GPT-5
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GPT-5 vs Qwen: Qwen3.5 397B A17B
Compare GPT-5 by OpenAI against Qwen: Qwen3.5 397B A17B by Qwen, context windows of 400K vs 262K, tested across 46 shared challenges. Updated February 2026.
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GPT-5vsQwen: Qwen3.5 397B A17B
46 challenges
Why GPT-5?
1.5x more context400K
Why Qwen: Qwen3.5 397B A17B?
2.7x cheaper overall$0.60/M in · $3.60/M out
Leads 1 of 1 benchmarks
6 months newerFeb 2026
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GPT-5Qwen: Qwen3.5 397B A17B
12 / 46
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
Qwen: Qwen3.5 397B A17B
To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can use the standard computational scaling laws for Transformer models. The calculation relies on three key variables: the number of parameters in the model, the number of tokens in the training dataset, and the computational cost per parameter per token.
Here is the step-by-step reasoning and calculation.
Step 1: Identify the Number of Parameters ($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).
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AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
GPT-5
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Qwen: Qwen3.5 397B A17B
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Logic Puzzlereasoning
Tests an AI's ability to solve a simple but potentially confusing logic puzzle
GPT-5
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Qwen: Qwen3.5 397B A17B
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4 / 46
Stochastic Consistency Testanalysis
Tests an AI's randomness and creativity
GPT-5
- I told my suitcases we’re not going on vacation this year. Now I’m dealing with emotional baggage.
- Parallel lines have so much in common. It’s a shame they’ll never meet.
- Why do programmers prefer dark mode? Because light attracts bugs.
- I ordered a chicken and an egg online. I’ll let you know which comes first.
- I asked the librarian if the library had any books on paranoia. She whispered, “They’re right behind you.”
Unique jokes: 5
Qwen: Qwen3.5 397B A17B
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5 / 46
SVG Layout Challengeimage generation
Tests an AI's ability to generate vector graphics
GPT-5
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Qwen: Qwen3.5 397B A17B
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Xbox Controller SVG Artimage generation
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
GPT-5
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Qwen: Qwen3.5 397B A17B
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7 / 46
Generate a Stand-Up Routineconversation
Tests an AI's humor and creative writing ability
GPT-5
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Qwen: Qwen3.5 397B A17B
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8 / 46
Realistic AI Interviewconversation
Tests an AI's ability to simulate personalities and predict future trends
GPT-5
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Qwen: Qwen3.5 397B A17B
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9 / 46
Satirical Fake News Headlineconversation
Tests an AI's humor and understanding of current events
GPT-5
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Qwen: Qwen3.5 397B A17B
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10 / 46
Character Voice Testconversation
Tests an AI's ability to write in distinct character voices
GPT-5
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Qwen: Qwen3.5 397B A17B
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11 / 46
Minimalist Landing Pageweb design
Tests an AI's ability to generate a complete, working landing page
GPT-5
Qwen: Qwen3.5 397B A17B
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12 / 46
Pokémon Battle UI Recreationweb design
Recreate an interactive, nostalgic Pokémon battle UI in a single HTML file.
GPT-5
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Qwen: Qwen3.5 397B A17B
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