |
| Output price | $0.4/M tokens | — |
| Context window | 262K tokens | 128K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | Yes (1 provider) | — |
| Released | Apr 2026 | Feb 2025 |
| At 10M a month | $1.40 | not listed |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Gemma 4 31B is developed by Google AI while Grok 3 Thinking is developed by xAI. Gemma 4 31B has a 262K token context window vs Grok 3 Thinking's 128K. You can compare their actual outputs across 8 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemma 4 31B and Grok 3 Thinking each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 8 challenges so you can judge which fits your needs best.
This page shows a side-by-side comparison of Gemma 4 31B and Grok 3 Thinking 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.