GPT-4o mini
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GPT-4o mini vs GPT-5 Codex
Compare GPT-4o mini and GPT-5 Codex, both from OpenAI, tested across 38 shared challenges. Updated February 2026.
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GPT-4o minivsGPT-5 Codex
38 challenges
Why GPT-4o mini?
No standout advantages
Why GPT-5 Codex?
14 months newerSep 2025
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GPT-4o miniGPT-5 Codex
12 / 38
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
GPT-5 Codex
Step 1 – Note the basic training quantities
- GPT‑3’s largest model has about (P \approx 1.75 \times 10^{11}) trainable parameters (175 billion).
- Public descriptions indicate it was trained on roughly (T \approx 3.0 \times 10^{11}) tokens (≈300 billion).
Step 2 – Relate parameters, tokens, and FLOPs
For dense transformers, almost all computation comes from matrix multiplies.
A practical rule (derived by counting multiply–adds in the attention and MLP blocks, and accounting for the backward pass) is:
[ \text{Training FLOPs} \approx 6 \times (\text{number of parameters}) \times (\text{number of tokens}) ]
Here’s the intuition:
- Forward pass: Each parameter contributes one multiply and one add → about (2) FLOPs per parameter per token.
- Backward pass: Computing gradients with respect to activations and weights requires roughly twice the forward cost (two more matrix multiplies of comparable size).
So total ≈ (3) times the forward cost → (3 \times 2 = 6) FLOPs per parameter per token.
(The softmax, layernorm, activation costs are much smaller and get absorbed into this constant.)
Step 3 – Plug in the GPT‑3 values
[ \text{FLOPs} \approx 6 \times (1.75\times 10^{11}) \times (3.0 \times 10^{11}) ]
[ = 6 \times 5.25 \times 10^{22} = 31.5 \times 10^{22} \approx 3.15 \times 10^{23} \text{ FLOPs} ]
Step 4 – Interpret
So, training GPT‑3 to completion requires on the order of (3 \times 10^{23}) floating-point operations.
That estimate aligns with detailed breakdowns of transformers and is accurate to within tens of percent, which is sufficient for order-of-magnitude comparisons.
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AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
GPT-4o mini
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GPT-5 Codex
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3 / 38
Logic Puzzlereasoning
Tests an AI's ability to solve a simple but potentially confusing logic puzzle
GPT-4o mini
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GPT-5 Codex
Sally has 1 sister.
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4 / 38
Math Misconception Testreasoning
Tests an AI's understanding of number representation
GPT-4o mini
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GPT-5 Codex
No. Since 9.11 is equal to 9.110 and 9.9 is equal to 9.900, we see that 0.110 < 0.900, so 9.11 is less than 9.9.
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5 / 38
Stochastic Consistency Testanalysis
Tests an AI's randomness and creativity
GPT-4o mini
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GPT-5 Codex
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6 / 38
SVG Layout Challengeimage generation
Tests an AI's ability to generate vector graphics
GPT-4o mini
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GPT-5 Codex
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7 / 38
Xbox Controller SVG Artimage generation
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
GPT-4o mini
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GPT-5 Codex
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8 / 38
Generate a Stand-Up Routineconversation
Tests an AI's humor and creative writing ability
GPT-4o mini
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GPT-5 Codex
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9 / 38
Realistic AI Interviewconversation
Tests an AI's ability to simulate personalities and predict future trends
GPT-4o mini
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GPT-5 Codex
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10 / 38
Satirical Fake News Headlineconversation
Tests an AI's humor and understanding of current events
GPT-4o mini
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GPT-5 Codex
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11 / 38
Character Voice Testconversation
Tests an AI's ability to write in distinct character voices
GPT-4o mini
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GPT-5 Codex
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12 / 38
Minimalist Landing Pageweb design
Tests an AI's ability to generate a complete, working landing page
GPT-4o mini
GPT-5 Codex
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