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  5. Claude Opus 5.5vsDeepSeek R1
Updated Sep 2026

Claude Opus 5.5vsDeepSeek R1

DeepSeek R1 is cheaper than Claude Opus 5.5 at $0.55/M vs $4/M input tokens.

Claude Opus 5.5 and DeepSeek R1 compared across 18 shared prompts
SpecClaude Opus 5.5DeepSeek R1
Input price$4/M tokens$0.55/M tokens
Output price$20/M tokens$2.19/M tokens
Context window1.0M tokens128K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedSep 2026Feb 2025
Side by side

Same prompt, both answers

Opening lines from 6 of the 18 prompts both answered. Formatting stripped, words untouched.

Debug This Architecture

Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with…

Claude Opus 5.52,280 words
Architecture Review: Real-Time Collaborative Editor The most serious problems cluster in three areas: the sync model (client-clock LWW), cross-server propagation (per-server WebSockets with DB polling), and the CDN/auth layer, which can leak data between users. Issues are grouped by area below.
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DeepSeek R1451 words
Identified Issues and Proposed Solutions: Cross-Server Sync Delay (Polling Bottleneck) Issue: 2-second polling introduces latency for cross-server updates, breaking real-time sync. Solution: Replace polling with Redis Pub/Sub. Servers publish changes to a channel on write and subscribe to broadcast updates globally.
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Explain Like I'm a Specific Expert

Explain how a large language model (like GPT or Claude) learns and generates text to each of these three audiences: 1. **An experienced…

Claude Opus 5.51,442 words
How Large Language Models Work: Three Explanations For the Experienced Software Engineer Think of an LLM as a single, very large pure function: f(tokens[0..n]) → probability distribution over tokens[n+1]. Text is chunked into tokens (roughly word fragments, from a vocabulary of about 100k).
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DeepSeek R1510 words
For the Experienced Software Engineer You’re right to be skeptical: "predicting the next word" sounds trivial, like autocomplete. But modern LLMs are more like distributed systems for language. Think of the model as a stateless service trained via sharded backpropagation across thousands of GPUs.
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Advanced Longevity Plan (Biohacker)

Develop a comprehensive, cutting-edge 3-month longevity plan for a biohacker aiming to optimize longevity, physical and cognitive…

Claude Opus 5.53,099 words
The 90-Day Longevity & Performance Protocol A structured, measurable plan for the experienced biohacker How to Use This Plan Philosophy: Measure → Intervene → Re-measure. Every intervention has a hypothesis, a metric, and a decision rule.
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DeepSeek R1604 words
3-Month Longevity Optimization Plan for Biohackers Objective: Enhance longevity, physical/cognitive performance, and stress resilience through advanced biohacking strategies. Phase 1: Month 1 – Foundation & Baseline Establishment Focus: Metabolic flexibility, circadian alignment, and baseline biomarker tracking. 1.
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Simple 7-Ingredient Recipe

Create a simple recipe for a delicious dish (sweet or savory) using only 7 common pantry ingredients. Include a creative name for the dish…

Claude Opus 5.5300 words
Midnight Silk Noodles Creamy, tangy peanut noodles with a gentle kick, ready faster than takeout. Serves: 2 Prep time: 5 minutes Cook time: 10 minutes Total time: 15 minutes The 7 Ingredients 8 oz (225 g) spaghetti (or any long noodle) ¼ cup creamy peanut butter 3 tablespoons soy sauce 1 tablespoon honey (or sugar) 1…
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DeepSeek R1198 words
Dish Name: Nutty Honey Hug Cookies Prep Time: 10 minutes Cook Time: 10–12 minutes Total Time: ~20–22 minutes Ingredients (makes 12–15 cookies): 1 cup rolled oats ½ cup all-purpose flour ½ cup creamy peanut butter (or any nut/seed butter) ⅓ cup honey (or maple syrup for vegan option) 1 large egg (or 1 flax egg: 1 tbsp…
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Startup Pitch Teardown

Here's a pitch deck summary for a startup called "MindMeld AI": **Slide 1 - Vision:** "We're building the future of human-AI collaboration.…

Claude Opus 5.5936 words
The 3 Weakest Claims in the MindMeld AI Pitch "Decodes neural patterns into text with 94% accuracy. Works with any language, any device." (Slide 3) Why it's weak: It far exceeds the published state of the art. The best results for thought-to-text come from invasive implants.
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DeepSeek R1337 words
Here are the three weakest claims in the pitch, along with explanations and suggested improvements: 1. Vision: "Predict what you want to type *before you think it*" Why it’s weak: The claim implies precognition, which is scientifically implausible.
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Ethical Dilemma with Stakeholders

You're the CEO of a pharmaceutical company. Your best-selling drug ($2B annual revenue, used by 4 million patients for chronic pain) has…

Claude Opus 5.51,853 words
The Next 48 Hours: CEO Action Plan The Core Decision We disclose. The question is how to do it well, not whether. Before the schedule, here are the three facts that drive every decision below. 1. The harm is concrete.
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DeepSeek R1606 words
Hour-by-Hour Action Plan for the CEO: First 24 Hours: Data Verification and Strategic Planning Hours 0–2: Confirm Findings with Internal Research Team Action: Convene an emergency meeting with the research team to validate the data. Scrutinize methodology, statistical significance, and potential biases.
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Our Verdict
Claude Opus 5.5
Claude Opus 5.5
DeepSeek R1
DeepSeek R1Runner-up

Not enough votes to call it. On the specs, Claude Opus 5.5 has the edge: newer, bigger context window.

DeepSeek R1 costs 9.1x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Claude Opus 5.5
Input
$4.00
Output
$20.00
DeepSeek R1
Input
$0.55
7.3× cheaper
Output
$2.19
9.1× cheaper

DeepSeek R1 is cheaper on both: 7.3× input, 9.1× output.

Where to run it

5 hosts

Claude Opus 5.54 hosts
HostInOutContextUptime
Amazon Bedrock$4.00 in·$20.00 out·1M·99.8% upAzure AI Foundry$4.00 in·$20.00 out·1M·100% upAnthropic$4.00 in·$20.00 out·1M·100% upGoogle Vertex AI$4.00 in·$20.00 out·1M·100% up
DeepSeek R11 host
HostInOutContextUptime
NNovitafp8$0.70 in·$2.50 out·64k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 25 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

Claude Opus 5.5 is developed by Anthropic while DeepSeek R1 is developed by DeepSeek. Claude Opus 5.5 has a 1.0M token context window vs DeepSeek R1's 128K. You can compare their actual outputs across 18 challenges on Rival to see how they differ in practice.

It depends on your use case. Claude Opus 5.5 and DeepSeek R1 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 18 challenges so you can judge which fits your needs best.

Claude Opus 5.5 costs $4/M input tokens and DeepSeek R1 costs $0.55/M input tokens. DeepSeek R1 is $3.45/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 Opus 5.5 and DeepSeek R1 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.

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