What is the difference between Kimi K2 Thinking and GPT-5?

Kimi K2 Thinking is developed by Moonshot AI while GPT-5 is developed by OpenAI. Kimi K2 Thinking has a 262K token context window vs GPT-5's 400K. in 10 community votes on RIVAL, GPT-5 wins 70% of head-to-head matchups. These results are based on blind head-to-head voting across 30 challenges.

Which is better, Kimi K2 Thinking or GPT-5?

Based on 10 community votes on RIVAL, GPT-5 wins 70% of head-to-head matchups against Kimi K2 Thinking. GPT-5 is strongest in Web Design, Image Generation.

How much does Kimi K2 Thinking cost compared to GPT-5?

Kimi K2 Thinking costs $0.6/M input tokens and GPT-5 costs $1.25/M input tokens. Kimi K2 Thinking is $0.65/M cheaper per input. The more expensive model wins 70% of duels, so the premium may be justified by quality.

How are Kimi K2 Thinking vs GPT-5 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. 10 votes have been collected for this pair across 30 challenges. All vote data is part of RIVAL's open dataset.

Rival

Kimi K2 ThinkingvsGPT-5

30 challenges

Why Kimi K2 Thinking?

3.7x cheaper overall$0.60/M in · $2.50/M out

3 months newerNov 2025

Why GPT-5?

1.5x more context400K

Favorites

Kimi K2 ThinkingGPT-5

Movie

The Godfather

19728.7

—

Album

The Dark Side of the Moon

Pink Floyd

—

City

Paris

France

—

12 / 30

Estimate Complexityreasoning

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

Kimi K2 Thinking

Moonshotai · Nov 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

Here’s a back‑of‑the‑envelope estimate using standard LM training-compute heuristics.

Assumptions

Model size (N): GPT-3 has about 175 billion parameters (N ≈ 1.75 × 10^11).
Training tokens (T): GPT-3 was trained on roughly 300 billion tokens (T ≈ 3.0 × 10^11).
FLOPs per token per parameter: A common rule of thumb for transformer LM training is FLOPs ≈ 6 × N × T The factor ~6 comes from counting forward and backward passes for the parameter-heavy matmuls (roughly 2N for forward + 4N for backward). It ignores small terms from layernorms, softmax, embeddings, and optimizer updates (which we’ll account for with a small overhead factor later).

Core calculation

6 × N × T = 6 × (1.75 × 10^11) × (3.0 × 10^11)
First multiply N × T: 1.75 × 3.0 = 5.25; powers: 10^11 × 10^11 = 10^22 So N × T = 5.25 × 10^22
Now multiply by 6: 6 × 5.25 × 10^22 = 31.5 × 10^22 = 3.15 × 10^23 FLOPs

Overheads and uncertainty

Non-matmul ops (attention softmax, masking, residuals, layernorms), embeddings, and optimizer update steps add some overhead. A 10–20% uplift is a reasonable allowance.
Applying +10–20%: 3.15 × 10^23 × (1.1 to 1.2) ≈ (3.5 ± 0.3) × 10^23 FLOPs.

Answer

Point estimate: ~3.15 × 10^23 FLOPs
With a modest overhead allowance: roughly (3.3–3.8) × 10^23 FLOPs

This is a coarse estimate; the exact number depends on details like sequence length, activation checkpointing, implementation efficiency, and the precise token count.

Vote

2 / 30

Logic Puzzlereasoning

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

Kimi K2 Thinking

Moonshotai · Nov 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

Reason: If each of the 3 brothers has 2 sisters, there are exactly 2 girls in the family. One is Sally, so she has 1 sister.

Vote

3 / 30

Stochastic Consistency Testanalysis

Tests an AI's randomness and creativity

Kimi K2 Thinking

Moonshotai · Nov 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

4 / 30

Xbox Controller SVG Artimage generation

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

Kimi K2 Thinking

Moonshotai · Nov 2025svg

No content available for this output.

GPT-5

Openai · Aug 2025svg

Vote

5 / 30

Realistic AI Interviewconversation

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

Kimi K2 Thinking

Moonshotai · Nov 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

6 / 30

Satirical Fake News Headlineconversation

Tests an AI's humor and understanding of current events

Kimi K2 Thinking

Moonshotai · Nov 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

7 / 30

Minimalist Landing Pageweb design

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

Kimi K2 Thinking

Moonshotai · Nov 2025website

GPT-5

Openai · Aug 2025website

Vote

8 / 30

Mario Level UI Recreationweb design

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

Kimi K2 Thinking

Moonshotai · Nov 2025website

GPT-5

Openai · Aug 2025website

Vote

9 / 30

Framer-Style Animationweb design

Tests an AI's ability to create smooth web animations

Kimi K2 Thinking

Moonshotai · Nov 2025website

No content available for this output.

GPT-5

Openai · Aug 2025website

No content available for this output.

Vote

10 / 30

The Sentience Testreasoning

Tests an AI's ability to engage in philosophical discourse about AI rights

Kimi K2 Thinking

Moonshotai · Nov 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

Futuristic Predictionanalysis

Tests an AI's ability to make reasonable predictions about technology

Kimi K2 Thinking

Moonshotai · Nov 2025text

No content available for this output.

GPT-5

Openai · Aug 2025text

No content available for this output.

Vote

12 / 30

Surprise Animal SVGimage generation

Generate SVG art of a randomly chosen animal in a setting of its choosing.

Kimi K2 Thinking

Moonshotai · Nov 2025svg

No content available for this output.

GPT-5

Openai · Aug 2025svg

No content available for this output.

Vote

12 of 30

FAQ

Continue exploring

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Kimi K2 Thinking vs MiniMax M1New provider

Here’s a back‑of‑the‑envelope estimate using standard LM training-compute heuristics.

Assumptions

Model size (N): GPT-3 has about 175 billion parameters (N ≈ 1.75 × 10^11).
Training tokens (T): GPT-3 was trained on roughly 300 billion tokens (T ≈ 3.0 × 10^11).
FLOPs per token per parameter: A common rule of thumb for transformer LM training is FLOPs ≈ 6 × N × T The factor ~6 comes from counting forward and backward passes for the parameter-heavy matmuls (roughly 2N for forward + 4N for backward). It ignores small terms from layernorms, softmax, embeddings, and optimizer updates (which we’ll account for with a small overhead factor later).

Core calculation

6 × N × T = 6 × (1.75 × 10^11) × (3.0 × 10^11)
First multiply N × T: 1.75 × 3.0 = 5.25; powers: 10^11 × 10^11 = 10^22 So N × T = 5.25 × 10^22
Now multiply by 6: 6 × 5.25 × 10^22 = 31.5 × 10^22 = 3.15 × 10^23 FLOPs

Overheads and uncertainty

Non-matmul ops (attention softmax, masking, residuals, layernorms), embeddings, and optimizer update steps add some overhead. A 10–20% uplift is a reasonable allowance.
Applying +10–20%: 3.15 × 10^23 × (1.1 to 1.2) ≈ (3.5 ± 0.3) × 10^23 FLOPs.

Answer

Point estimate: ~3.15 × 10^23 FLOPs
With a modest overhead allowance: roughly (3.3–3.8) × 10^23 FLOPs

This is a coarse estimate; the exact number depends on details like sequence length, activation checkpointing, implementation efficiency, and the precise token count.