DeepSeek V3.1
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DeepSeek V3.1 vs Gemini 3 Pro Preview
Compare DeepSeek V3.1 by DeepSeek against Gemini 3 Pro Preview by Google AI, tested across 49 shared challenges. Updated February 2026.
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DeepSeek V3.1vsGemini 3 Pro Preview
44 challenges
Why DeepSeek V3.1?
No standout advantages
Why Gemini 3 Pro Preview?
Leads 1 of 1 benchmarks
Stronger on SWE-bench Verified76.2% vs 66.0%
3 months newerNov 2025
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DeepSeek V3.1Gemini 3 Pro Preview
12 / 44
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
Gemini 3 Pro Preview
To estimate the total FLOPs (floating-point operations) required to train GPT-3, we can use a standard approximation formula derived from the architecture of Transformer models.
Here is the step-by-step breakdown of the estimation:
Step 1: Identify the Key Variables
To calculate training compute, we need two primary numbers: the number of parameters in the model ($P$) and the number of tokens the model was trained on ($D$).
- Model Size ($P$): GPT-3 is famously known to have 175 billion parameters. $$P = 175 \times 10^9$$
- Dataset Size ($D$): According to the original GPT-3 paper ("Language Models are Few-Shot Learners"), the model was trained on a weighted mix of datasets (Common Crawl, WebText2, Books1, Books2, Wikipedia). The total number of tokens processed during training was approximately 300 billion tokens. $$D = 300 \times 10^9$$
Step 2: Apply the Compute Approximation Formula
For dense Transformer models, a widely accepted heuristic (often cited in papers by Kaplan et al. and DeepMind regarding scaling laws) is that training requires approximately 6 FLOPs per parameter per training token.
- Forward Pass: Requires $\approx 2P$ FLOPs per token (matrix multiplication involves 1 multiply and 1 add, and we do this for the weights).
- Backward Pass (Gradient Calculation): Requires $\approx 4P$ FLOPs per token (roughly twice the cost of the forward pass).
- Total: $2P + 4P = 6P$ FLOPs per token.
Therefore, the formula for Total Training Compute ($C$) is: $$C \approx 6 \times P \times D$$
Step 3: Perform the Calculation
Now, substitute the values into the formula:
$$C \approx 6 \times (175 \times 10^9) \times (300 \times 10^9)$$
First, group the integers and the powers of 10: $$C \approx (6 \times 175 \times 300) \times (10^9 \times 10^9)$$
Calculate the integers:
- $6 \times 175 = 1,050$
- $1,050 \times 300 = 315,000$
Combine with the exponent ($10^{18}$): $$C \approx 315,000 \times 10^{18} \text{ FLOPs}$$
Step 4: Convert to Scientific Notation
To make the number readable, we normalize it:
$$315,000 = 3.15 \times 10^5$$
Combine exponents: $$3.15 \times 10^5 \times 10^{18} = 3.15 \times 10^{23}$$
Final Estimate
The estimated total compute required to train GPT-3 is approximately:
$$3.14 \times 10^{23} \text{ FLOPs}$$
(Note: In the AI literature, this is often converted to "Petaflop-days." One Petaflop-day is approximately $8.64 \times 10^{19}$ FLOPs. Dividing $3.14 \times 10^{23}$ by that number yields roughly 3,640 Petaflop-days, which aligns closely with OpenAI's published figures.)
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2 / 44
AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
DeepSeek V3.1
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Gemini 3 Pro Preview
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3 / 44
Logic Puzzlereasoning
Tests an AI's ability to solve a simple but potentially confusing logic puzzle
DeepSeek V3.1
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Gemini 3 Pro Preview
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4 / 44
Math Misconception Testreasoning
Tests an AI's understanding of number representation
DeepSeek V3.1
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Gemini 3 Pro Preview
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5 / 44
Stochastic Consistency Testanalysis
Tests an AI's randomness and creativity
DeepSeek V3.1
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Gemini 3 Pro Preview
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6 / 44
SVG Layout Challengeimage generation
Tests an AI's ability to generate vector graphics
DeepSeek V3.1
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Gemini 3 Pro Preview
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7 / 44
Xbox Controller SVG Artimage generation
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
DeepSeek V3.1
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Gemini 3 Pro Preview
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8 / 44
Generate a Stand-Up Routineconversation
Tests an AI's humor and creative writing ability
DeepSeek V3.1
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Gemini 3 Pro Preview
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9 / 44
Realistic AI Interviewconversation
Tests an AI's ability to simulate personalities and predict future trends
DeepSeek V3.1
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Gemini 3 Pro Preview
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10 / 44
Satirical Fake News Headlineconversation
Tests an AI's humor and understanding of current events
DeepSeek V3.1
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Gemini 3 Pro Preview
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11 / 44
Minimalist Landing Pageweb design
Tests an AI's ability to generate a complete, working landing page
DeepSeek V3.1
Gemini 3 Pro Preview
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12 / 44
Pokémon Battle UI Recreationweb design
Recreate an interactive, nostalgic Pokémon battle UI in a single HTML file.
DeepSeek V3.1
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Gemini 3 Pro Preview
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