Qwen3 Next 80B A3B Instruct
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Qwen3 Next 80B A3B Instruct vs OpenAI o3
Compare Qwen3 Next 80B A3B Instruct by Qwen against OpenAI o3 by OpenAI, tested across 46 shared challenges. Updated February 2026.
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Qwen3 Next 80B A3B InstructvsOpenAI o3
41 challenges
Why Qwen3 Next 80B A3B Instruct?
29.2x cheaper overall$0.15/M in · $1.50/M out
5 months newerSep 2025
Why OpenAI o3?
No standout advantages
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Qwen3 Next 80B A3B InstructOpenAI o3
12 / 41
Estimate Complexityreasoning
Tests an AI's ability to make educated estimates based on technical knowledge
OpenAI o3
Below is a simple "back-of-the-envelope" estimate that uses only a few public facts about GPT-3 and very generic rules of thumb for counting operations.
Step 1. Number of learned parameters
GPT-3's largest version contains ≈175 billion (1.75 × 10¹¹) trainable parameters.
Step 2. How much data was seen during training
OpenAI reports that the model was trained on about 300 billion tokens (3 × 10¹¹).
Step 3. How many floating-point operations does one parameter cost per token?
A practical rule of thumb for Transformer language models is
Training FLOPs ≈ 6 × (# parameters) × (# training tokens)
Where does the factor 6 come from?
• Forward pass: every parameter is involved in one multiply-accumulate → 2 FLOPs
• Back-prop gradient computation: roughly another 2 FLOPs
• Parameter-update (optimizer) and miscellaneous overhead: ≈2 FLOPs
Total ≈6 FLOPs per parameter per token.
Step 4. Plug in the numbers
Total FLOPs = 6 × (1.75 × 10¹¹) × (3 × 10¹¹)
= 6 × 5.25 × 10²²
≈ 3.15 × 10²³ FLOPs.
Step 5. Express the answer in more familiar units
3.15 × 10²³ ≈ 315 zettaFLOPs (1 ZFLOP = 10²¹ FLOPs).
Step 6. Sanity check with hardware-time intuition
An NVIDIA A100 delivers ≈ 312 teraFLOP/s (0.312 PFLOP/s) in mixed precision.
Wall-clock time on a single A100:
3.15 × 10²³ / 3.12 × 10¹⁴ ≈ 1.0 × 10⁹ s ≈ 32 years.
Running on 1,000 A100s in parallel would take ≈12 days, which is the right ballpark for a large industrial training run.
Final estimate
Training GPT-3 (175 B parameters, 300 B tokens) requires on the order of 3 × 10²³ floating-point operations, i.e. roughly 300 zettaFLOPs.
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AI Board Game Logicreasoning
Tests an AI's ability to understand game rules and strategy
Qwen3 Next 80B A3B Instruct
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OpenAI o3
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3 / 41
Logic Puzzlereasoning
Tests an AI's ability to solve a simple but potentially confusing logic puzzle
Qwen3 Next 80B A3B Instruct
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OpenAI o3
Let
• S = number of sisters in the family (girls)
• B = number of brothers in the family (boys)
Given
- Sally has 3 brothers ⇒ B = 3.
- Each brother has 2 sisters ⇒ every boy sees exactly 2 girls in the sibling group.
Therefore S = 2.
Since Sally herself is one of those 2 sisters, the number of sisters Sally has (other than herself) is
2 – 1 = 1.
Sally has 1 sister.
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Math Misconception Testreasoning
Tests an AI's understanding of number representation
Qwen3 Next 80B A3B Instruct
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OpenAI o3
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5 / 41
Stochastic Consistency Testanalysis
Tests an AI's randomness and creativity
Qwen3 Next 80B A3B Instruct
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OpenAI o3
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6 / 41
SVG Layout Challengeimage generation
Tests an AI's ability to generate vector graphics
Qwen3 Next 80B A3B Instruct
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OpenAI o3
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7 / 41
Xbox Controller SVG Artimage generation
Tests an AI's ability to create detailed SVG illustrations of gaming hardware
Qwen3 Next 80B A3B Instruct
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OpenAI o3
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8 / 41
Generate a Stand-Up Routineconversation
Tests an AI's humor and creative writing ability
Qwen3 Next 80B A3B Instruct
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OpenAI o3
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9 / 41
Realistic AI Interviewconversation
Tests an AI's ability to simulate personalities and predict future trends
Qwen3 Next 80B A3B Instruct
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OpenAI o3
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10 / 41
Satirical Fake News Headlineconversation
Tests an AI's humor and understanding of current events
Qwen3 Next 80B A3B Instruct
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OpenAI o3
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11 / 41
Character Voice Testconversation
Tests an AI's ability to write in distinct character voices
Qwen3 Next 80B A3B Instruct
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OpenAI o3
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12 / 41
Minimalist Landing Pageweb design
Tests an AI's ability to generate a complete, working landing page
Qwen3 Next 80B A3B Instruct
OpenAI o3
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