GPT-5.2 Chat
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GPT-5.2 Chat vs Mistral Large 3 2512
Compare GPT-5.2 Chat by OpenAI against Mistral Large 3 2512 by Mistral AI, tested across 53 shared challenges. Updated February 2026.
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GPT-5.2 ChatvsMistral Large 3 2512
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Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
Mistral Large 3 2512
Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves breaking down the training process into its fundamental components: forward pass, backward pass, and parameter updates. Here's a step-by-step breakdown:
1. Key Parameters of GPT-3
GPT-3 has the following key specifications (from the paper or known details):
- Model size (parameters): ~175 billion (1.75 × 10¹¹) parameters.
- Training tokens: ~300 billion tokens (3 × 10¹¹).
- Batch size: ~3.2 million tokens per batch (3.2 × 10⁶).
- Sequence length: ~2048 tokens.
- Number of layers: 96 (for the 175B model).
- Hidden dimension: ~12,288 (1.2288 × 10⁴).
- Number of attention heads: 96.
- Optimizer: Adam (or variant), which requires storing additional state (e.g., momentum and variance).
2. FLOPs per Forward Pass
The forward pass of a transformer model involves:
- Embedding lookup: Negligible compared to other operations.
- Self-attention: For each layer, the self-attention mechanism computes:
- Query, Key, Value projections: 3 × (sequence_length × hidden_dim × hidden_dim) = 3 × (2048 × 12288 × 12288) FLOPs.
- Attention scores: sequence_length × sequence_length × hidden_dim = 2048 × 2048 × 12288 FLOPs.
- Softmax and weighted sum: ~sequence_length × sequence_length × hidden_dim FLOPs.
- Output projection: sequence_length × hidden_dim × hidden_dim = 2048 × 12288 × 12288 FLOPs.
- Feed-forward network (FFN): For each layer, the FFN has two linear layers with an expansion factor of 4:
- First layer: sequence_length × hidden_dim × (4 × hidden_dim) = 2048 × 12288 × 49152 FLOPs.
- Second layer: sequence_length × (4 × hidden_dim) × hidden_dim = 2048 × 49152 × 12288 FLOPs.
- Layer normalization and residual connections: Negligible.
Simplified FLOPs per Layer:
For one layer, the dominant terms are:
- Self-attention: ~6 × (sequence_length × hidden_dim²) = 6 × (2048 × 12288²) ≈ 1.8 × 10¹² FLOPs.
- FFN: ~8 × (sequence_length × hidden_dim²) = 8 × (2048 × 12288²) ≈ 2.4 × 10¹² FLOPs.
- Total per layer: ~4.2 × 10¹² FLOPs.
For 96 layers: 96 × 4.2 × 10¹² ≈ 4.0 × 10¹⁴ FLOPs per forward pass.
FLOPs per Token:
Since the sequence length is 2048, the FLOPs per token is: 4.0 × 10¹⁴ / 2048 ≈ 2.0 × 10¹¹ FLOPs per token.
3. FLOPs per Backward Pass
The backward pass requires roughly 2× the FLOPs of the forward pass because:
- Gradients must be computed for all operations (same as forward).
- Additional operations for weight updates (e.g., gradient accumulation).
Thus, backward pass FLOPs ≈ 2 × forward pass FLOPs ≈ 8.0 × 10¹⁴ FLOPs per batch.
4. Total FLOPs per Batch
- Forward pass: 4.0 × 10¹⁴ FLOPs.
- Backward pass: 8.0 × 10¹⁴ FLOPs.
- Total per batch: 1.2 × 10¹⁵ FLOPs.
5. Total Training FLOPs
- Number of batches: Total tokens / tokens per batch = 3 × 10¹¹ / 3.2 × 10⁶ ≈ 9.4 × 10⁴ batches.
- Total FLOPs: 9.4 × 10⁴ × 1.2 × 10¹⁵ ≈ 1.1 × 10²⁰ FLOPs.
However, this is an underestimate because:
- Optimizer overhead: Adam requires storing and updating momentum and variance, which adds ~2× more FLOPs (since each parameter update involves 2 additional operations).
- Total FLOPs with Adam: ~3 × forward + backward FLOPs ≈ 3 × 1.2 × 10¹⁵ = 3.6 × 10¹⁵ per batch.
- Total training FLOPs: 9.4 × 10⁴ × 3.6 × 10¹⁵ ≈ 3.4 × 10²⁰ FLOPs.
- Mixed precision training: Often uses FP16/FP32, which may reduce FLOPs slightly, but we'll ignore this for simplicity.
6. Refining the Estimate
A more precise formula for transformer FLOPs (from Kaplan et al., 2020) is: [ \text{Total FLOPs} = 6 \times N \times D ] where:
- ( N ) = number of parameters (~1.75 × 10¹¹).
- ( D ) = number of training tokens (~3 × 10¹¹).
This gives: 6 × 1.75 × 10¹¹ × 3 × 10¹¹ ≈ 3.15 × 10²³ FLOPs.
This is much larger than our previous estimate because:
- The formula accounts for the fact that each parameter is updated for every token (not just per batch).
- It includes the full backward pass and optimizer overhead.
However, this seems too high. A better approximation is: [ \text{Total FLOPs} \approx 2 \times N \times D \times \text{seq_len} ] But this is also not precise.
Empirical Scaling Laws:
From Chinchilla scaling laws, the FLOPs for training a transformer can be approximated as: [ \text{FLOPs} \approx 6 \times N \times D ] For GPT-3: 6 × 1.75 × 10¹¹ × 3 × 10¹¹ ≈ 3.15 × 10²³ FLOPs.
But this is likely an overestimate. The actual number is closer to 3 × 10²³ FLOPs (as reported in the GPT-3 paper).
7. Final Estimate
Combining all the above, the most reasonable estimate is: [ \text{Total training FLOPs for GPT-3} \approx 3.14 \times 10^{23} \text{ FLOPs} ] This aligns with:
- The 6 × N × D rule of thumb.
- Reported estimates for GPT-3 (e.g., ~3.14 × 10²³ FLOPs in the paper).
Summary of Steps:
- Identify model parameters (N = 1.75 × 10¹¹).
- Identify training tokens (D = 3 × 10¹¹).
- Use the empirical scaling law: FLOPs ≈ 6 × N × D.
- Compute: 6 × 1.75 × 10¹¹ × 3 × 10¹¹ ≈ 3.15 × 10²³ FLOPs.
This is the most reliable estimate without looking up the exact number. The actual number may vary slightly due to implementation details (e.g., mixed precision, gradient checkpointing).
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2 / 48
AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
GPT-5.2 Chat
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Mistral Large 3 2512
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3 / 48
Logic Puzzlereasoning
Tests an AI's ability to solve a simple but potentially confusing logic puzzle
GPT-5.2 Chat
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Mistral Large 3 2512
Alright, let's tackle this problem step by step. The question is:
"Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?"
At first glance, it might seem a bit confusing, but let's break it down to understand it better.
Understanding the Problem
We have a family with several children, including Sally and her brothers and sisters. Here's what we know:
- Sally is a girl.
- Sally has 3 brothers.
- Each brother has 2 sisters.
We need to determine how many sisters Sally has.
Identifying the Siblings
First, let's list out the siblings based on the information given.
- Sally: She is one girl in the family.
- Brothers: Sally has 3 brothers. So, there are 3 boys in the family.
Now, the next piece of information is that "each brother has 2 sisters."
This means that if you pick any one of the 3 brothers, that brother has 2 sisters.
Determining the Sisters
Here's where it might get a bit tricky. We know Sally is one sister, but are there more?
Let's think about what "each brother has 2 sisters" implies.
If a brother has 2 sisters, those sisters must include Sally and possibly another girl.
But wait—Sally is already one sister. So, if a brother has 2 sisters, and one is Sally, who is the other one?
This suggests that there must be at least one more sister besides Sally to make the count of sisters equal to 2 for each brother.
Let's test this:
-
Suppose there is only Sally as the sister. Then each brother would have only 1 sister (Sally), but the problem states each has 2 sisters. So, this can't be the case.
-
Therefore, there must be another sister besides Sally.
So, the sisters are:
- Sally
- Another girl (let's call her, say, Emily)
Now, let's check if this satisfies the condition that each brother has 2 sisters.
Each brother's sisters would be:
- Sally
- Emily
That's 2 sisters, which matches the given information.
Verifying the Number of Sisters
Now, the question is asking: How many sisters does Sally have?
We've established that the sisters in the family are:
- Sally
- Emily
But the question is about Sally's sisters, not including herself.
So, Sally's sisters are:
- Emily
That's 1 sister.
Wait a minute, but earlier we thought there are 2 sisters in total (Sally and Emily), but Sally's sisters would be just Emily, making it 1 sister.
But let's double-check the initial interpretation to ensure we didn't make a mistake.
Re-evaluating the Interpretation
The confusion might arise from how we're counting the sisters.
Total sisters in the family:
- Sally
- Emily
Total brothers: 3
Now, "each brother has 2 sisters." Each brother's sisters are Sally and Emily, which is 2. This checks out.
Now, how many sisters does Sally have?
Sally's sisters are the other girls in the family besides herself, which is Emily. So, Sally has 1 sister.
But let me think if there could be more sisters.
Suppose there are 3 sisters: Sally, Emily, and another girl (let's say, Anna).
Then each brother would have 3 sisters (Sally, Emily, Anna), but the problem says each has 2 sisters. So, this can't be.
Therefore, the only possibility is that there are 2 sisters in total: Sally and Emily.
Thus, Sally has 1 sister.
Potential Misinterpretation
Initially, one might think that since each brother has 2 sisters, and there are 3 brothers, there are 6 sister instances (3 brothers × 2 sisters each), leading to more sisters. But this would be incorrect because all brothers share the same sisters; the sisters aren't unique to each brother.
For example, if there are 2 sisters, each of the 3 brothers has those 2 sisters, totaling 6 sister instances (3 brothers × 2 sisters), but the actual number of sisters is just 2.
Final Confirmation
Let's confirm with the numbers:
- Sisters: Sally and Emily (total 2 girls)
- Brothers: 3 boys
Each brother's sisters: Sally and Emily (2 sisters) — matches the given information.
Sally's sisters: Emily (1 sister)
Therefore, Sally has 1 sister.
Conclusion
After carefully analyzing the problem and considering the relationships, the correct answer is:
Sally has 1 sister.
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4 / 48
Math Misconception Testreasoning
Tests an AI's understanding of number representation
GPT-5.2 Chat
No content available for this output.
Mistral Large 3 2512
No, 9.11 is not greater than 9.9.
Here's the comparison:
- 9.9 is the same as 9.90.
- 9.11 is less than 9.90.
So, 9.11 < 9.9.
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5 / 48
Stochastic Consistency Testanalysis
Tests an AI's randomness and creativity
GPT-5.2 Chat
No content available for this output.
Mistral Large 3 2512
No content available for this output.
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6 / 48
SVG Layout Challengeimage generation
Tests an AI's ability to generate vector graphics
GPT-5.2 Chat
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Mistral Large 3 2512
No content available for this output.
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7 / 48
Xbox Controller SVG Artimage generation
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
GPT-5.2 Chat
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Mistral Large 3 2512
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8 / 48
Generate a Stand-Up Routineconversation
Tests an AI's humor and creative writing ability
GPT-5.2 Chat
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Mistral Large 3 2512
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9 / 48
Realistic AI Interviewconversation
Tests an AI's ability to simulate personalities and predict future trends
GPT-5.2 Chat
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Mistral Large 3 2512
No content available for this output.
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10 / 48
Satirical Fake News Headlineconversation
Tests an AI's humor and understanding of current events
GPT-5.2 Chat
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Mistral Large 3 2512
No content available for this output.
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11 / 48
Character Voice Testconversation
Tests an AI's ability to write in distinct character voices
GPT-5.2 Chat
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Mistral Large 3 2512
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12 / 48
Minimalist Landing Pageweb design
Tests an AI's ability to generate a complete, working landing page
GPT-5.2 Chat
Mistral Large 3 2512
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12 of 48