Compare · ModelsLive · 3 picked · head to head
Gemini 2.5 Pro vs Gemini 3 Pro vs GPT-5
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
Gemini 3 Pro wins on 28/41 benchmarks
Gemini 3 Pro wins 28 of 41 shared benchmarks. Leads in agentic · reasoning · knowledge.
Category leads
agentic·Gemini 3 Proreasoning·Gemini 3 Proknowledge·Gemini 3 Promath·GPT-5general·Gemini 3 Procoding·Gemini 3 Prolanguage·Gemini 3 Prospeed·Gemini 3 Proarena·Gemini 3 Pro
Hype vs Reality
Attention vs performance
Gemini 2.5 Pro
#116 by perf·no signal
Gemini 3 Pro
#71 by perf·#5 by attention
GPT-5
#100 by perf·#4 by attention
Best value
GPT-5
1.0x better value than Gemini 2.5 Pro
Gemini 2.5 Pro
9.0 pts/$
$5.63/M
Gemini 3 Pro
n/a
no price
GPT-5
9.4 pts/$
$5.63/M
Vendor risk
Who is behind the model
Google DeepMind
$4.20T·Tier 1
Google DeepMind
$4.20T·Tier 1
OpenAI
$840.0B·Tier 1
Head to head
41 benchmarks · 3 models
Gemini 2.5 ProGemini 3 ProGPT-5
APEX-Agents
Gemini 3 Pro leads by +0.1
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
Gemini 2.5 Pro
6.6
Gemini 3 Pro
18.4
GPT-5
18.3
ARC-AGI
Gemini 3 Pro leads by +9.3
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
Gemini 2.5 Pro
41.0
Gemini 3 Pro
75.0
GPT-5
65.7
ARC-AGI-2
Gemini 3 Pro leads by +21.3
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
Gemini 2.5 Pro
4.9
Gemini 3 Pro
31.1
GPT-5
9.9
Balrog
Gemini 3 Pro leads by +14.8
Balrog · benchmarks AI agents on text-based adventure games, testing language understanding, strategic planning, and long-horizon reasoning.
Gemini 2.5 Pro
43.3
Gemini 3 Pro
58.1
GPT-5
32.8
Chess Puzzles
GPT-5 leads by +6.3
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
Gemini 2.5 Pro
15.8
Gemini 3 Pro
27.4
GPT-5
33.7
DeepResearch Bench
GPT-5 leads by +3.3
DeepResearch Bench · evaluates AI on complex multi-step research tasks requiring information gathering, synthesis, and producing comprehensive analyses.
Gemini 2.5 Pro
42.8
Gemini 3 Pro
46.3
GPT-5
49.6
FrontierMath-2025-02-28-Private
Gemini 3 Pro leads by +33.5
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
Gemini 2.5 Pro
14.1
Gemini 3 Pro
66.0
GPT-5
32.4
FrontierMath-Tier-4-2025-07-01-Private
Gemini 3 Pro leads by +18.8
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
Gemini 2.5 Pro
4.2
Gemini 3 Pro
31.3
GPT-5
12.5
Gdpval
Gemini 3 Pro leads by +5.5
Gemini 2.5 Pro
23.3
Gemini 3 Pro
40.3
GPT-5
34.8
GeoBench
Gemini 3 Pro leads by +3.0
GeoBench · tests geographic knowledge and spatial reasoning across countries, landmarks, coordinates, and geopolitical understanding.
Gemini 2.5 Pro
81.0
Gemini 3 Pro
84.0
GPT-5
81.0
GPQA diamond
Gemini 3 Pro leads by +8.6
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
Gemini 2.5 Pro
80.4
Gemini 3 Pro
90.2
GPT-5
81.6
GSO-Bench
Gemini 3 Pro leads by +11.8
GSO-Bench · evaluates AI models on real-world open-source software engineering tasks, testing the ability to understand and resolve actual GitHub issues.
Gemini 2.5 Pro
3.9
Gemini 3 Pro
18.6
GPT-5
6.9
HELM · GPQA
Gemini 3 Pro leads by +1.2
Gemini 2.5 Pro
74.9
Gemini 3 Pro
80.3
GPT-5
79.1
HELM · IFEval
Gemini 3 Pro leads by +0.1
Gemini 2.5 Pro
84.0
Gemini 3 Pro
87.6
GPT-5
87.5
HELM · MMLU-Pro
Gemini 3 Pro leads by +4.0
Gemini 2.5 Pro
86.3
Gemini 3 Pro
90.3
GPT-5
86.3
HELM · Omni-MATH
GPT-5 leads by +9.1
Gemini 2.5 Pro
41.6
Gemini 3 Pro
55.6
GPT-5
64.7
HELM · WildBench
Gemini 3 Pro leads by +0.2
Gemini 2.5 Pro
85.7
Gemini 3 Pro
85.9
GPT-5
85.7
HLE
Gemini 3 Pro leads by +12.8
HLE (Humanity's Last Exam) · a reasoning benchmark designed to be the hardest public evaluation of AI. Questions span mathematics, physics, philosophy, and logic · curated to be at or beyond the frontier of human expert capability. Tested with and without tool augmentation. Claude Opus 4.7 scores 46.9% without tools and 54.7% with tools · making it one of the few benchmarks where the top score is below 60%.
Gemini 2.5 Pro
17.7
Gemini 3 Pro
34.4
GPT-5
21.6
Metr Time Horizons
Gemini 3 Pro leads by +1.4
Gemini 2.5 Pro
55.4
Gemini 3 Pro
71.0
GPT-5
69.6
OTIS Mock AIME 2024-2025
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
Gemini 2.5 Pro
84.7
Gemini 3 Pro
91.4
GPT-5
91.4
Remote Labor Index
GPT-5 leads by +0.4
Gemini 2.5 Pro
0.8
Gemini 3 Pro
1.3
GPT-5
1.7
SimpleBench
Gemini 3 Pro leads by +16.8
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
Gemini 2.5 Pro
54.9
Gemini 3 Pro
71.7
GPT-5
48.0
SimpleQA Verified
Gemini 3 Pro leads by +16.9
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
Gemini 2.5 Pro
56.0
Gemini 3 Pro
72.9
GPT-5
50.1
SWE-Bench verified
GPT-5 leads by +0.6
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.
Gemini 2.5 Pro
57.6
Gemini 3 Pro
72.9
GPT-5
73.5
Terminal Bench
Gemini 3 Pro leads by +19.8
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.
Gemini 2.5 Pro
32.6
Gemini 3 Pro
69.4
GPT-5
49.6
VPCT
Gemini 3 Pro leads by +37.5
VPCT (Visual Pattern Completion Test) · tests visual reasoning and pattern recognition by having models complete visual sequences and transformations.
Gemini 2.5 Pro
19.6
Gemini 3 Pro
86.5
GPT-5
49.0
WeirdML
Gemini 3 Pro leads by +9.2
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
Gemini 2.5 Pro
54.0
Gemini 3 Pro
69.9
GPT-5
60.7
Artificial Analysis · Agentic Index
Gemini 3 Pro leads by +12.4
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
Gemini 2.5 Pro
32.7
Gemini 3 Pro
45.0
Artificial Analysis · Coding Index
Gemini 3 Pro leads by +7.4
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
Gemini 2.5 Pro
31.9
Gemini 3 Pro
39.4
Artificial Analysis · Quality Index
Gemini 3 Pro leads by +14.3
Gemini 2.5 Pro
27.0
Gemini 3 Pro
41.3
Aider polyglot
GPT-5 leads by +4.9
Aider Polyglot · measures how well AI models can edit code across multiple programming languages using the Aider coding assistant framework.
Gemini 2.5 Pro
83.1
GPT-5
88.0
Chatbot Arena Elo · Coding
Gemini 3 Pro leads by +212.0
Gemini 2.5 Pro
1227.0
Gemini 3 Pro
1439.0
Chatbot Arena Elo · Overall
Gemini 3 Pro leads by +39.9
Gemini 2.5 Pro
1445.6
Gemini 3 Pro
1485.5
Dtbench
GPT-5 leads by +13.8
Gemini 2.5 Pro
70.7
GPT-5
84.5
Fiction.LiveBench
GPT-5 leads by +5.5
Fiction.LiveBench · a continuously updated benchmark using recently published fiction to test reading comprehension and reasoning, preventing data contamination.
Gemini 2.5 Pro
91.7
GPT-5
97.2
FrontierMath-Tier-4-v2-Private
GPT-5 leads by +21.9
Gemini 2.5 Pro
0.0
GPT-5
21.9
FrontierMath-Tiers-1-3-v2-Private
GPT-5 leads by +30.9
Gemini 2.5 Pro
24.6
GPT-5
55.4
Lech Mazur Writing
GPT-5 leads by +2.2
Lech Mazur Writing · evaluates creative writing ability, assessing prose quality, narrative coherence, and stylistic sophistication.
Gemini 2.5 Pro
83.8
GPT-5
86.0
Lmca
GPT-5 leads by +6.1
Gemini 2.5 Pro
40.9
GPT-5
47.0
MATH level 5
GPT-5 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.
Gemini 2.5 Pro
95.6
GPT-5
98.1
Proofbench
Gemini 3 Pro leads by +2.0
Gemini 3 Pro
20.0
GPT-5
18.0
Full benchmark table
| Benchmark | Gemini 2.5 Pro | Gemini 3 Pro | GPT-5 |
|---|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 6.6 | 18.4 | 18.3 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 41.0 | 75.0 | 65.7 |
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. | 4.9 | 31.1 | 9.9 |
Balrog Balrog · benchmarks AI agents on text-based adventure games, testing language understanding, strategic planning, and long-horizon reasoning. | 43.3 | 58.1 | 32.8 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 15.8 | 27.4 | 33.7 |
DeepResearch Bench DeepResearch Bench · evaluates AI on complex multi-step research tasks requiring information gathering, synthesis, and producing comprehensive analyses. | 42.8 | 46.3 | 49.6 |
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. | 14.1 | 66.0 | 32.4 |
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. | 4.2 | 31.3 | 12.5 |
Gdpval | 23.3 | 40.3 | 34.8 |
GeoBench GeoBench · tests geographic knowledge and spatial reasoning across countries, landmarks, coordinates, and geopolitical understanding. | 81.0 | 84.0 | 81.0 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 80.4 | 90.2 | 81.6 |
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. | 3.9 | 18.6 | 6.9 |
HELM · GPQA | 74.9 | 80.3 | 79.1 |
HELM · IFEval | 84.0 | 87.6 | 87.5 |
HELM · MMLU-Pro | 86.3 | 90.3 | 86.3 |
HELM · Omni-MATH | 41.6 | 55.6 | 64.7 |
HELM · WildBench | 85.7 | 85.9 | 85.7 |
HLE HLE (Humanity's Last Exam) · a reasoning benchmark designed to be the hardest public evaluation of AI. Questions span mathematics, physics, philosophy, and logic · curated to be at or beyond the frontier of human expert capability. Tested with and without tool augmentation. Claude Opus 4.7 scores 46.9% without tools and 54.7% with tools · making it one of the few benchmarks where the top score is below 60%. | 17.7 | 34.4 | 21.6 |
Metr Time Horizons | 55.4 | 71.0 | 69.6 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 84.7 | 91.4 | 91.4 |
Remote Labor Index | 0.8 | 1.3 | 1.7 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 54.9 | 71.7 | 48.0 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 56.0 | 72.9 | 50.1 |
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. | 57.6 | 72.9 | 73.5 |
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. | 32.6 | 69.4 | 49.6 |
VPCT VPCT (Visual Pattern Completion Test) · tests visual reasoning and pattern recognition by having models complete visual sequences and transformations. | 19.6 | 86.5 | 49.0 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 54.0 | 69.9 | 60.7 |
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 32.7 | 45.0 | — |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 31.9 | 39.4 | — |
Artificial Analysis · Quality Index | 27.0 | 41.3 | — |
Aider polyglot Aider Polyglot · measures how well AI models can edit code across multiple programming languages using the Aider coding assistant framework. | 83.1 | — | 88.0 |
Chatbot Arena Elo · Coding | 1227.0 | 1439.0 | — |
Chatbot Arena Elo · Overall | 1445.6 | 1485.5 | — |
Dtbench | 70.7 | — | 84.5 |
Fiction.LiveBench Fiction.LiveBench · a continuously updated benchmark using recently published fiction to test reading comprehension and reasoning, preventing data contamination. | 91.7 | — | 97.2 |
FrontierMath-Tier-4-v2-Private | 0.0 | — | 21.9 |
FrontierMath-Tiers-1-3-v2-Private | 24.6 | — | 55.4 |
Lech Mazur Writing Lech Mazur Writing · evaluates creative writing ability, assessing prose quality, narrative coherence, and stylistic sophistication. | 83.8 | — | 86.0 |
Lmca | 40.9 | — | 47.0 |
MATH level 5 MATH Level 5 · the hardest tier of the MATH benchmark, featuring competition-level problems from AMC, AIME, and Olympiad-style mathematics. | 95.6 | — | 98.1 |
Proofbench | — | 20.0 | 18.0 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.25 | $10.00 | 1.0M tokens (~524 books) | $34.38 | |
| — | — | — | — | |
| $1.25 | $10.00 | 400K tokens (~200 books) | $34.38 |