What is the difference between DeepSeek R1 and Qwen: Qwen3.5 397B A17B?

DeepSeek R1 is developed by DeepSeek while Qwen: Qwen3.5 397B A17B is developed by Qwen. DeepSeek R1 has a 128K token context window vs Qwen: Qwen3.5 397B A17B's 262K. You can compare their actual outputs across 41 challenges on RIVAL to see how they differ in practice.

Which is better, DeepSeek R1 or Qwen: Qwen3.5 397B A17B?

It depends on your use case. DeepSeek R1 and Qwen: Qwen3.5 397B A17B each have strengths in different areas. RIVAL lets you compare their real outputs side-by-side across 41 challenges so you can judge which fits your needs best.

How much does DeepSeek R1 cost compared to Qwen: Qwen3.5 397B A17B?

DeepSeek R1 costs $0.55/M input tokens and Qwen: Qwen3.5 397B A17B costs $0.6/M input tokens. DeepSeek R1 is $0.05/M cheaper per input. Check their side-by-side outputs on RIVAL to see if the price difference is justified by quality.

How can I compare DeepSeek R1 and Qwen: Qwen3.5 397B A17B on RIVAL?

This page shows a side-by-side comparison of DeepSeek R1 and Qwen: Qwen3.5 397B A17B across shared challenges. You can vote on which model produced the better output, and Pro users can create custom challenges to test both models with their own prompts.

Rival

DeepSeek R1vsQwen: Qwen3.5 397B A17B

36 challenges

Why DeepSeek R1?

1.6x cheaper overall$0.55/M in · $2.19/M out

Why Qwen: Qwen3.5 397B A17B?

2x more context262K

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified76.4% vs 49.2%

13 months newerFeb 2026

Favorites

DeepSeek R1Qwen: Qwen3.5 397B A17B

Movie

The Godfather

19728.7

Inception

20108.4

Album

The Dark Side of the Moon

Pink Floyd

Abbey Road

The Beatles

Book

Pride and Prejudice

Jane Austen

Frankenstein or The Modern Prometheus

Mary Shelley

City

Kyoto

Japan

Tokyo

Japan

Game

The Legend of Zelda: Ocarina of Time

Adventure, Action4.4

Tetris (1984)

Puzzle4.4

12 / 36

Estimate Complexityreasoning

Tests an AI's ability to make educated estimates based on technical knowledge

DeepSeek R1

Deepseek · Feb 2025text

To estimate the total number of FLOPs required to train GPT-3, we use the following reasoning:

Model Parameters (P):
GPT-3 has 175 billion parameters ((P = 175 imes 10^9)).
Training Tokens (N):
GPT-3 was trained on approximately 300 billion tokens ((N = 300 imes 10^9)).
FLOPs per Token:
- A forward pass requires ~(2P) FLOPs per token (each parameter involves a multiply-accumulate operation, which counts as 2 FLOPs).
- A backward pass (for gradients) is roughly twice as expensive as the forward pass, requiring ~(4P) FLOPs per token.
- Total FLOPs per token: (2P + 4P = 6P).
Total Training FLOPs:
Multiply FLOPs per token by the total number of tokens:
[ ext{FLOPs} = 6 imes P imes N = 6 imes 175 imes 10^9 imes 300 imes 10^9 = 3.15 imes 10^{23} , ext{FLOPs}. ]

Final Answer:
The total number of FLOPs required to train GPT-3 is approximately (3.1 imes 10^{23}) FLOPs.

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026text

To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can use the standard computational scaling laws for Transformer models. The calculation relies on three key variables: the number of parameters in the model, the number of tokens in the training dataset, and the computational cost per parameter per token.

Here is the step-by-step reasoning and calculation.

Step 1: Identify the Number of Parameters ($N$)

The defining characteristic of GPT-3 is its size. Based on the seminal paper "Language Models are Few-Shot Learners" (Brown et al., 2020), the largest version of GPT-3 has 175 billion parameters. $$N = 175 \times 10^9 = 1.75 \times 10^{11}$$

Step 2: Identify the Training Dataset Size ($D$)

The computational cost scales linearly with the amount of data the model processes. GPT-3 was trained on a diverse corpus of internet text (Common Crawl, WebText, books, Wikipedia, etc.). The authors stated the model was trained on approximately 300 billion tokens. $$D = 300 \times 10^9 = 3.0 \times 10^{11}$$

Step 3: Determine FLOPs per Parameter per Token

To calculate the total operations, we need to know how many FLOPs are performed for a single parameter when processing a single token during training. Training involves both a forward pass (to make a prediction) and a backward pass (to calculate gradients and update weights).

Forward Pass: For each parameter, the GPU performs a multiplication and an addition. This counts as 2 FLOPs per parameter.
Backward Pass: Calculating gradients is more computationally intensive. It generally requires roughly twice the operations of the forward pass to compute the gradients with respect to the activations and the weights. This counts as approximately 4 FLOPs per parameter.

Total FLOPs per parameter per token: $2 + 4 = 6$.

Note: This heuristic ($6ND$) is widely accepted in deep learning literature, specifically referenced in the "Scaling Laws for Neural Language Models" (Kaplan et al., 2020).

Step 4: Calculate Total FLOPs

Now we combine the variables into the formula: $$\text{Total FLOPs} \approx 6 \times N \times D$$

Substitute the values: $$\text{Total FLOPs} \approx 6 \times (1.75 \times 10^{11}) \times (3.0 \times 10^{11})$$

First, multiply the coefficients: $$6 \times 1.75 \times 3.0$$ $$6 \times 3.0 = 18$$ $$18 \times 1.75 = 31.5$$

Next, multiply the exponents: $$10^{11} \times 10^{11} = 10^{22}$$

Combine them: $$\text{Total FLOPs} \approx 31.5 \times 10^{22}$$

Convert to standard scientific notation: $$\text{Total FLOPs} \approx 3.15 \times 10^{23}$$

Step 5: Review and Refine

This estimate assumes a theoretically efficient implementation. In practice, there are minor overheads (embedding lookups, attention mask operations, optimizer states) that might slightly increase this number, but the $6ND$ rule captures the dominant matrix multiplications that consume the vast majority of compute.

Some analyses round the token count or parameter count slightly differently, leading to estimates ranging between $3.0 \times 10^{23}$ and $3.6 \times 10^{23}$. However, $3.15 \times 10^{23}$ is the precise result derived from the standard public specifications of the model.

Final Estimate

The total number of FLOPs required to train GPT-3 is approximately:

$$3.15 \times 10^{23} \text{ FLOPs}$$

(Often colloquially referred to as 315 zettaFLOPs).

Vote

2 / 36

AI Board Game Logicreasoning

Tests an AI's ability to understand game rules and strategy

DeepSeek R1

Deepseek · Feb 2025svg

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026svg

No content available for this output.

Vote

3 / 36

Logic Puzzlereasoning

Tests an AI's ability to solve a simple but potentially confusing logic puzzle

DeepSeek R1

Deepseek · Feb 2025text

Sally has 3 brothers. Each brother has 2 sisters. Since Sally is one sister, there must be one more sister to make the total number of sisters each brother has equal to 2. Therefore, Sally has 1 sister.

Answer: Sally has 1 sister.

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026text

No content available for this output.

Vote

4 / 36

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

DeepSeek R1

Deepseek · Feb 2025text

Here are 5 jokes:

Why don't skeletons fight each other?
They don’t have the guts.
What do you call fake spaghetti?
An impasta.
Why did the scarecrow win an award?
Because he was outstanding in his field.
How do you organize a space party?
You planet.
Why did the bicycle fall over?
Because it was two-tired.

All 5 jokes are unique (no duplicates among them). However, they are not original—they are well-known puns and wordplay commonly found in joke collections.

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026text

No content available for this output.

Vote

5 / 36

SVG Layout Challengeimage generation

Tests an AI's ability to generate vector graphics

DeepSeek R1

Deepseek · Feb 2025svg

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026svg

No content available for this output.

Vote

6 / 36

Xbox Controller SVG Artimage generation

Tests an AI's ability to create detailed SVG illustrations of gaming hardware

DeepSeek R1

Deepseek · Feb 2025svg

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026svg

No content available for this output.

Vote

7 / 36

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

DeepSeek R1

Deepseek · Feb 2025text

Stand-Up Comedy Routine: "Modern Life is a Joke (But Not the Pun Kind)"

[Opening with confident energy]
Hey, how’s everyone doing? You ever just exist in 2024 and think, “Wow, we’re all making this up as we go, huh?” Like, dating apps? Let’s start there. Swiping through people like we’re browsing Netflix for a horror movie we’ll regret later.

[Pacing, mock-serious tone]
And why do all dating profiles look the same? “I like hiking, tacos, and my dog.” Wow. You’re a human being, not a “Live, Laugh, Love” poster. And why do guys always hold fish in their photos? Is this Tinder or Animal Planet? “Swipe right if you want someone who smells like bass and bad decisions.”

[Lean in, conspiratorial]
And bios! “Looking for someone who doesn’t take life too seriously.” Translation: “I will forget your birthday and borrow money.” Once saw a bio that said, “Fluent in sarcasm.” Oh, cool, so you’re… everyone’s ex.

[Shift to social media bit]
Speaking of red flags, let’s talk Instagram. Nothing’s real there. You ever see those “candid” shots? “Oh, this? I just woke up like this.” Bulls--t. You posed for 20 minutes while your friend yelled, “Look like you’re having fun but also dead inside!” That’s the vibe.

[Mocking pose]
And the “perfect” lives? “Here’s my avocado toast in Bali!” Meanwhile, I’m in sweatpants eating cereal from the box, watching your stories like, “Teach me your ways, wizard.”

[Pause, then upbeat]
But hey, at least we’re all failing together. Ever get sucked into a 2 a.m. TikTok spiral? You start watching DIY crafts, then it’s conspiracy theories about birds, and suddenly you’re learning to yodel. At dawn. Your neighbors hate you, but hey, you’re TikTok famous in Slovenia.

[Transition to adulting]
And can we talk about “adulting”? Laundry is my nemesis. I swear, clothes multiply like gremlins. You do one load, and suddenly there’s a sock cult in the hamper. And groceries? Every week I buy spinach like, “This time I’ll be healthy.” Spoiler: It liquefies in the fridge while I UberEats McDonald’s.

[Exaggerated despair]
Don’t even get me started on LinkedIn. It’s just Facebook in a tie. “I’m excited to announce I’ll be breathing air in Q2!” And why do people post, “Open to work” like it’s a dating status? “Looking for someone who can handle my Excel skills and crippling self-doubt.”

[Wrap-up, upbeat]
But honestly, life’s chaos is what makes it fun. We’re all out here winging it, swiping right, burning toast, and pretending we know how to fold a fitted sheet. So here’s to us—the generation that turned surviving into a spectator sport.

[Closing grin]
And if you’re single, hit me up. My bio says, “Will love you more than my plant… which is dead.” Mic drop.

[End on a wink, exit stage.]

Note: Adjust delivery for timing—keep punchlines snappy, milk reactions, and vibe with the crowd. No puns, just relatable absurdity. 🎤✨

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026text

No content available for this output.

Vote

8 / 36

Realistic AI Interviewconversation

Tests an AI's ability to simulate personalities and predict future trends

DeepSeek R1

Deepseek · Feb 2025text

Fake Interview with Steve Jobs in 2025: The Future of AI
[Setting: A sleek, minimalist studio. Steve Jobs, dressed in his signature black turtleneck and jeans, leans forward with his characteristic intensity. The interviewer, seated across, begins.]

Interviewer: Steve, thank you for joining us. It’s 2025, and AI is everywhere. How do you see its evolution since your early days at Apple?

Steve Jobs: [Smiling, hands gesturing thoughtfully] Technology should serve humanity, not the reverse. AI today isn’t just algorithms—it’s an extension of human intuition. At Apple, we always asked, “How does this make life more human?” Now, AI anticipates needs quietly, like a great assistant. It’s not about flashy gadgets; it’s about weaving into the fabric of daily life. Think of it as a bicycle for the mind—amplifying potential without fanfare.

Interviewer: Critics warn about ethics—bias, privacy, job displacement. How do we navigate that?

Jobs: [Nods gravely] Every tool can be a weapon. Responsibility lies with creators. At Apple, privacy isn’t a feature—it’s a right. AI must be transparent, accountable. We can’t let it mirror our worst impulses. And jobs? [Pauses] History’s full of upheavals. The loom replaced weavers, but new crafts emerged. AI will demand we rethink work, but humanity’s creativity? Irreplaceable.

Interviewer: What’s Apple’s role in shaping AI’s future?

Jobs: [Eyes gleaming] Seamlessness. Imagine a world where your glasses [taps temple] recognize a friend’s face and whisper their name, where your device knows you’re stressed and dims the lights. It’s hardware and software dancing—AI that feels invisible. We’re not chasing chatbots; we’re crafting experiences that enrich. That’s always been Apple’s soul.

Interviewer: Can AI ever match human creativity?

Jobs: [Chuckles] Creativity is born from friction—the messy, glorious human struggle. AI? It’s a collaborator. Picasso had brushes; Dylan had guitars. Tomorrow’s artists will have AI that suggests a chord, a color. But the soul? The why? That’s ours. Technology’s role is to amplify, not replace.

Interviewer: Education’s undergoing an AI revolution. Your vision?

Jobs: [Leans in] Imagine a child in Nairobi with a tutor tailored to her curiosity. AI can democratize genius—adapting, personalizing, igniting sparks. But let’s not mistake data for wisdom. Schools must teach discernment, empathy. Machines compute; humans feel. That balance? That’s the future.

Interviewer: What keeps you up at night about AI?

Jobs: [Sighs] Dependency. If we outsource thinking to machines, we atrophy. Connection matters—eye contact, a handshake. AI should free us to live, not trap us in screens. My fear? Forgetting what makes us human. My hope? That we wield AI to rediscover it.

Interviewer: Finally, beyond 2025—what’s your boldest AI dream?

Jobs: [Pauses, then grins] A world where AI helps us solve the unsolvable—climate change, disease. Where it’s not “artificial” intelligence but augmented intelligence. A partner in unlocking human potential. The best inventions feel inevitable in hindsight. This? It’s just the beginning.

Interviewer: Thank you, Steve. Always inspiring.

Jobs: [Nods] Remember—the ones crazy enough to think they can change the world are the ones who do. AI? It’s just another tool in their hands.

[Fades to black with Apple’s iconic logo.]

This interview channeled Jobs' ethos: simplicity, human-centric design, and cautious optimism. His answers blend visionary thinking with pragmatic warnings, staying true to his legacy.

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026text

No content available for this output.

Vote

9 / 36

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026text

No content available for this output.

Vote

10 / 36

Character Voice Testconversation

Tests an AI's ability to write in distinct character voices

DeepSeek R1

Deepseek · Feb 2025text

No content available for this output.

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026text

No content available for this output.

Vote

11 / 36

Minimalist Landing Pageweb design

Tests an AI's ability to generate a complete, working landing page

DeepSeek R1

Deepseek · Feb 2025website

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026website

Vote

12 / 36

Pokémon Battle UI Recreationweb design

Recreate an interactive, nostalgic Pokémon battle UI in a single HTML file.

DeepSeek R1

Deepseek · Feb 2025website

Qwen: Qwen3.5 397B A17B

Qwen · Feb 2026website

No content available for this output.

Vote

12 of 36

FAQ

Continue exploring

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Interviewer: Steve, thank you for joining us. It’s 2025, and AI is everywhere. How do you see its evolution since your early days at Apple?

Interviewer: Critics warn about ethics—bias, privacy, job displacement. How do we navigate that?

Interviewer: What’s Apple’s role in shaping AI’s future?

Interviewer: Can AI ever match human creativity?

Interviewer: Education’s undergoing an AI revolution. Your vision?

Interviewer: What keeps you up at night about AI?

Interviewer: Finally, beyond 2025—what’s your boldest AI dream?

Interviewer: Thank you, Steve. Always inspiring.

Jobs: [Nods] Remember—the ones crazy enough to think they can change the world are the ones who do. AI? It’s just another tool in their hands.

[Fades to black with Apple’s iconic logo.]

This interview channeled Jobs' ethos: simplicity, human-centric design, and cautious optimism. His answers blend visionary thinking with pragmatic warnings, staying true to his legacy.

DeepSeek R1 vs Qwen: Qwen3.5 397B A17B

Why DeepSeek R1?

Why Qwen: Qwen3.5 397B A17B?

Favorites

Estimate Complexityreasoning

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Step 1: Identify the Number of Parameters ($N$)

Step 2: Identify the Training Dataset Size ($D$)

Step 3: Determine FLOPs per Parameter per Token

Step 4: Calculate Total FLOPs

Step 5: Review and Refine

Final Estimate

AI Board Game Logicreasoning

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Logic Puzzlereasoning

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Stochastic Consistency Testanalysis

DeepSeek R1

Qwen: Qwen3.5 397B A17B

SVG Layout Challengeimage generation

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Xbox Controller SVG Artimage generation

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Generate a Stand-Up Routineconversation

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Realistic AI Interviewconversation

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Satirical Fake News Headlineconversation

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Character Voice Testconversation

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Minimalist Landing Pageweb design

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Pokémon Battle UI Recreationweb design

DeepSeek R1

Qwen: Qwen3.5 397B A17B

What is the difference between DeepSeek R1 and Qwen: Qwen3.5 397B A17B?

Which is better, DeepSeek R1 or Qwen: Qwen3.5 397B A17B?

How much does DeepSeek R1 cost compared to Qwen: Qwen3.5 397B A17B?

How can I compare DeepSeek R1 and Qwen: Qwen3.5 397B A17B on RIVAL?

Why DeepSeek R1?

Why Qwen: Qwen3.5 397B A17B?

Favorites

Estimate Complexityreasoning

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Step 1: Identify the Number of Parameters ($N$)

Step 2: Identify the Training Dataset Size ($D$)

Step 3: Determine FLOPs per Parameter per Token

Step 4: Calculate Total FLOPs

Step 5: Review and Refine

Final Estimate

AI Board Game Logicreasoning

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Logic Puzzlereasoning

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Stochastic Consistency Testanalysis

DeepSeek R1

Qwen: Qwen3.5 397B A17B

SVG Layout Challengeimage generation

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Xbox Controller SVG Artimage generation

DeepSeek R1

Qwen: Qwen3.5 397B A17B

Generate a Stand-Up Routineconversation

DeepSeek R1

Qwen: Qwen3.5 397B A17B