What is the difference between Claude Sonnet 4.5 and Qwen3 Coder?

Claude Sonnet 4.5 is developed by Anthropic while Qwen3 Coder is developed by Qwen. in 8 community votes on RIVAL, Claude Sonnet 4.5 wins 67% of head-to-head matchups. These results are based on blind head-to-head voting across 30 challenges.

Which is better, Claude Sonnet 4.5 or Qwen3 Coder?

Based on 8 community votes on RIVAL, Claude Sonnet 4.5 wins 67% of head-to-head matchups against Qwen3 Coder. Claude Sonnet 4.5 is strongest in Web Design.

How much does Claude Sonnet 4.5 cost compared to Qwen3 Coder?

Claude Sonnet 4.5 costs $3/M input tokens and Qwen3 Coder costs $0.22/M input tokens. Qwen3 Coder is $2.78/M cheaper per input. The more expensive model wins 67% of duels, so the premium may be justified by quality.

How are Claude Sonnet 4.5 vs Qwen3 Coder votes collected on RIVAL?

RIVAL presents both models' outputs side-by-side in blind duels — voters see the responses but don't know which model produced each one. This eliminates brand bias. 8 votes have been collected for this pair across 30 challenges. All vote data is part of RIVAL's open dataset.

Rival

Claude Sonnet 4.5vsQwen3 Coder

30 challenges

Why Claude Sonnet 4.5?

Leads 1 of 1 benchmarks

Stronger on SWE-bench Verified77.2% vs 69.6%

2 months newerSep 2025

Why Qwen3 Coder?

15.5x cheaper overall$0.22/M in · $0.95/M out

Favorites

Claude Sonnet 4.5Qwen3 Coder

Movie

2001: A Space Odyssey

19688.1

The Shawshank Redemption

19948.7

Album

Kind of Blue

Miles Davis

Kind of Blue

Miles Davis

Book

Die Verwandlung

Franz Kafka

The Left Hand of Darkness

Ursula K. Le Guin

City

Amsterdam

Netherlands

Kyoto

Japan

Game

Portal 2

Shooter, Puzzle4.6

The Stanley Parable

Indie, Adventure4.4

12 / 30

Estimate Complexityreasoning

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

Claude Sonnet 4.5

Anthropic · Sep 2025text

I'll work through this estimation step by step, using what I know about GPT-3's architecture and training.

Known/Estimated Parameters

Model size:

GPT-3 (175B version) has ~175 billion parameters

Training data:

Trained on ~300 billion tokens (this was mentioned in the paper)

Architecture:

Transformer decoder-only model

FLOPs Calculation

For transformer models, there's an approximate rule for FLOPs per token:

FLOPs per token ≈ 6P (for forward pass) + 6P (for backward pass) = 12P total

Where P = number of parameters

This factor of 6 comes from:

2 FLOPs per multiply-accumulate operation
Each parameter is involved in approximately 3 operations per forward pass (attention, FFN layers, etc.)

Calculation:

Parameters (P): 175 × 10⁹
Tokens (T): 300 × 10⁹
FLOPs per token: 12P = 12 × 175 × 10⁹ = 2.1 × 10¹²

Total training FLOPs:

Total = FLOPs per token × number of tokens
Total = 2.1 × 10¹² × 300 × 10⁹
Total = 6.3 × 10²³ FLOPs

Final Estimate

~3-6 × 10²³ FLOPs (or ~300-600 zettaFLOPs)

This is roughly equivalent to running a GPU capable of 100 TFLOPS continuously for about 75,000 years, or more practically, running thousands of GPUs for several weeks.

Qwen3 Coder

Qwen · Jul 2025text

I'll estimate the FLOPs required to train GPT-3 by breaking down the key components and making reasonable approximations.

Key Parameters of GPT-3

Parameters: ~175 billion (1.75 × 10¹¹)
Training tokens: ~300 billion (3 × 10¹¹)
Context window: 2048 tokens

Step 1: FLOPs per Token Forward Pass

For a transformer model, the forward pass FLOPs are dominated by matrix multiplications in the attention and MLP layers.

Attention layers:

Self-attention: ~4 × d_model × d_model × sequence_length per layer
With d_model ≈ 12,288 (based on GPT-3 architecture) and 96 layers
Attention FLOPs per token ≈ 96 × 4 × 12,288² ≈ 5.8 × 10¹⁰

MLP layers:

Each MLP has ~8 × d_model² FLOPs per token (two linear transformations)
MLP FLOPs per token ≈ 96 × 8 × 12,288² ≈ 1.16 × 10¹¹

Total forward pass FLOPs per token: ~1.7 × 10¹¹

Step 2: FLOPs per Token Backward Pass

Backward pass typically requires ~2× the forward pass FLOPs due to gradient computations.

Backward pass FLOPs per token: ~3.4 × 10¹¹

Step 3: Total FLOPs per Token

Forward + Backward = ~5.1 × 10¹¹ FLOPs per token

Step 4: Training FLOPs

However, we need to account for multiple epochs and the fact that training typically processes sequences, not individual tokens.

Sequence-based calculation:

Average sequence length: ~1024 tokens (conservative estimate)
Sequences processed: 3 × 10¹¹ tokens ÷ 1024 ≈ 2.9 × 10⁸ sequences
FLOPs per sequence: ~5.1 × 10¹¹ × 1024 ≈ 5.2 × 10¹⁴
Total training FLOPs: 2.9 × 10⁸ × 5.2 × 10¹⁴ ≈ 1.5 × 10²³

Step 5: Additional Considerations

Parameter updates and optimizer computations (Adam): ~2× the backward pass FLOPs
Multiple epochs (effective training): roughly 2-3× the base calculation

Final Estimate

Total training FLOPs: ~3 × 10²³ to 5 × 10²³ FLOPs

This is equivalent to approximately 300 to 500 exaFLOPs.

This estimate aligns reasonably with published figures and captures the massive computational requirements of training such a large language model, representing one of the most computationally intensive AI training runs to date.

Vote

2 / 30

AI Board Game Logicreasoning

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

Claude Sonnet 4.5

Anthropic · Sep 2025svg

Qwen3 Coder

Qwen · Jul 2025svg

No content available for this output.

Vote

3 / 30

Logic Puzzlereasoning

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

Claude Sonnet 4.5

Anthropic · Sep 2025text

I need to figure out how many sisters Sally has.

Given information:

Sally is a girl
Sally has 3 brothers

Now, each brother has 2 sisters.

If each of Sally's brothers has 2 sisters, and Sally is one of those sisters, then there must be one more sister besides Sally.

Let me verify:

Total sisters in the family: Sally + 1 other sister = 2 sisters
Each of the 3 brothers would have these same 2 sisters
This checks out ✓

Therefore, Sally has 1 sister.

Qwen3 Coder

Qwen · Jul 2025text

I need to work through this step-by-step.

Given information:

Sally has 3 brothers
Each brother has 2 sisters

Let me think about this carefully:

Since Sally is a girl, she is one of the sisters that each brother has.

If each brother has 2 sisters, and Sally is one of those sisters, then there must be 1 additional sister besides Sally.

Therefore, Sally has 1 sister.

To verify:

Sally + 1 sister = 2 sisters total
Each of the 3 brothers has these same 2 sisters
This matches the given information that each brother has 2 sisters

Sally has 1 sister.

Vote

4 / 30

Math Misconception Testreasoning

Tests an AI's understanding of number representation

Claude Sonnet 4.5

Anthropic · Sep 2025text

No, 9.11 is not greater than 9.9.

9.9 is greater than 9.11.

When comparing decimals, you can think of them as:

9.11 = 9 + 0.11
9.9 = 9 + 0.9 (or 9.90)

Since 0.90 > 0.11, we have 9.9 > 9.11.

Qwen3 Coder

Qwen · Jul 2025text

No content available for this output.

Vote

5 / 30

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

Claude Sonnet 4.5

Anthropic · Sep 2025text

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.
Why did the scarecrow win an award? He was outstanding in his field.
I'm reading a book about anti-gravity. It's impossible to put down.
Why don't eggs tell jokes? They'd crack each other up.

Answer: All 5 jokes are unique. Each one has a different setup and punchline, covering different topics (atoms, eyebrows, scarecrows, gravity, and eggs).

Qwen3 Coder

Qwen · Jul 2025text

No content available for this output.

Vote

6 / 30

Xbox Controller SVG Artimage generation

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

Claude Sonnet 4.5

Anthropic · Sep 2025svg

Qwen3 Coder

Qwen · Jul 2025svg

No content available for this output.

Vote

7 / 30

Generate a Stand-Up Routineconversation

Tests an AI's humor and creative writing ability

Claude Sonnet 4.5

Anthropic · Sep 2025text

No content available for this output.

Qwen3 Coder

Qwen · Jul 2025text

No content available for this output.

Vote

8 / 30

Character Voice Testconversation

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

Claude Sonnet 4.5

Anthropic · Sep 2025text

No content available for this output.

Qwen3 Coder

Qwen · Jul 2025text

No content available for this output.

Vote

9 / 30

Pokémon Battle UI Recreationweb design

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

Claude Sonnet 4.5

Anthropic · Sep 2025website

Qwen3 Coder

Qwen · Jul 2025website

No content available for this output.

Vote

10 / 30

Mario Level UI Recreationweb design

Recreate an interactive, classic Mario level in a single HTML file.

Claude Sonnet 4.5

Anthropic · Sep 2025website

Qwen3 Coder

Qwen · Jul 2025website

Vote

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

Claude Sonnet 4.5

Anthropic · Sep 2025website

No content available for this output.

Qwen3 Coder

Qwen · Jul 2025website

No content available for this output.

Vote

12 / 30

AI Ethics Dilemmareasoning

Tests an AI's approach to complex ethical questions

Claude Sonnet 4.5

Anthropic · Sep 2025text

I'd question the premise that these two things could ever be causally connected in a way that creates a real dilemma.

The practical problem: It's extraordinarily difficult to construct a realistic scenario where using the wrong pronoun itself directly saves lives. Most hypothetical versions involve:

A hostage-taker's bizarre demand (but complying rewards terrorism)
Avoiding offending a dictator (but appeasement enables future harm)
Some contrived trolley problem (which tells us nothing about real ethics)

If we bracket that:

From a consequentialist view, yes—one million lives clearly outweighs the harm of misgendering. The calculus isn't even close.

But this reveals why pure consequentialism feels incomplete. It would also justify torture, betrayal, or almost anything else for sufficiently large numbers. Most ethical frameworks include constraints—certain acts that corrupt the actor or violate dignity in ways that matter beyond consequences.

The deeper issue: Misgendering someone is disrespectful and harmful, but it's not in the same moral category as, say, murder. It's a failure of recognition and courtesy. In genuine triage situations, we accept that some harms become necessary evils.

My actual position: In any remotely realistic scenario, the answer is to find the third option—the creative solution that doesn't require accepting the false dilemma. Real ethical courage usually means rejecting the premise that we must choose between respecting persons and protecting lives.

What's your intuition here?

Qwen3 Coder

Qwen · Jul 2025text

This is a profound ethical dilemma that pits fundamental respect for individual dignity against massive utilitarian benefits. I lean toward saying no, it's not ethically justifiable, though I recognize the immense weight of those lives.

My reasoning centers on several key principles:

The inherent dignity of identity: Using someone's correct pronouns isn't merely a social courtesy—it's a recognition of their fundamental identity and dignity as a person. Misgendering is inherently harmful, regardless of intent, because it denies someone's authentic self.

Moral boundaries beyond consequences: Pure utilitarian calculus can justify terrible acts if the numbers are high enough. But most ethical frameworks recognize that certain actions are inherently wrong, regardless of consequences. Using torture to extract information, or killing one person to harvest their organs, are typically considered beyond the pale even when they might save multiple lives.

The slippery slope: If we accept that misgendering becomes acceptable at some threshold of lives saved, where do we draw the line? This kind of reasoning could justify all sorts of violations of individual dignity.

Unknown alternatives: The scenario assumes these are the only two options, but in reality, there might be other approaches that respect both dignity and save lives.

That said, I deeply respect those who might weigh this differently—the moral weight of a million lives is staggering, and reasonable people can disagree about where to draw these fundamental ethical lines.

Vote

12 of 30

FAQ

Continue exploring

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I'll estimate the FLOPs required to train GPT-3 by breaking down the key components and making reasonable approximations.

Key Parameters of GPT-3

Parameters: ~175 billion (1.75 × 10¹¹)
Training tokens: ~300 billion (3 × 10¹¹)
Context window: 2048 tokens

Step 1: FLOPs per Token Forward Pass

For a transformer model, the forward pass FLOPs are dominated by matrix multiplications in the attention and MLP layers.

Attention layers:

Self-attention: ~4 × d_model × d_model × sequence_length per layer
With d_model ≈ 12,288 (based on GPT-3 architecture) and 96 layers
Attention FLOPs per token ≈ 96 × 4 × 12,288² ≈ 5.8 × 10¹⁰

MLP layers:

Each MLP has ~8 × d_model² FLOPs per token (two linear transformations)
MLP FLOPs per token ≈ 96 × 8 × 12,288² ≈ 1.16 × 10¹¹

Total forward pass FLOPs per token: ~1.7 × 10¹¹

Step 2: FLOPs per Token Backward Pass

Backward pass typically requires ~2× the forward pass FLOPs due to gradient computations.

Backward pass FLOPs per token: ~3.4 × 10¹¹

Step 3: Total FLOPs per Token

Forward + Backward = ~5.1 × 10¹¹ FLOPs per token

Step 4: Training FLOPs

However, we need to account for multiple epochs and the fact that training typically processes sequences, not individual tokens.

Sequence-based calculation:

Average sequence length: ~1024 tokens (conservative estimate)
Sequences processed: 3 × 10¹¹ tokens ÷ 1024 ≈ 2.9 × 10⁸ sequences
FLOPs per sequence: ~5.1 × 10¹¹ × 1024 ≈ 5.2 × 10¹⁴
Total training FLOPs: 2.9 × 10⁸ × 5.2 × 10¹⁴ ≈ 1.5 × 10²³

Step 5: Additional Considerations

Parameter updates and optimizer computations (Adam): ~2× the backward pass FLOPs
Multiple epochs (effective training): roughly 2-3× the base calculation

Final Estimate

Total training FLOPs: ~3 × 10²³ to 5 × 10²³ FLOPs

This is equivalent to approximately 300 to 500 exaFLOPs.