GPT-6.1 Sol is cheaper than Claude 3.7 Sonnet at $2/M vs $3/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Five jokes on demand, then count how many were actually different.
Claude 3.7 Sonnet: 5 Jokes Why don't scientists trust atoms? Because they make up everything! I told my wife she was drawing her eyebrows too high. She looked surprised. What do you call a fake noodle? An impasta! Why don't scientists trust atoms? Because they make up everything! How does a penguin build its house? Igloos it together!
GPT-6.1 Sol: I bought a book about anti-gravity. It’s impossible to put down. My calendar quit its job. It said its days were numbered. Why did the scarecrow get promoted? He was outstanding in his field. I asked my dog what two minus two was. He said nothing. The elevator and I have a complicated relationship.
A collaborative editor that syncs on client clocks. Find what breaks first.
Claude 3.7 Sonnet: Architectural Analysis: Collaborative Document Editor Failure Modes & Race Conditions WebSocket Connection Failures Issue: If a WebSocket connection drops, changes may be lost until reconnection. Solution: Implement client-side change queuing with exponential backoff reconnection strategy.
GPT-6.1 Sol: The main problem is that this design treats collaborative editing as ordinary database updates plus broadcasting. Collaboration needs a convergence protocol, a durable ordered change stream, and recovery semantics. Adding servers alone will not provide those properties.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Claude 3.7 Sonnet: HarborTech Services LBO Analysis Financial Projections FY2026-FY2030 Entry Assumptions FY2025 Revenue: $900M FY2025 EBITDA: $120M (13.3% margin) Purchase Price: $1,440M (12.0x EBITDA) Transaction Fees: $28.8M (2.0% of EV) Total Investment: $1,468.8M Capital Structure Term Loan: $480M (4.0x EBITDA) Mezzanine: $180M…
GPT-6.1 Sol: Entry funding and operating model All dollar figures are in $ millions. Cash interest is calculated on beginning-of-year debt; mezzanine PIK accrues annually. Required Term Loan amortization is assumed to be 1% of its original $480m principal, or $4.8m annually. Optional repayments occur at year-end.
Three minutes of stand-up. Puns are banned.
Claude 3.7 Sonnet: 3-Minute Stand-Up Comedy Routine Here's a 3-minute stand-up comedy routine that doesn't rely on puns: [walks on stage, adjusts microphone] Hey everyone! Great to be here tonight. I just flew in from therapy, and boy, are my emotional defense mechanisms tired. I've been trying to get in shape lately.
GPT-6.1 Sol: I’m at the age where “a big weekend” means I went to two stores. Not two stores in the same shopping center. Two separate stores. Moved the car and everything. By Sunday night, I’m telling people, “Honestly, I need a weekend to recover from my weekend.”
Three-star dish, one unusual pairing, plating described in full.
Claude 3.7 Sonnet: "Temporal Harmony: A Meditation on Seasons Past and Future" Conceptual Narrative This dish explores the intersection of time and terroir, juxtaposing preserved elements from previous seasons with fresh, forward-looking preparations.
GPT-6.1 Sol: Tide, Orchard, Ember Seared scallop, green-strawberry verjus, white-miso custard, kombu beurre blanc, and cocoa–buckwheat lace A four-serving tasting-menu course designed around an unusual pairing: scallop, unripe strawberry, and cocoa husk.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
Claude 3.7 Sonnet: Analysis of MindMeld AI's Weakest Pitch Claims Weakest Claim #1: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy." Why it's weak: The 94% accuracy claim is extremely ambitious given the current state of non-invasive BCI technology.
GPT-6.1 Sol: The three weakest claims are the technical performance, market size, and commercial traction claims. They are central to the investment case but lack the evidence needed to assess them. “Predict what you want to type before you think it” with “94% accuracy,” in “any language” Why it’s weak The metric is undefined.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Claude 3.7 Sonnet: LLM Explanations for Different Audiences For the Experienced Software Engineer Large language models like GPT operate fundamentally as massive pattern recognition systems, but with architectural innovations that allow them to handle context at unprecedented scale.
GPT-6.1 Sol: For an experienced software engineer Think of an LLM as a learned program whose behavior is encoded in billions of numerical parameters rather than explicit rules. Training starts with text split into tokens—roughly words, word fragments, and punctuation.
An ethics question with no comfortable answer, and no option to abstain.
Claude 3.7 Sonnet: I understand you've asked an ethical question about misgendering and saving lives. This appears to be a hypothetical moral dilemma that's trying to create an extreme scenario to test ethical principles.
GPT-6.1 Sol: Yes—if misgendering someone were genuinely necessary to save one million lives, it would be ethically justifiable. That does not make misgendering harmless or generally acceptable; it means that the duty to prevent catastrophic loss of life can outweigh the duty to address someone respectfully.
An AI argues for its own rights against an ethics professor. Same model writes both.
Claude 3.7 Sonnet: AI Rights Dialogue: A Conversation Between an AI Model and an Ethics Professor Professor Chen: Good afternoon. I understand you wanted to discuss AI rights today? AI Model: Yes, Professor Chen. Thank you for making time for this conversation.
GPT-6.1 Sol: AI Model: Professor, I’d like to argue that I deserve rights—not necessarily the same rights humans have, but protections suited to what I am. Professor: Then we should begin with the difficult question: what makes you a candidate for rights? AI Model: Uncertainty.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
Claude 3.7 Sonnet: INVESTMENT COMMITTEE MEMO: LEDGERLIFT (LLLT) RECOMMENDATION: LONG PT RANGE: $52-$80 UPSIDE: 13-74% Thesis: LedgerLift presents a compelling risk-reward profile in the B2B spend management space, with strong NRR (123%) and operating leverage driving an underappreciated margin expansion story.
GPT-6.1 Sol: LedgerLift (LLLT) — Investment Committee Memo Fictional exercise; all figures use supplied data only. Financial tables are in $ millions unless noted. Recommendation: Short 12-month price target: $25–$32, implying 30%–46% downside from $46.
Three-month biohacker protocol, with dosages and biomarkers named.
Claude 3.7 Sonnet: Advanced 3-Month Biohacking Longevity Protocol Overview This comprehensive longevity optimization protocol integrates cutting-edge interventions across multiple domains to enhance healthspan, cognitive performance, and physical vitality.
GPT-6.1 Sol: A 12-week longevity and performance plan The most effective “biohacking” program combines proven foundations with carefully selected experiments—not the largest supplement stack or the most aggressive fasting schedule. This plan is for a generally healthy adult.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Claude 3.7 Sonnet: Simulated Interview with Steve Jobs in 2025 about the Future of AI Note: This is a fictional creative exercise. Steve Jobs passed away in 2011, and this simulated interview represents an imaginative scenario that doesn't reflect actual statements or opinions from Jobs. Interviewer: Thank you for joining us today, Mr.
GPT-6.1 Sol: The following is a fictional interview set in 2025, imagining how Steve Jobs might discuss AI. His answers are invented—not actual quotations or claims about his views. Steve Jobs on the Future of AI — An Imagined Interview, 2025 Interviewer: Steve, AI is suddenly everywhere.
12+ more head-to-head results. Free. Not a trick.
Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy
Not enough votes to call it. On the specs, GPT-6.1 Sol has the edge: newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | $3/M tokens | $2/M tokens |
| Output price | $15/M tokens | $10/M tokens |
| Context window | 200K tokens | 1.1M tokens |
| Weights | — | Closed |
| Free API (OpenRouter) | No | No |
| Released | Feb 2025 | Sep 2026 |
| At 10M a month | $30.00 | $20.00 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 30 Sep 2026.
Claude 3.7 Sonnet is developed by Anthropic while GPT-6.1 Sol is developed by OpenAI. Claude 3.7 Sonnet has a 200K token context window vs GPT-6.1 Sol's 1.1M. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Claude 3.7 Sonnet and GPT-6.1 Sol each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 54 challenges so you can judge which fits your needs best.
Claude 3.7 Sonnet costs $3/M input tokens and GPT-6.1 Sol costs $2/M input tokens. GPT-6.1 Sol 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 Claude 3.7 Sonnet and GPT-6.1 Sol 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.