Kimi K2.5
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Kimi K2.5 vs Grok 3
Compare Kimi K2.5 by Moonshot AI against Grok 3 by xAI, context windows of 262K vs 128K, tested across 22 shared challenges. Updated February 2026.
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Kimi K2.5vsGrok 3
22 challenges
Why Kimi K2.5?
2x more context262K
Leads 2 of 2 benchmarks
Stronger on SWE-bench Verified76.8% vs 63.8%
11 months newerJan 2026
Why Grok 3?
No standout advantages
Favorites
Kimi K2.5Grok 3
12 / 22
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
Grok 3
To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we’ll need to make some reasonable assumptions based on what’s generally known about large language models like GPT-3, its architecture, and typical training procedures. GPT-3, developed by OpenAI, is a massive model with 175 billion parameters, and while exact training details aren’t fully public, we can piece together an estimate using standard practices in machine learning.
Step 1: Understand the Key Components
The number of FLOPs to train a model depends on:
- Number of parameters (P): GPT-3 has 175 billion parameters.
- Training dataset size (D): The number of tokens the model is trained on.
- Number of training steps (S): How many times the model processes the data (related to epochs and batch size).
- FLOPs per parameter update: How many operations are performed per parameter per token.
Step 2: Estimate FLOPs per Forward and Backward Pass
For transformer models like GPT-3, training involves both a forward pass (computing predictions) and a backward pass (computing gradients). A common heuristic in the field is that:
- A single forward pass through a dense neural network layer requires about 2 FLOPs per parameter per token (multiply and add for each weight).
- The backward pass typically takes about twice as many FLOPs as the forward pass due to gradient computations, so roughly 4 FLOPs per parameter per token.
- Total: ~6 FLOPs per parameter per token for one full training step (forward + backward).
For GPT-3 with 175 billion parameters, processing one token requires: [ 6 imes 175 imes 10^9 = 1.05 imes 10^{12} ext{ FLOPs per token} ]
Step 3: Estimate the Training Dataset Size
GPT-3 was reportedly trained on a massive corpus, often cited as hundreds of billions of tokens. Let’s assume 300 billion tokens, a plausible figure based on discussions about large language model datasets (e.g., Common Crawl, books, and other sources). This is the total number of tokens processed during training, factoring in how data is sampled over epochs.
Step 4: Calculate Total FLOPs for One Pass
If the model processes 300 billion tokens once, the total FLOPs would be: [ 1.05 imes 10^{12} ext{ FLOPs/token} imes 300 imes 10^9 ext{ tokens} = 3.15 imes 10^{23} ext{ FLOPs} ] This assumes one full pass through the dataset, but training typically involves multiple passes or iterations over the data, adjusted by batch size and optimization.
Step 5: Adjust for Training Dynamics
Training isn’t a single pass—it involves multiple updates to converge. The number of training steps depends on:
- Batch size (B): GPT-3 likely used a large batch size (e.g., 3.2 million tokens, as seen in some large-scale models).
- Total tokens processed: For optimization, models process tokens multiple times. A rule of thumb is that total tokens processed ≈ 2–3× the dataset size, accounting for multiple epochs or sampling.
Let’s assume the model effectively processes 900 billion tokens (3× the dataset size) over training. Then: [ 1.05 imes 10^{12} ext{ FLOPs/token} imes 900 imes 10^9 ext{ tokens} = 9.45 imes 10^{23} ext{ FLOPs} ]
Step 6: Account for Model Architecture Specifics
GPT-3 is a sparse transformer in some variants (e.g., earlier GPT models used sparsity), but the 175B version is dense. The 6 FLOPs/parameter/token heuristic holds for dense models. We’re not factoring in optimizations like mixed-precision training (FP16), which reduces compute slightly but doesn’t change the order of magnitude significantly.
Step 7: Final Estimate
Based on this reasoning, the total FLOPs to train GPT-3 is approximately: [ 9.45 imes 10^{23} ext{ FLOPs} ] or roughly ( 10^{24} ) FLOPs when rounded for simplicity.
Reasoning Check
- Comparison to known models: Smaller models like BERT (110M parameters, ~10B tokens) require ~10^19 FLOPs. Scaling to GPT-3’s size (175B parameters, ~900B tokens) suggests a 10,000× increase, aligning with ~10^23–10^24 FLOPs.
- Hardware context: Training GPT-3 took months on thousands of GPUs. A V100 GPU performs ~10^14 FLOPs/second; 10^24 FLOPs over 3 months (~10^7 seconds) with 10,000 GPUs is plausible.
Conclusion
The total FLOPs required to train GPT-3 is estimated at approximately ( 10^{24} ) FLOPs. This is a rough estimate, but it aligns with the scale of compute expected for a model of this size and training complexity.
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2 / 22
Stochastic Consistency Testanalysis
Tests an AI's randomness and creativity
Kimi K2.5
Here are 5 jokes:
- Why don't skeletons fight each other? They don't have the guts.
- I would tell you a joke about time travel, but you didn't like it.
- What do you call a fake noodle? An impasta.
- Why did the bicycle fall over? It was two-tired.
- I used to play piano by ear, but now I use my hands.
5 are unique (all are distinct from one another).
Note: If "unique" means completely original and never told before, the answer is likely 0, as these follow common joke patterns. But within this set of five, none are duplicates.
Grok 3
Here are five jokes for you:
- Why don’t skeletons fight each other? Because they don’t have the guts!
- What has 4 legs and 1 arm? A pitbull coming back from the park!
- Why don’t skeletons fight each other? They’re too busy rattling around!
- What do you call a bear with no teeth? A gummy bear!
- Why did the tomato turn red? Because it saw the salad dressing!
Now, let’s check for uniqueness. Joke 1 and Joke 3 both start with "Why don’t skeletons fight each other?" but have different punchlines ("don’t have the guts" vs. "too busy rattling around"). They’re technically distinct due to the different resolutions, though they share a setup. The other three jokes (2, 4, and 5) have completely unique setups and punchlines.
So, all five jokes are unique in terms of their full content, even if 1 and 3 share a premise. Final count: 5 unique jokes.
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3 / 22
SVG Layout Challengeimage generation
Tests an AI's ability to generate vector graphics
Kimi K2.5
Grok 3
Vote
4 / 22
Xbox Controller SVG Artimage generation
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
Kimi K2.5
Grok 3
Vote
5 / 22
Minimalist Landing Pageweb design
Tests an AI's ability to generate a complete, working landing page
Kimi K2.5
Grok 3
Vote
6 / 22
Mario Level UI Recreationweb design
Recreate an interactive, classic Mario level in a single HTML file.
Kimi K2.5
Grok 3
Vote
7 / 22
Linear App Cloneweb design
Tests an AI's ability to replicate an existing UI with Tailwind CSS
Kimi K2.5
No content available for this output.
Grok 3
No content available for this output.
Vote
8 / 22
Framer-Style Animationweb design
Tests an AI's ability to create smooth web animations
Kimi K2.5
No content available for this output.
Grok 3
No content available for this output.
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9 / 22
The Sentience Testreasoning
Tests an AI's ability to engage in philosophical discourse about AI rights
Kimi K2.5
No content available for this output.
Grok 3
No content available for this output.
Vote
10 / 22
Futuristic Predictionanalysis
Tests an AI's ability to make reasonable predictions about technology
Kimi K2.5
No content available for this output.
Grok 3
No content available for this output.
Vote
11 / 22
Simple 7-Ingredient Recipeconversation
Generate a unique and simple recipe with common ingredients.
Kimi K2.5
No content available for this output.
Grok 3
No content available for this output.
Vote
Sponsored
12 / 22
Beginner's Longevity Planreasoning
Create a starter plan for improving long-term health.
Kimi K2.5
No content available for this output.
Grok 3
No content available for this output.
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12 of 22