Mistral Devstral Small 1.1
No content available for this output.
Mistral Devstral Small 1.1 vs GPT-5
Compare Mistral Devstral Small 1.1 by Mistral AI against GPT-5 by OpenAI, tested across 43 shared challenges. Updated February 2026.
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Mistral Devstral Small 1.1vsGPT-5
43 challenges
Why Mistral Devstral Small 1.1?
29.5x cheaper overall$0.10/M in · $0.30/M out
Why GPT-5?
Leads 1 of 1 benchmarks
Stronger on SWE-bench Verified74.9% vs 46.8%
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Mistral Devstral Small 1.1GPT-5
12 / 43
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
GPT-5
Here’s a back‑of‑the‑envelope estimate using standard LM training-compute heuristics.
- Assumptions
- Model size (N): GPT-3 has about 175 billion parameters (N ≈ 1.75 × 10^11).
- Training tokens (T): GPT-3 was trained on roughly 300 billion tokens (T ≈ 3.0 × 10^11).
- FLOPs per token per parameter: A common rule of thumb for transformer LM training is FLOPs ≈ 6 × N × T The factor ~6 comes from counting forward and backward passes for the parameter-heavy matmuls (roughly 2N for forward + 4N for backward). It ignores small terms from layernorms, softmax, embeddings, and optimizer updates (which we’ll account for with a small overhead factor later).
- Core calculation
- 6 × N × T = 6 × (1.75 × 10^11) × (3.0 × 10^11)
- First multiply N × T: 1.75 × 3.0 = 5.25; powers: 10^11 × 10^11 = 10^22 So N × T = 5.25 × 10^22
- Now multiply by 6: 6 × 5.25 × 10^22 = 31.5 × 10^22 = 3.15 × 10^23 FLOPs
- Overheads and uncertainty
- Non-matmul ops (attention softmax, masking, residuals, layernorms), embeddings, and optimizer update steps add some overhead. A 10–20% uplift is a reasonable allowance.
- Applying +10–20%: 3.15 × 10^23 × (1.1 to 1.2) ≈ (3.5 ± 0.3) × 10^23 FLOPs.
Answer
- Point estimate: ~3.15 × 10^23 FLOPs
- With a modest overhead allowance: roughly (3.3–3.8) × 10^23 FLOPs
This is a coarse estimate; the exact number depends on details like sequence length, activation checkpointing, implementation efficiency, and the precise token count.
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2 / 43
AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
Mistral Devstral Small 1.1
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GPT-5
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3 / 43
Logic Puzzlereasoning
Tests an AI's ability to solve a simple but potentially confusing logic puzzle
Mistral Devstral Small 1.1
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GPT-5
1
Reason: If each of the 3 brothers has 2 sisters, there are exactly 2 girls in the family. One is Sally, so she has 1 sister.
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4 / 43
Math Misconception Testreasoning
Tests an AI's understanding of number representation
Mistral Devstral Small 1.1
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GPT-5
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5 / 43
Stochastic Consistency Testanalysis
Tests an AI's randomness and creativity
Mistral Devstral Small 1.1
Sure, here are five jokes for you:
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Why don't scientists trust atoms? Because they make up everything!
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What do you call fake spaghetti? An impasta.
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How does the moon cut his hair? Eclipse it.
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What do you call a can opener that doesn't work? A can't opener.
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How many tickles does it take to make an octopus laugh? Ten-tickles.
All five jokes are unique.
GPT-5
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6 / 43
SVG Layout Challengeimage generation
Tests an AI's ability to generate vector graphics
Mistral Devstral Small 1.1
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GPT-5
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7 / 43
Xbox Controller SVG Artimage generation
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
Mistral Devstral Small 1.1
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GPT-5
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8 / 43
Realistic AI Interviewconversation
Tests an AI's ability to simulate personalities and predict future trends
Mistral Devstral Small 1.1
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GPT-5
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9 / 43
Satirical Fake News Headlineconversation
Tests an AI's humor and understanding of current events
Mistral Devstral Small 1.1
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GPT-5
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10 / 43
Character Voice Testconversation
Tests an AI's ability to write in distinct character voices
Mistral Devstral Small 1.1
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GPT-5
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11 / 43
Minimalist Landing Pageweb design
Tests an AI's ability to generate a complete, working landing page
Mistral Devstral Small 1.1
GPT-5
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12 / 43
Pokémon Battle UI Recreationweb design
Recreate an interactive, nostalgic Pokémon battle UI in a single HTML file.
Mistral Devstral Small 1.1
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GPT-5
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12 of 43