Compare · ModelsLive · 2 picked · head to head
GPT-5.2 vs Gemini 3 Flash Preview
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GPT-5.2 wins on 12/17 benchmarks
GPT-5.2 wins 12 of 17 shared benchmarks. Leads in agentic · reasoning · knowledge.
Category leads
agentic·GPT-5.2reasoning·GPT-5.2arena·Gemini 3 Flash Previewknowledge·GPT-5.2math·GPT-5.2coding·GPT-5.2
Hype vs Reality
Attention vs performance
GPT-5.2
#76 by perf·no signal
Gemini 3 Flash Preview
#98 by perf·no signal
Best value
Gemini 3 Flash Preview
4.1x better value than GPT-5.2
GPT-5.2
6.9 pts/$
$7.88/M
Gemini 3 Flash Preview
28.1 pts/$
$1.75/M
Vendor risk
Who is behind the model
OpenAI
$840.0B·Tier 1
Google DeepMind
$4.00T·Tier 1
Head to head
17 benchmarks · 2 models
GPT-5.2Gemini 3 Flash Preview
APEX-Agents
GPT-5.2 leads by +10.3
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
GPT-5.2
34.3
Gemini 3 Flash Preview
24.0
ARC-AGI
GPT-5.2 leads by +64.7
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
GPT-5.2
86.2
Gemini 3 Flash Preview
21.5
ARC-AGI-2
GPT-5.2 leads by +19.3
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
GPT-5.2
52.9
Gemini 3 Flash Preview
33.6
Chatbot Arena Elo · Coding
Gemini 3 Flash Preview leads by +33.3
GPT-5.2
1403.1
Gemini 3 Flash Preview
1436.4
Chatbot Arena Elo · Overall
Gemini 3 Flash Preview leads by +34.4
GPT-5.2
1439.5
Gemini 3 Flash Preview
1473.9
Chess Puzzles
GPT-5.2 leads by +11.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
GPT-5.2
49.0
Gemini 3 Flash Preview
38.0
FrontierMath-2025-02-28-Private
GPT-5.2 leads by +5.1
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
GPT-5.2
40.7
Gemini 3 Flash Preview
35.6
FrontierMath-Tier-4-2025-07-01-Private
GPT-5.2 leads by +14.6
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
GPT-5.2
18.8
Gemini 3 Flash Preview
4.2
GPQA diamond
GPT-5.2 leads by +10.9
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
GPT-5.2
88.5
Gemini 3 Flash Preview
77.6
GSO-Bench
GPT-5.2 leads by +17.6
GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues.
GPT-5.2
27.4
Gemini 3 Flash Preview
9.8
OTIS Mock AIME 2024-2025
GPT-5.2 leads by +3.3
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
GPT-5.2
96.1
Gemini 3 Flash Preview
92.8
SimpleBench
Gemini 3 Flash Preview leads by +18.4
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
GPT-5.2
35.0
Gemini 3 Flash Preview
53.3
SimpleQA Verified
Gemini 3 Flash Preview leads by +28.5
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
GPT-5.2
38.9
Gemini 3 Flash Preview
67.4
SWE-Bench verified
Gemini 3 Flash Preview leads by +1.6
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability.
GPT-5.2
73.8
Gemini 3 Flash Preview
75.4
Terminal Bench
GPT-5.2 leads by +0.6
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
GPT-5.2
64.9
Gemini 3 Flash Preview
64.3
VPCT
GPT-5.2 leads by +17.1
VPCT (Visual Pattern Completion Test) · tests visual reasoning and pattern recognition by having models complete visual sequences and transformations.
GPT-5.2
76.0
Gemini 3 Flash Preview
58.9
WeirdML
GPT-5.2 leads by +10.6
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
GPT-5.2
72.2
Gemini 3 Flash Preview
61.6
Full benchmark table
| Benchmark | GPT-5.2 | Gemini 3 Flash Preview |
|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 34.3 | 24.0 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 86.2 | 21.5 |
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. | 52.9 | 33.6 |
Chatbot Arena Elo · Coding | 1403.1 | 1436.4 |
Chatbot Arena Elo · Overall | 1439.5 | 1473.9 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 49.0 | 38.0 |
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. | 40.7 | 35.6 |
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. | 18.8 | 4.2 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 88.5 | 77.6 |
GSO-Bench GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues. | 27.4 | 9.8 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 96.1 | 92.8 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 35.0 | 53.3 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 38.9 | 67.4 |
SWE-Bench verified SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability. | 73.8 | 75.4 |
Terminal Bench Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence. | 64.9 | 64.3 |
VPCT VPCT (Visual Pattern Completion Test) · tests visual reasoning and pattern recognition by having models complete visual sequences and transformations. | 76.0 | 58.9 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 72.2 | 61.6 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.75 | $14.00 | 400K tokens (~200 books) | $48.13 | |
| $0.50 | $3.00 | 1.0M tokens (~524 books) | $11.25 |
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