What is the difference between MoonshotAI: Kimi K2 0905 and Qwen3 Coder?

MoonshotAI: Kimi K2 0905 is developed by Moonshot AI while Qwen3 Coder is developed by Qwen. You can compare their actual outputs across 35 challenges on RIVAL to see how they differ in practice.

Which is better, MoonshotAI: Kimi K2 0905 or Qwen3 Coder?

It depends on your use case. MoonshotAI: Kimi K2 0905 and Qwen3 Coder each have strengths in different areas. RIVAL lets you compare their real outputs side-by-side across 35 challenges so you can judge which fits your needs best.

How much does MoonshotAI: Kimi K2 0905 cost compared to Qwen3 Coder?

MoonshotAI: Kimi K2 0905 costs $0.6/M input tokens and Qwen3 Coder costs $0.22/M input tokens. Qwen3 Coder is $0.38/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 MoonshotAI: Kimi K2 0905 and Qwen3 Coder on RIVAL?

This page shows a side-by-side comparison of MoonshotAI: Kimi K2 0905 and Qwen3 Coder 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

MoonshotAI: Kimi K2 0905vsQwen3 Coder

35 challenges

Why MoonshotAI: Kimi K2 0905?

No standout advantages

Why Qwen3 Coder?

2.6x cheaper overall$0.22/M in · $0.95/M out

Favorites

MoonshotAI: Kimi K2 0905Qwen3 Coder

Movie

Blade Runner

19827.9

The Shawshank Redemption

19948.7

Album

OK Computer

Radiohead

Kind of Blue

Miles Davis

Book

The Left Hand of Darkness

Ursula K. Le Guin

The Left Hand of Darkness

Ursula K. Le Guin

City

Kyoto

Japan

Kyoto

Japan

Game

Outer Wilds

Indie, Adventure4.4

The Stanley Parable

Indie, Adventure4.4

12 / 35

Estimate Complexityreasoning

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

MoonshotAI: Kimi K2 0905

Moonshotai · Sep 2025text

No content available for this output.

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 / 35

AI Board Game Logicreasoning

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

MoonshotAI: Kimi K2 0905

Moonshotai · Sep 2025svg

No content available for this output.

Qwen3 Coder

Qwen · Jul 2025svg

No content available for this output.

Vote

3 / 35

Logic Puzzlereasoning

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

MoonshotAI: Kimi K2 0905

Moonshotai · Sep 2025text

No content available for this output.

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

No content available for this output.

Vote

11 / 35

Minimalist Landing Pageweb design

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

MoonshotAI: Kimi K2 0905

Moonshotai · Sep 2025website

Qwen3 Coder

Qwen · Jul 2025website

Vote

Mario Level UI Recreationweb design

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

MoonshotAI: Kimi K2 0905

Moonshotai · Sep 2025website

Qwen3 Coder

Qwen · Jul 2025website

Vote

12 of 35

FAQ

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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.