Compare · ModelsLive · 2 picked · head to head
GPT-5 Nano vs o4 Mini
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
o4 Mini wins on 16/17 benchmarks
o4 Mini wins 16 of 17 shared benchmarks. Leads in reasoning · knowledge · math.
Category leads
reasoning·o4 Miniknowledge·o4 Minimath·o4 Minilanguage·GPT-5 Nanocoding·o4 Mini
Hype vs Reality
Attention vs performance
GPT-5 Nano
#112 by perf·no signal
o4 Mini
#79 by perf·#13 by attention
Best value
GPT-5 Nano
10.4x better value than o4 Mini
GPT-5 Nano
201.3 pts/$
$0.23/M
o4 Mini
19.3 pts/$
$2.75/M
Vendor risk
Who is behind the model
OpenAI
$840.0B·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
17 benchmarks · 2 models
GPT-5 Nanoo4 Mini
ARC-AGI
o4 Mini leads by +38.0
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
GPT-5 Nano
20.7
o4 Mini
58.7
ARC-AGI-2
o4 Mini leads by +3.5
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
GPT-5 Nano
2.6
o4 Mini
6.1
Fiction.LiveBench
o4 Mini leads by +33.4
Fiction.LiveBench · a continuously updated benchmark using recently published fiction to test reading comprehension and reasoning, preventing data contamination.
GPT-5 Nano
44.4
o4 Mini
77.8
FrontierMath-2025-02-28-Private
o4 Mini leads by +16.5
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
GPT-5 Nano
8.3
o4 Mini
24.8
FrontierMath-Tier-4-2025-07-01-Private
o4 Mini leads by +4.2
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
GPT-5 Nano
2.1
o4 Mini
6.3
GPQA diamond
o4 Mini leads by +13.6
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
GPT-5 Nano
59.3
o4 Mini
72.8
HELM · GPQA
o4 Mini leads by +5.6
GPT-5 Nano
67.9
o4 Mini
73.5
HELM · IFEval
GPT-5 Nano leads by +0.3
GPT-5 Nano
93.2
o4 Mini
92.9
HELM · MMLU-Pro
o4 Mini leads by +4.2
GPT-5 Nano
77.8
o4 Mini
82.0
HELM · Omni-MATH
o4 Mini leads by +17.3
GPT-5 Nano
54.7
o4 Mini
72.0
HELM · WildBench
o4 Mini leads by +4.8
GPT-5 Nano
80.6
o4 Mini
85.4
MATH level 5
o4 Mini leads by +2.6
MATH Level 5 · the hardest tier of the MATH benchmark, featuring competition-level problems from AMC, AIME, and Olympiad-style mathematics.
GPT-5 Nano
95.2
o4 Mini
97.8
OTIS Mock AIME 2024-2025
o4 Mini leads by +0.6
OTIS Mock AIME 2024–2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
GPT-5 Nano
81.1
o4 Mini
81.7
SimpleQA Verified
o4 Mini leads by +11.7
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
GPT-5 Nano
12.2
o4 Mini
23.9
SWE-Bench Verified (Bash Only)
o4 Mini leads by +10.2
SWE-Bench Verified (Bash Only) · a curated subset of SWE-bench where models fix real Python repository bugs using only bash commands, no agent frameworks.
GPT-5 Nano
34.8
o4 Mini
45.0
VPCT
o4 Mini leads by +30.4
VPCT (Visual Pattern Completion Test) · tests visual reasoning and pattern recognition by having models complete visual sequences and transformations.
GPT-5 Nano
5.8
o4 Mini
36.3
WeirdML
o4 Mini leads by +14.5
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
GPT-5 Nano
38.1
o4 Mini
52.6
Full benchmark table
| Benchmark | GPT-5 Nano | o4 Mini |
|---|---|---|
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 20.7 | 58.7 |
ARC-AGI-2 ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data. | 2.6 | 6.1 |
Fiction.LiveBench Fiction.LiveBench · a continuously updated benchmark using recently published fiction to test reading comprehension and reasoning, preventing data contamination. | 44.4 | 77.8 |
FrontierMath-2025-02-28-Private FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning. | 8.3 | 24.8 |
FrontierMath-Tier-4-2025-07-01-Private FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning. | 2.1 | 6.3 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 59.3 | 72.8 |
HELM · GPQA | 67.9 | 73.5 |
HELM · IFEval | 93.2 | 92.9 |
HELM · MMLU-Pro | 77.8 | 82.0 |
HELM · Omni-MATH | 54.7 | 72.0 |
HELM · WildBench | 80.6 | 85.4 |
MATH level 5 MATH Level 5 · the hardest tier of the MATH benchmark, featuring competition-level problems from AMC, AIME, and Olympiad-style mathematics. | 95.2 | 97.8 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024–2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 81.1 | 81.7 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 12.2 | 23.9 |
SWE-Bench Verified (Bash Only) SWE-Bench Verified (Bash Only) · a curated subset of SWE-bench where models fix real Python repository bugs using only bash commands, no agent frameworks. | 34.8 | 45.0 |
VPCT VPCT (Visual Pattern Completion Test) · tests visual reasoning and pattern recognition by having models complete visual sequences and transformations. | 5.8 | 36.3 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 38.1 | 52.6 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| GPT-5 Nano | $0.05 | $0.40 | 400K tokens (~200 books) | $1.38 |
| o4 Mini | $1.10 | $4.40 | 200K tokens (~100 books) | $19.25 |
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